DL_task.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DL-task.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "buqRKQxNQ4rJ",
        "colab_type": "code",
        "outputId": "21ace80d-3517-4a85-b936-7be8e959295a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "import numpy as np\n",
        "import os\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "from matplotlib import pyplot as plt\n",
        "import random\n",
        "%tensorflow_version 1.x\n",
        "import keras\n",
        "from keras.models import Sequential"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7kLm-VNMEQ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOl9orSuSEPT",
        "colab_type": "code",
        "outputId": "d333b1eb-7812-4bf1-b384-09caadabc15d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "! unzip DRIMDB.zip"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  DRIMDB.zip\n",
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            "  inflating: __MACOSX/._DRIMDB       \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aiiBIdOASGno",
        "colab_type": "code",
        "outputId": "5d59fa4d-12af-4abd-ef06-091263c222d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "os.listdir()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['.config', 'DRIMDB.zip', 'DRIMDB', '__MACOSX', 'sample_data']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mCot1ncMc_1",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# EDA (Part 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbKYHyA0SKwl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path1='./DRIMDB/Good/'\n",
        "list1=os.listdir(path1)\n",
        "data_good=[]\n",
        "for elem in list1:\n",
        "  if elem!='drimdb_good':\n",
        "    data_good.append(cv2.resize(cv2.imread(path1+elem,1),(512,512)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZLQop_tNg6L",
        "colab_type": "code",
        "outputId": "e461d0cb-35e4-40a7-ed27-6efe1f8cd335",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(data_good))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J58hs_2JNr_j",
        "colab_type": "text"
      },
      "source": [
        "There are 125 examples of good quality images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnSMh4U1NXy0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "path2='./DRIMDB/Bad/'\n",
        "list2=os.listdir(path2)\n",
        "data_bad=[]\n",
        "for elem in list2:\n",
        "  if elem!='drimdb_bad':\n",
        "    data_bad.append(cv2.resize(cv2.imread(path2+elem,1),(512,512)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Me5YG_9xM3cx",
        "colab_type": "code",
        "outputId": "ad503255-744c-4f62-bcb1-32c47bb332ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(len(data_bad))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "69\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z0b8W35nM0e5",
        "colab_type": "text"
      },
      "source": [
        "There are 69 examples of bad quality images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DyWSEp7jOMnC",
        "colab_type": "code",
        "outputId": "aa41eab8-04ff-4287-cb3a-bf755862098f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "data_good=np.array(data_good)\n",
        "data_bad=np.array(data_bad)\n",
        "print(data_good.shape)\n",
        "print(data_bad.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(125, 512, 512, 3)\n",
            "(69, 512, 512, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-TmgqXJiyp7q",
        "colab_type": "text"
      },
      "source": [
        "## plotting images of good category"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cCJnkno4AW0",
        "colab_type": "code",
        "outputId": "023dc7f2-b799-48a4-b503-fc6d81bddb41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "plt.figure(1)\n",
        "for i in range(0, 9):\n",
        "\tplt.subplot(330 + 1 + i)\n",
        "\tplt.imshow(cv2.cvtColor(data_good[i], cv2.COLOR_BGR2RGB))\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yL4jI5ih2RUXNcOjgem88fBDskIhCcbi5AWrl0SM1PNTI9sYjHPeJftDhOF8b\nFztnyEaxylykc5BMHG+Lhpnc1Wj1XX1HmdfvBRWE72h1AYdKIrlkQEslJj0kUlfibw0ocj6Zp3aH\n9eY43LgSi8aBnUSujeZFYtO0L1EE+bGBCR6FDuBwVAq73cBF4C4yGhbjm0fHQ+uAHDoLERVbVJy7\nrBlXZ1vn5haur+DBQ2e3ges9ge9aYFVOLUpJPPDKhOCEklrkbCTT3ZUMynS3E/NNX40HmYMCdtjr\n8OlDx/hsB3UWPWiI+VQU7UXqw8ECQVYJh6goaZ7k2HISnnu+0xO9KgKqjfoTsVWK+6iUcN7BMCgz\ncYj7UvgqNbHZqKlhGhX9xGCQEEQRll+9aRwLSii1iaG8lsv9SpYl2F04Q1a7dJ/U2nucE9Pk1Nl4\n+FD6J9PkzLOLU48gpFYQy1njikp0EuYUjQ3Z2GzktA8H4zgZh4MgKCfRD5VpDJ8QbfgNpjvOMITE\nKgj+sQTzlOia1kWC6gmjUqlMk6lrtNoyW809MU9VexvV+9zd/SD9obeFmb1vZn/FzP62mf0tM/uT\n8fgTM/ufzOxX47+P43Ezs//UzL5mZv+3mf3Dn+SDNFC8pfMeAhQpKW3bRDUTO6FnVV8OHI+b/frG\neH4juTd1prBEuzmtlefqzmGGY8zDKg5ziFS3U62GQ990ME2J7Rb2B21eKYqYS9P/NKVCKcAmr75o\ncQqmCGzSGqsijPKOvdmfdb2qfS2z+M7zaPQWRZJwwMeDcbg1xlGawtshZP/CSebAyufZFqNsS8Us\nPXZ9HXSeUJIbRyLqtMUxLgpyCNopwbVt4vQ1CquNfqSiTUycGDQUsVsmQEBOlaEXcOB1xZUBcP/Y\ndJBXuV7FvnoUOtvIKpBw++WZ8/CBHNP1VVD0OhWfyyx8PhlQE/MkBymBGtlLmz+XkuCf1tGmcTmt\nXqOOQlD2Mc+y++NBBbYuy9anCGSmCfDKcQx9DCdYCHqNtaNR94oXVExDhdp2n7QRXHel+32Sc3gG\n/m13/73AHwL+hJn9XuBPA3/J3X8K+EvxPcA/B/xU/Ps54D/7YW/QIk6vvnQdQWxoRJwqkGljt4MM\n5DAax0kn5v4WXtwY/UZjNnZDxf0UQxX60ww3mGPLBXRnjXzKqtV5PIrj12VnnuRknz+H/V7tx8mE\n44rbnRZnas3A4ybxFjGnNjTP6Dpj6F5bhPtK9tVMe3icwJKYAoo0jcOhcrOvlKqCRU6CDxoNqBTR\nwA6jIKVa1oJoLcJJp1n7N09KLccxxXwxApqK/a8wHsVcMJNT7fLacdYaUxqW2wSzvYbO68Ij1n1Y\ng79roa3cd8IpW/HucHuP94ONproAACAASURBVJW4/lHUDqGRxZ52G6ffqE336gVcXTu3ezmt7SA+\n67DRDLLNJvSSI0NoxUhHzrKPQ60mNVW0zpgG2zTRobFqKGy7L8zCl6BgqgQkVYsOU6KgrgPA45AM\n+qjL6e62avmeY+C2uPR329cf6nDd/dvu/n/G11do5tGXgZ8FfiGe9gvAH4mvfxb4L13rfwcemdkX\nf/hHkXUWXyMgvac2dq1E68EutegGPnqmtOVi52wDnF8j5Rbt1MBhWvSpny9FtlbgqgGeV0EYjUPo\nNYo2xZm88tEVPH8h3C/lkKKrK34sfDk2xxo/dKWBdZ0KgdvN64lwX9W+zoGxNilK9zZlAy4vpHG7\n6aKlN/B44ayqUs+T9jqn9XCsRXDCfi8DOt/FeJekSRqteMnJ4VddBjsXW6hbpZzCCabRTKZ7oxkZ\ntlJJ3FsHmyhgKQ4IeYQoItV1KsTrWK9iXw0WZslhDEW2A4yHmFtXnLOh8vix885j5+xMe5XRoZeS\n8d4jePfSeO+h8d67tkSgsl1du5TWDLKP9uBSLLr4BDEoyrWFFnacoJS0Nh9F8Q3T/rR6q2o0qbmd\ngK3W920sk2we2sqsGPAd1qfCcM3sx4A/APxV4PPu/u340XeAz8fXXwZ+4+TXvhGPffvkMczs59CJ\nynZ7UvByRY0pgG8XdW+ZJ1VDwHp/K8Pcbn0Zo1JK62VX5FGj6aHLxnGU4a6dZ4J/NKRSYL/ZqnJU\nXC2iFp8jBcd3tzG2aGPHETZbpTab5uirhM6rtS6WAOdLFIoMMON4VPQW0Pyn2YYf+Xpp+7rRnh2P\na4cWLpjh4hwuzmUYt7fC1jad2kPJcHOwZZy9VNdsgWNA0U1n4k+Po3Bzi+aE0rjY06qTAFHhLiqk\nGR9/PXdjniymN4SM4yTNVGtZ1skkg67T/TEdNVWiEIpZWdFwF5zh17le1r5uBmHwXed0AfT0KWCY\nqlbnWlQoTTmuRWjfnu2cL33O+If+QWM6VG6uJOT/LKnFd+hXtkOfAlsFqldmAvY7GNuNrr/uL6Wr\nOYSSiiFqplUsWvX12eQ/5mAWMUIXjBhiLpvUCnX4q24bk4h7FdCbENJnXZ8Y2jezC+C/A/4td39x\n+jNvc3E+xXL3n3f3n3b3n+77qFLPDcdNIqBHu55OH6UL42g8f6HvL88qm058zSGwNsvaiEobpwEg\nJa9W/V50DmqI15SW9mvjzIXxTJOfTIVVSc9dr3mxlSLVYS/c5/awxEEqloWjzl20IhaN2KnVpCaV\n4GwDD88/zVX70a+Xua9DL+NsAtBzjLPxoigoVfjC5+F3/4QmKEPgdy7DHIY28dWXnvx50gE5FUk+\n4jBXU1bixlx1j0xTFGwmyeopArL4mxvUEfeDKwWFqsgWX6CFefKlacOrtAA8UtE+CYccenVO5YjS\nE/W1N7S8VHsN7QoPil49UebbdDH6PIs5cnvQ1IUnj2Cqzs0N/J4fS/zu31X58hec8ws52MtLRa4p\nOL3ZEq387G7i8E7rwXsI5ycucAMaAmqYtH8NUvAw4MXOW5aT1tdvwkkef0pzrMnQuKT4ud3xEP1E\nEa6Z9Wjz/it3/+/j4e+a2Rfd/duRgnwvHv8m8P7Jr38lHvttV60S/8idU5tWwqTToJGq2zjlnJ3z\nbRJ9qMrBKrptUSQnt5JDCt6lO1PTaGCNqFfDsOViY+L2qTBjITge+gkGHmzAoZdR3+41u+twDCMO\nJz9PqCU5VYatHK27NFpr1e3UwPvXsV72vgJQY+x4Mo5HZSNl0nXIMRngnUeZvq98/Wsy4oeXwtzm\n500LV40w4jbrd7ukNupAg4AGA4RCnKTdKIHxliDhS0tB90SJWkEtSHjGjGl2Olehc41+dYCXKhnJ\nuagVXCNkojPJWTrgapVmwOtar2Jfa2Rxp1nL7Hqs68TeSSaogWJse3j3Hfjed52uGk8eqwh+ewvf\nm50HZ8YHHk0yicDa17bbEjBATtD1iTJVpimiU1Lg7zr03LXvudMHasyTBvNMk9Mh51xMzKjcSS3u\ncJTU5mYLtwcNQTjd17sepJ+EpWDAnwV+yd3/o5Mf/QXgj8XXfwz48yeP/+tR/fxDwPOTVOa3eQ/Y\n7kyzkfoVDCerD3qzUytoP4iz13eVxttt1eE2UsVPvm+R6TrRIdTnWzGlNoyvMQviX4tVq0asFCJ6\nmoQDTSN4Ef4zdFKQuj3ESJCIqspsjEdBGXUGSgwubB1ycaeOx0+4Uz/i9Sr2lbhJ5+LkToUSXNHP\ng4fOO+9E08HklDHz+LHx3nvC/MajKHm7TXQAto4vWtvvWlxtMGsJlkI8qiKph/xjNKc0hzzPgc27\neuZvb+U0ShXUA6H45RF1h1fP0axiFlFwa3RxpautUpheH0vh5e9rrNZIUAKm6QYpew2DOLl9L8fV\n9bDbqsZyeQ7PrpxNJw2T83N1fe12rdBsC6unFDgeBVFM0xILIee5slK0C+pCK/NKCSwxEbplrhSW\nlt2luN2KoJF14fp512bmBduoRfL1FQiQ/+PAvwb8P2b2N+Oxfx/4D4A/Z2Z/HPh14F+On/1F4A8D\nX0MCXP/GD3sDs8ZIkDENMfm0D75mtrXfHtflNWudKzI0C12CRnJexuO4sKGuY2EOrJ1keo/dToPs\nmjOm/bcVYatOUhW6hEk54mfOBR4+hOtrh43S2aHXYdB12uxxFhyy7WNGVqSq86QW0teE9b30fa0e\nAjPZyBgPH+qaHPfw4DJzfaVreXD46vtwODrf+ZYq0g8u4KPnMfYoazPGUdHu7RGpuQXjpNQg3KP9\napM8hkEc6uKCLxK6x8DWgzqxaCiXKl506iNSkokzl8bdVUCQAudqjt+jyJsjOwuf8LrWS9/XttzV\nhNCZCp85BKXXwpctlMlGmc3ZePik8v3vw8W5wax8cVgon2qQaI0ljTmgQEg+YHsG1zdNPc4QWaIu\n+HwNyGCRBj2pATVIYHXeqz+oVU1N82x4JmQUtNcl5DYXHZTPuH6ow3X3/zU+32+1fua3eL4Df+LT\nfAgnCO4JLPsyflkyuA1XiSg06F1NQyFlGaEwXl8q06cRLKycTaJ9s52UUzEepoRRlmJa692utXXG\nCMdrNwJEh1in5/U92KUUprpB79Un4XqlCmoginlzkaD1eBQ/sb+jGMZnXa9iX3PSKJRxDubHrPlm\n3ZkgpPe/ZDx5VNQKOyvFPNs5T2+M2xvY36hQIfqRDrep+LLflsBLDdUn1pl0rgGV20EG74HzeoUh\nBormpOJrm0fm1UP83CT9ObcMygPrb8MKdSPVpeuoCSXpXisRCb8uROFV7Csusf+UZCuNutXe1lh5\ntBCNIXFIdb3z/EPj+TNFoDc3cJxCoSsmSNQKNJ61NehPwx6LOw864yYw+JpaUKQgZgqOvqF23pxh\nSr4W0muSbGpVRFvcSDktNm8o+yyzLxkaiMpZCczkDuuN6TSrVan7plNUEefkIv/W6ByJkxMzsNZG\nv/LQJyDV4ATq8S6EpS1bPA4tyrECL67rcoJBGDh6r2nW6Ou+1y21cAUjuuozUGGToAxSwp8N8pmi\nrKajkPu1YDdO6jMn3Z1I/aYvFTSNsy2887hymNS6+fy5k4rx7Gnm8gKePq30KXF7rHz4VIfie+8Y\n+73GIr3Yq4Op70xTGKb1UGx82xg7pogX9ejXGrKLBmNVqNQlSGkVnq5zY6RIUvAYkxzaUA/HSJ1q\nDIpqjbkmnMpUWYZftkacpTB7T1eYD5bUEmtmkQvoGrjZScZpS7GzdZX9zV9yPvc48e47Gjs+T84x\n2rb7PnQrZlvmxQW1R/aajZuD9nOBjyIyrkjAHE+SXYzGhZx1iDY9bXNb9sedhTpmKWCNeE33tVmi\nGwQn1Dvu6xvhcFt4v+BeEdm2E8fbIEksaBotili5nTlYCSWwOlWS11ZObFUfEkUkRDDqivuZxZym\nIL0nE62oyxIzaYW22ZXadIMw5mnSwaCGDBHwD0FVsU4n69Cz4IfjEbohDpR77G+d4D1HvztmbDvn\nZoaPnjuPLis/+RPOthcv+cUNXP+68ZUvwf7aVcAwOb9Nb3Qx7sYRRjpPq25FDRJ+CXzWTG2khozT\nuuDQomiXKbrPAsejtq42j4keyDiR4W167V0NyKl6DV6u4XHwl9IEyP23jzHvwWoV/mRiICSiwK2f\ngktYhoD9yhy6IR1cXYs58O3vV8ZRWOFYnedXq4LePCdyqkvAlFPTpdUBuz+y1ALwNnl3Ff5vl98i\nam3NU9VFJawuRcBaNbHDqwpl0yTHWqrqAp6QVGTfmqdS1HdeIqTwqlaTXWz4thoddMHbDZySeH6p\nsMy4av31p1XiVo2UDYoR0GT3hOXYwq30KLY0kZklOrE1eyjRbGEmqMACB/RiHG/RjRWiOU8eaQrE\nPAffM07KuaAR6oEP5+icsXtsmPiaZpeZaFYwPnwqPLxU58Vz2CdhsqVAorK/UYFzHOUARfM6SdUb\nto4iU+Hy8qalsO5p1TXHGiy1HnA1MEUcUtPJdaPLiS5aPLNJhaod6h4OZKwNsgp931nYfZsKMRUW\nTPm+rlP8s3pEtikySIU70pKvcny3o2oWrVHo6ZXkSqWeZ3z3A90T8+yS5YyshdYhuoy2sZOu1PAP\nMfTTwjHr861TRFLguRISb3eNoqraGiRM8ILNtsAaEqmHrqvMFeoUkdgd1hvhcD1SdIcQAvElIrXc\npNsaZCDDzdkgr9BD08dU9b85aqUpKZ/iv7Y4T0ESLI64eqX9epTcCO41TiN7WziSVhhoaYnwyta+\na71jRTdaSrbyR0dtfql24mzvZ5jbCpTjpAPmeJCiV6PuXb2Ab33TePeJkTvnu991vv0towZft0YD\nSjvMOIWZPDiSGOPCo9XEgGkOVkKS08zJloJJl8QH1YyreM0Wqcbn7vrK4SAYocyROdW6UNCaHq6j\ne3GcgYRGvZcWRb/66/3KVgQlHkM55RdPW0ObDUfhKhnTEUWKLt1hd+P66HQz7A/iq29iMoNH3l6d\nRct2aXiZ2ry4FFq8DoHb5gTWtxqMYJ4cVM7cGpyieqf+JlsOBDA2veQChgjYWv3nlLd/14z0jXC4\nWm2jDG84ysd+ZAtZXeB3RL0mQ7KoJrY03WPTl9ZdT+Ega2wqS0GlVgsn+4Mpvn55qjJk99YS/PEN\n6fq1iJLCQ1sVrLDdKJIbR0jBE66un+v3769luoulMPQh/r5XVN9lTdowg9xVUrYFk3WkrVpHUebG\nKJo1HLal7DmKJWZiQcyljTU/PQg92qk9DmbdO12yUIPTfTRNkfUkB6vKlNB7j5PYDlNRd2GTB+y6\n2GeLSnqKCbBpVUi7twcp4YTSCiks2UNqWaWLBhi/0PXivBLa1i9unMsL+P5HcHamaLLG69aQZEsE\nHj8rEp6nYJ9sAjPHl+xTjITIYk+KbEtKFJyTdBKFm0gV8Rmjk7AGZztoZ15l2xWHVEXxvMN6gxyu\n1pKqhEP1ul5AGrbWQN/YdD8B7RcM11tzQ1gxEpmxFFXleLiZhNdGAwpycygWFUR7kcZqG+PBElFp\nU43zc+P8TJFWNxjXB/jwI9iY4IWzizDuWUUapWL1tfE1X8WqDi9unF1v2Jnxre86Dy6Nvk8MqVCD\n03q9N168qLy40qTVWsLAZidtNAV2PGi35mKqigcs0+h/1aH3oGa1axp+T7PmVPJWgUfwVZvQqkO5\nBq/XmOL+meaEV+cwOtUSm42zM6XCLXGeEyeyjAQWvKpR3de1QCYpcPYa2WnSv74TLFOK4IDHD1GB\n0WXX02zsD6J/4k6ZoEwsWG1zfqc8WVwskwYj1Yg6F1pptFN7tNQnYqIza2RqOLUk+o0+WxdUPuLQ\nhxXCnOZEzjVgpfZZ7nbd3gyHe/JH+PJ/OukkRiIy8zRKS3W7CYqO6aZvjIEIkhfqmFfhbqev7bV1\ni/nJezW2gC34oGbFJZLV4AGGjmb1ONGRsw84YTPIEW8G4/wSCEHjLjsXF2pHnqZQRKquG9FPKq33\ncOUElxc64K73zuVlu35V4t4HuLqCjz7S8E6vxpOHIQ5eo200Mgq1ZkPf9E9tzRYqRqopxqM4Q4JZ\n+QrVZVTKjFJwvZWKNt3cHOpt0mzV/j/ba2bW5YVS0CmaYkqBoVtx2tpkP0v08kuEQ1HuPY1wjRYY\n2uL42vfNs40jYM520HjyZMR0FLGGvKwKgC+ujYcX+noKOc/aFN8wuihG7nbE5Nywbw/oz1gw3FJk\nc0QRvTWtpOg2a1lRl2AsypwW9klWQCVmoaRah84DHlpS5TtduzfD4cbf4B5cSvMlag0SH3Nxjken\ndCFw0dp4A1AN3W+9VkxoPb04XtfiSY0w2eO0XX4eK0VKmBIxPdaXTqXkbQqwYcFo6PIK0HdJBpkR\nM+EsOmhyt4qjp5T0mHvgUfd3dR3sztogR+f6WtemVOfqRoWxs43SSzO4ufXoCPQ4HGU8ujfWvcEF\nGfTZmFC0m3OkhNainRgS2a1sAwLTrRhTEXvCplbN1muPRdoMl2fB9UQUwRI1BDdlSguQaxJImivR\nJWXtOL+3yyNwqEBJRhf54hxdekMnGI6WdVRnnqVFIu0UaWVMc2K7E22sHwBXC3gX03trRfKXfbxv\nmGlrPrHgTns4WMuxZxEIWaoazVVEzVy3JZxyJqqn0WSTLLrR4tCPKHqNAu92kL4ZDpeGtduSqi9c\nW3Ta5KRqcqtCNsgAmjGfvEaQdhuOvwpD60m1NU/Q8B1bXlP/9ebHP3Zp2zMb9isnLRxL7Z5yCsdb\n2F8ryumyRvy0Ee5zw4isEbvvbyQEYK3g4RIaefjQub0xLi806v69J7rW20GY2/kWplEGeHMD17ch\noUe0fZ5mBK5iW06manJGRPgJGWMUdHJW4bNBUeMIhxhv9PBBk+1Tq24tEiG3bOS+RtEOui4pHUXS\nnbUK6rDmFIhW06r7p9zjg1T0ubjers7AUhUgDX3TGZZNjTNcbgOGyzq6pCMth5qSs8V4ceWMexP9\nL+aPjUdjtxMt06sxO8GhjvSelcap4NOWIa2JFU7sYsqL5qNFM0z8HYm1VVyz19SZmsM+U2NCNDjk\njlWzN8PhxiniaQFapBTkQQcJOs7ZjhjLHFFnK6TUBoqvsowNcDdL/GC84S3ChXAEawGspR3uMt7U\nIInF+cvRWnxOr1E1jdPw6FIy+ugZXJ6j07OEfmoWVNFEV5ojvs9LHUCKGB8+0DhszirDYDx96tzc\naE5cw1FVcFE/fe6cizNh4Nf7tYkkZRnUcYI8qaosRTaiDRuW9HFJLRE9EGOcJMO3DbzYAwLohkbv\n0mTXBh0JUmrZkfYudxJAmUuoZgGt+82qhzLZ/TxI21+l9nWT+HoPHauAN4Qzrq3eIaN2Qjc4DkIs\n5DovxP44HkX36gdnu131kTWOR6ykhT3g4s+2IY9lXjFgXNFvG4e0TKhIASPRAqqALFuDhTmbrXE4\nsPgXj2K4lXVs+2ddb4bDXaCf4N56Wsjn7q02FkyEAMaXNrwwghz90h4XqQG6DfNpq12u5nSXxxeq\n2BphW2MdQMAR4bp9bdKoETHPR0Xgtwc4BDm/zM5kzs1eE2udOL29UjypMDCdfqr7t4o7h9F4cBZa\nGAZnW6mHvfue8xvflIB4GoSZSk5Raec4qehhroGfmsSwZiZ9r/25vU14NYZBYtEt4qnFQuNARlVd\nPNBa4eysMQwUUXtETyTHRyN1HuNe2uQJIoU1FT0JOGNycmeLJGAw1pbs6j6uxbyiWaSpuUVX9RJt\neomwJhqKxlmP5xyTNYphWRO6zVUH6ZL0k4fwTM154kBtkIGiz5x1wNWY4JuiiyzlaNmlYkn3SYmJ\n0Jb03HlOOOoko2oPizdaqbPZGYfrtRgq9KguP/+s6824LXyNHIFF3Yugb4maEala+QHnaYHdmFTj\nr2+bM01Lpwm0lNSX96qR+rXCzCoR2v6d4H5IEF1vZwHa62nNIR9GddPMVTcMwP5GegnjCMNON9nz\n53Bzk3SSz/e7aAZtjyQKkpMHxm102fjiu0Z/Bt/4lgoWtSYZyGDLdAzH8Zgj1nfRSk1ENxF99p2z\nO1PBbJqiA62Kjpc7NSgcjs7xoPRyu5VBx9m9iBLhkn1sBVEzlqnAltYCjHSZ1WHYd2rSaPivw6K7\nep/XklmHTS2CTwCItXA4qL5RqvizVy9YtEg2g6al5GR0sbc5aS/7QXz1FjSt7IC1Gco89Ihdyl4d\nLugAUf46E12waxxsk3NNkS1PITafsmAEPAK6KJ57Db2UarQpMz+KHX0jHO6a2ksoWtJqvsjqyZRE\n5ulsbVow06lUopDV9+K9WlSiTwc6NrR9xX79Bz5AEyhPSyTb9BusKDoiiNM5UqjcJbIZVFvSpGQy\nxHGU7F+porGkbFw+gPMHQo8Pe+P2pomu3+Pltiq+db6Q0lOq3N4YP/FV5/neefbU2GyczUYG0HUx\nfDKEwPWYL4VPk3r0Gt2YKGWbrZ5zOMJxNOY5+uiTevM1QkeFuWn2iGBWKAsnJEJVnW59+GoLF/7T\n8EMiu5rcqfEaLWi4z1CRw9KpadEvvQYtRLu8M7vap0uBp8+Nrkscp0qZVQQ7xPDIZKvaWspS23Nk\nV3mhe61Od5xaQCT+bU4xsDJC7NP2/37Qc/oePA7FaZJ95xN+NvH/tcZ95bovyiy/MM3r13dZb4TD\nBU70a33pBfCYwnDaaaLOFn0TdNlW8mLIMOQaBqQ5Z42is14nW7Cc9k+VzgWZ0nNo9DFN5Vu8L9BG\nsyiNCim/qshqCtX6rheZ++KicnEp+lDUbNjuKmeXMJz50rJ6X1fD8Nrly1mTOXKG73/oMMP7X4Hf\n+Jazv2ahf90elC2YGX0WtagVp1reXmbDa1J0FIpfRiveGJcX0lzdDCq89CG0sgnh+BI83HXnnX6o\nASXpkXlEqbErMlLnlO7TOVLqhNpWS4lJEyNMx3t8kLocYu5OhfqjGB0B780toR+i4ifJePeJc3Zu\n0kqIIZ7jUWyhnEKQPstBbrfKZqdRtjPPCoYUTRtD1wrXa3a8KK6Yot0umA3ZoIb+8XjQPp3vqpxy\n8jUbMUFgq8Zum/wBWPrNRdvPsN4MDPdkmf9mhMSQMdVG97Dob0eC0O0GmCMdaAD5wnQwJ0XTwjwZ\nXR8NCvF68wnVQ7zfukQwkV2yVMkM8OiwsSBox6HQRnTvdqKVlEmGz6Sobb+Psc4nSmMXl6/qyr76\n5d7SRBXBSo2uvkjddxfwwYfG595z9o/hb/0y/Pj7Eq1uezfNwsRvbk64n8FvdozdToW2aYosI+iB\nrTmiRV4ehZXc+TLhYbo10kYRcvXmSG2RYqzm7fwFNHWAWTPTUkTf41H3RE4wmqruxxA9v7crIs3W\nGl1dX1eIg1EatSlp/24PxhfeczpznjxIfPChpj/sdnB7k9ju6kLLa9rT+l50yuZMaxE3fxsdbBY2\naeZSAvRgPzQ5xqCtHWdh98NGWXBCE3oJPm8E02HPzjRbyAHIp1hpXXB3w2/hTXG48Te4qxngYwNP\nw7k1sFodLbZgsRadLooeV9gAhOHUKIQ54vaKstLa/FqbqFM9RXRUl+i2GVqyVeC8kbUFLyTaOJeU\nVPBxh/Odc3VNYENyykMHYydD99EX0fTTwt19XEMfqVpJ1Fl0rxmj2znb3mFIHG4T73+h8vUZvvZ3\n0UTXd3Qw3dxKuP2dxytcOI5yrmVyDrcWc6t0vYed3reGMFB1OdF2CCtS0r1hu8rtrQWlzAPnUzp5\nnOW0W0H3VODeo1MxG3ivzrO5ClNs+h2vS+f4layILFtHlrfrEvujoaEKnq73cqwtYMmBy49HFbPG\nOWaNoZlhXWDnxVCrtgGxNzmDVYsOQ32UBjekJK1bCzbJ4aj3syws9vxCXZ3HY2K7Y6Wc0gI2byMU\nF0fe/timtdL+vrusTwwpmFk2s79hZv9DfP/jZvZXzexrZvbfmNkQj2/i+6/Fz3/sk7x+rQElVJ1k\nGpPxA1CDCz2vkULUwE5F1RHWpqJJDIdcYABbDGTTK7oyQqi4OtmkbJStCjMyC1wwRMdpHW/CB6O+\nQqmVnCspqylDimdKs/Z73STz3AovSpmo0vwERcPHw+sF+172vuJtL5WRlFnFrFpFXj/bKfqgOl/9\nkvPlr8D3r52/83eN/V6dZ5fnwvz6zhk6jd25OHMePITzCzn1zUAcwq2hRXu/FFxsHWKpiFaaGmcX\nhA6Cil9N4m+7aVhzwEpFdYW5qFlFUyZqdDGpc22aQ8KxOaPXuF6+vbbRRSdZRGWZM9Yl0SOnIofb\n9iCZc37u3NzKxoatc3296lQ3iLDBN4L/khglQDIN6FSQoz1eCmnBB65u7LYa33O+a/iwqKIpKaii\nFdFjlQrFazAsFBA5wm2XAbPcvcj9aTDcP4lm3Lf1HwJ/xt1/N/AR8Mfj8T8OfBSP/5l43g9dCx4T\njQHTEQ4HO9nQ+MDeaF8R0ZowmVIiVe3k2DQ40mDBXexEo9OXk0y99bpBtMGqZGZrlecmvegLopDM\n6FJaqttzkUrU/la47Rgj1IXz6esS8owpOq4g0uXxU+zAy1kvdV9rYem1tzjImmZF38dBajo8DXh8\nCT/5VeO26GDabo1hWDMXMU9qqH8BSNWr64mZab7QlbC00vcWic92+KkQR5UTtxScWuRM+65RhFom\n1PjEeqy40ttaocaRPFe42bNMkH7N66XuK87Sftugm1JMynDZmGbjxbVao1Wk9EUGdbfROCVMXPXj\nKAqfhz2bicXSZ1sca06r3RAH3Lo/rf1fr7Xb+TJhNyQ06GJ/N70vBfcWvIlaKzdvgSWL6ULIQrZ/\n7c797OsTOVwz+wrwzwP/eXxvwD8J/LfxlF8A/kh8/bPxPfHzn7FTIuxvtyKsJ/4pYvVFPvHkwyx6\nBBA6B0HvWrvNwqh6teQW90WNf4leP1b9XCPaTFRNCd5evFZKaammNmzXXIfCOOtGE3Rg0gVgNe5G\nPWu6vKADQroQr69ujmuWTgAAIABJREFU+Sr2VYI9+vunUZzXUkJWz8MQq7O/1TXc74HqvPvEud7r\nGreo0SzkNgM3p90HkfG0/VwaV2pdnG8Xc+ZAWcc82VIVd5eDHCfXjDlTpAZ63WkM2c0+JtIOojGV\nea2lekTTwpdtjRBew3rZ++reimQss8aafjFhjy+unYsL2HQymDaZY56d7UZNK8pynN2589EzDVxV\nRrtGrLIjW7rEGnxn0WNtScGVuXF72wSkWCAMR6yFKdgqjf3pHjAja8v9Sk1dC6mNreSRRdc7ggqf\n1Nr/Y+DfYeEP8A7wzN1bA+M3gC/H118GfkN/gM/A83j+x5aZ/ZyZ/aKZ/eIUlCqrKXh6onVsh3a2\n6I/0xcBaxNNEJ5oDlTNrFDOjpaI6dbtOMELTwDRUdDNUQMsLO6FN9lwjYWCBMIr7MmqnorEsDy9j\nsGGF22hRdDR1QFGQKui5W/FntSW+1tTzpe9rcUUJHp1X/SCnNR2DCJ91U19dry22AA8uqqL/1roU\nUWnfrxhb7oJTO2gqcJcD089ypNIhDktfaH0N94dpNKgqbG564zCqwHJxFli7q0PO4v6qAVW42yLT\n1yQVyizssg0uXBpmXs96qfuqFugYXR4/n6M5JGc4HKUIt+1ZAiG9vgXVTofWzV7B09nW6XeVD5/C\n9z5wrq58adnN5mx6X9gLOapOOcU9EcW061tRCi92lb7VWiIlHWdNadlsKqmrio4tmEsO61Re1lls\n7cx0lCVXQWPHw90CpB/622b2LwDfc/f/407v9APL3X/e3X/a3X+6D3GRJrsW70zUxlhC+gV2iK6v\nRkOpKmTMU/wsLp6ZOpqGLC7o0BldH7/XBv8lXyJRvVmwFJLS1tQEjE1K9JvB2Ay2RMiliG7UZjsd\nJ6ma9X2MBukSm61xthNnd548tGBFe3ld4jWval8XPPNEtW2eK8fJhJsG1nmcYX+j7jyLyQvjpN+z\nLKfY92Ch0LbdtuxBhtXuA5Hd22dZn98Eh1KKUfW5krsa+PoKWTx5oMO5HagSJfIlwimzPuNchUfP\nRRnNR8/hdt/aVV9rdPvS97UVvhrrpMzG4RBdexNUTzx5FJAdjZMc2H2Rg7y8cD56RuhIw4NzeOdd\n2F44E84HH8KLZ5GFmJNSouu1v00PoUvKmm6P0r/YdJBNQVuNaLTplqgrcblG+i9or8yWQ7xE9K7n\nycbHOQpxt7bUXz7r+qRj0v9FM/vDwBZ4APwnwCMz6+JU/ArwzXj+N4H3gW+YWQc8BD78oe8Sabo3\nDM7Wrxe4wNYx49nSUsSaHeajIqBtL+pP66POEYGAih4pIiUV5jTjyEDpLW2ig9KXRiExRAHKMTQP\nwslnYtx5Q3gUqQ0blqkGXqrGAuXKeDRKScylMo3hEF6fbb6SfS0V0gzey9iOI/gJq6Pv5IC3u8RH\nLxKfexxtnzNMs3N9lTg7WyPLIRT9qRZRVVURNElUxhaEvmG2wvaac8YtIlY5gNRpMvCLa2GGAFRf\nJoWkXKG2Ka4W2GXQjYqzPwgbPA8tgDkOiddIPnkl+9omWh+L66DpwQ/gxbk8849p2+LqNqvZohVe\nh16pzjHYCmbq/tsN64U7HuDZM+PxYznUbODZCUYXt6MM/OJM4+37oS57HrUv9gdxsWsJ1TBWSmmN\nUNZa5mOtnkBjvi33zW2bVHLHgQE/NMJ193/P3b/i7j8G/FHgL7v7vwr8FeBfiqf9MeDPx9d/Ib4n\nfv6X/ROMpm0iE+ryanBBWmZULfhsC/ND/1KnnWFZUaPmEUX7bUQ2Zbmgggxydfpk9HmFDlLQR5K1\nVs82dNBxU0ojTV5nDtL8NDok9fxPExwOoikNvZ4zTiLHz0W92+Osjbdo3sivcWrvq9jXFnU2qtQ4\nw/Hg3B6d40HV/nlW5888Orud8+I6LRXqJ+8Y3/iOM46J46jIY5rleI+jboau0x6K1IccqrEoyOU2\n10ol7mX0eSmtj16FzKnCbisWwjhFxFvrglFagoqGWE5FdKebWxn35bloZZuNovEy213t8jOvV7Wv\n461xuNW1yD1cnjkPLsRB7wbd++rkixqG+1K8jpyEBw8l8kQwS9pMshT79+gSdpfw4dNoy02h2WCw\nv030g/HgXL5jGGoU1myBAm8PtmRCirAbthGEqIYVB/a+/NXxeA0o7BDUQYKVcpd1F0Di3wX+lJl9\nDWE+fzYe/7PAO/H4nwL+9Cd5sUb9akyFRu9pEmtN2T2nmGLWKtGsbZvD0CACX6vjrqJbe72UmhE6\nXa7x349zbBuuWkqrYGqDZmT4xxHGWflSznreXBNXN8Zup8dLXWUYU3LGUaPYS40up7mqM+e1Qn2/\n5fqR7mtTcJtmsTnGUcyMwyQHOlfRvXI2doPjCa6uRa17dKlmgu9/4Au8o8OrhgxiDP+rUYQzWzC+\nlTR50qUYm5m7xpYAUgwz7MTDrBaCJVnTPkqVPuscHWTPripPnwmvfHChSc2l+lLNHgYdMHfF+l7C\n+tHua4ZhowvbZ6AmFRFnCczPEck2KKY5Qmg2boqEk3N1nYBoICqr/ZYCu9559MR59ixE+4vxYq/5\ngWd9q++sIvJtr8dJsILEyH0pjOvgtCWQa0wExxabbdmRGYyjstq+UzTsdzxIP1Xjg7v/z8D/x967\nvVqWZel9vzHXWvt2bnHLyIjMrCqVWi3oFjKS3JYa2xiMXiy9tB5kYxtsIQR6aYFt/CL8F9h+8IPA\nGBosaIHB2MhggdrYjbDa+EHCLVnq1q1UWVVZec+4njiXfVlrzTn88I259slSlysrIyMj6igmHOLE\nPvuy9ppzjjnGN77xjb8dv38f+OO/x3O2wL/7ZS/IfY/r1JLQ2t7c8Gmj7PuR6Q5I/EI3vCbExnFv\n8DzrlKrnUyWxpySjXDPW2D6pUvVqC9G3zAup08aqmc1hqMpWhd3WuBWE/cDdhfkFPYwkWKNkm9Tp\nh21CFO+XN17kvOYI8yX8AmNyZh4lvGhjtS2TDOatk8LpM3jwSB7rnRP4+BM4PhZrpWLzbXRXaJJD\nCEs3CGvHlfwCrZ1hgNYNSypDy6PmJ0fWp4822/0QWesg4ueiUu3LrUVxg5NmcHwjtnmBfFVFDsFf\nbQe7l0uvBl7cvFZsM4/OfH5lL9ZodCLs7VsalXIFjsMhEtN3bsOjh87BgWCDCvc3LYy5YG7MWyMv\n4OEjVYmuljqcrxYoWA2D0UG86aUIB1HBFu7rGGXI1Zt1CMhPZbxKlhXc1Hev3xmLA5+eu+2nZM+X\nGq9GpRl7yGDKaAZGZPjU2qaxfWsNPYnPyaVVXm34tBKHzkZTdCLX1GP9jKktuu9PwFQNdnjCbsFk\nMKJXWi3ljd5Wkc+7vGTyjLW4bM9mwCZKVD/UenO1hH7vg6/tFr+UUYpw9yZDmkucaBideSQZLYGb\n5BDH0Zi7c+eWDN1mY6zXcHjkfP99+MY9YxXhey46YHOQEGor85IUzZSy18WtBqJUWlASRmtI1PrZ\nuUo4/VLvK3w3Mj7Bfjk4kEcnQrzw5UxdE/KISvGpqeKzzfNtzFd6uJyZJnSJa5hWWQqTXnVErETf\nP4iDbCQ67qpH2fLQ+fgT495dZ76Icv3I1ZTwOpcLGIMd0kQLnaqTXbt513LdbXSMsNA8qTCA+57G\nN0bSnOQqeBhleJvGyeGtPzszuoVw+e1OEdo//M7zRS6vhMG9GlXvja68kVKb8hWYDOuVxoslNkht\nh2HRQRULXm0b2gUehtT2RvbqZpzYColooQIkZT2hlgLrtSJ465pmM+h3cHFhnByHtqoBKbqODs56\nrUXQD0oUDDvAlDD4+NNruiljFJf4etuI7N4mYX45oKMqCO2NNkbfayOVrFLP5aHhF8YznMenxnIV\nJZhJcotNo81ZoZ8xpBHn8yig6UO8unOy6ZCrbWCKw6NTWCxFwK+RVAmv1SxgpWJTksWLtBR0aKru\nHgsRl8gx9IPx6Oz6zqsjzvKy0V5oUnCbc02GQSo6SAvGaIW2qUL/VxJU4bycHMCFOZ98Zty/q+IE\nMMwKJOHEc1OF4dMNHBeUVzEj5zLxdj2ScxcbY7WCIDZpJK2N7MolWCReE1qHQxyw7nKOzi+gncPF\nhd5nvgIfE9/56Pnm9ZUwuMAkWlMLGSqmY+xJ77X9sgUn0iIRVuugPdUM415IGNNpNw5aEKlSQAKj\naRoJX6QEtPsEiRIsNpHlK480F/FHHRUu9L1zfh5iJ+4Tz7DvjaH3EO9Ql9Jh9Kha0s8nnyiBdJ3H\nRBgv+zJQiO7KxSijkppjsz/MhkE19UMkN1MLN46MR4+dGyfO8TG4pyjl1MFZirznOqfywAwP49nv\nfIKoclRIPTxVhHTnRJsagzE8oLYx+sCKc8yXW+Vgh+eUawIoIq1o/f70Qpv6ug6v1OailvEWkUPJ\nkb9wJ+UQdK/tqBxJMabYx6OYHmqJY5wcwXzufPoQyiimz2ym96stchqDoyPn0VOJ4TSds4sCFhBU\nsNnWxFutOJRtyFFeX/H2ZNqfJVduva41Zzg7N5oZeOOsDlFUjPGPfgBPnl0Dg1tvRs0UVmk8fI/z\nJROG182YWpbvGQbhBdd2KW0ENWaqBUaqQwnwLJGLcfRJlYoE3kTFSpAHa4lvhSkmLQCXB9S2zrgz\nLi+1INpWhO985bDAmOTdKgxSDc6uNz745Hob25o0FE4GbRGOXormIcSmKJ6iRbWzzc4cGE3hvRXn\ncAFeEkdH8MOPjJ/vYD5nYkDsehnIrguvdrArEYyikVkbJdijsdnB41OYL41bx1Eq7vsDwcwYx4Kh\neR13UR7eRNQ1Ghkl0XBhu0QYPQzG4/P9fF/HUXy/J0S5c8oo2cvJuNb7GcpsNY4dQwt3zBaSmsEg\nwVnOIb0hrLabARnmjXHjBI6PdJDOWyiH8OmDxJt3FDUtZtpn662S0MulE7tdWLOLjlYTK+bG2ans\nQztzBiB1qv48v4TDE2c2V+5mDKbFZgf/73eeX4T8lTC4tfVGm+QRRdm7TtJUN4JPk2y2rypzV9ie\nAgpwV/10CZL71P48MoxVSCW7FIXanOgWNbETyTRqt1idwk0UYWTX6d20YhpcXkp9arkI9sFO19i0\nmpZhVIizXe/D2Bz414cficM5lbtcwzGMQInsdC2AqI0HCXYHMG8VCZQirnPt5JCLSj9LcQ6WUZ45\nFj7+1HjrvtPvNN9VpLqUmrhShNEEVdCLTeyQ03M4W8Otk8RqIQubs2CM2jnEKXuWTEBclpiYMeNo\nDKUKnlQYTEbn0TPJSdbQ+TqOHMbU2cNz4yAObe39VjVRIlik5CJ51RopRGVaGzAeLt9o0cLJiXN8\nFCyB0Xn0VEb4+BCOj4NhNHN++JE4tsJjFZGu5vLa1LhAe7heT+U0XF7C8gD9rUL1CS4uhdW3BuQy\nJUbLaPyT78HjM3/urfpKGNxcdEPv3g2MNoNHHXXj4Fm4WdcBQSnJIWphSeGkGAUqWMiDwyhc0Exg\nbimuXlQur2UxU6M6T0Cj5EmTI4kTZYMlsmsZ4Xgivydydja9DPZiHkY8qCZ5rCe8Kl12u31zvJT0\nPtstfPCpTxDKNbW3ZJcn+cbNhFF0fxrNqZgcUWCQpNY2joobh9gkYxzEBysoQ2G5MMbeOL90PvnU\nuH8vNldOk4iKmQeDQV7ULPDi08fO6aWYKDcO4XApgm3BxI+Og68aaMPpQ2xF6maiDhUPOtug92pw\nhig373vj4XnlK1zfiXWEk95M6nQxugxr9qrwJyjHiiJMp6hs1kKRLTvWicFjTfV0bZo3q3OBDPBb\n91RF+uSZcfppdHQAVnNju1X0cnSsA3jY2SRY5Alqtruygs7OjNSBUULWM0XuQO87s1inWVcwFuf0\nzPn7/zxm8zmn9ZUwuAAffOacHBrzVdC4EJXLoykcBcZe3oxX0e/GSUWGt5gFSV28Wut0kl5VhyqR\njBHko7vWNk4z18SPRSWnUBNsldGw7xCcWkEY3YwA7VElEsINzWodv/prmUdrGIx+J1HkH3wgnqD9\nnnfieo2PnjgnKyUXk0MZooNGYuI/D+Gp5uLkxsiD4SHzlHB2W7khXRvdIrZwtimMHxhv3FZ3AEkv\n+kQ/M1P79CdP4dGZcPw7t4zlwjm/hMsLYzYrYkiEd1wTs7tdVBVFi6Shig+xD5dr14dSHLLCz09O\nnaH8yzGvp1vncBaaEQVSGMucICf1APQUnu5YC07CY7wq0O6xr5ooqfUKD/r0d8/Cb9+4ATmghVln\nXK4Lp0+N0wvj2YUcqKqrUgtbEpLvzC6qoLDhQh4l5Xp56aw3giiPjpgEbgqOZxXE/L1/mjjf5D2F\n6jnGK2NwN4PzwSfwrXeiNUacchULqiW2Q4a2EFcuzKZBC75khZEJTRzFq2aJvJYCFKMkn07R2m7H\nkrFokv423dfKl9UC6kKpxN0Zmj0lqRrkHFoOForztdJtDI1fL86DR8aDp9fT8/m9xnqEDx8a37wr\nrnS9J0MRSX4+i5p3F/6Jq+AgmZKSxZy+FjiMxrMLZz5P3DqB9Q7e+8joDFYLODkW1zaPOtyenDub\n3rh101jNIpud4WABZxeFi2fG4UE0OowNXhNk0xIq2vBjeOTO1VLhMMTunK0Tp5tCrWi87mNweLKG\nNxrlV9raYSM4zD4LXmuISKWZGCKlyECXmGN3Z3BoZ7HHTQa6BE0rWUS7TYhORUm/uQ7yroPHj/TZ\nd++EXoL5ZNhLJMIoxjyEzB99Jky27WC2gBs35cleXhizUVWr7qIKfvCZ8c8+Enb/VQx7WaWln7sI\ns3PgOy/7Ol7SuAMcuPsbL/tCvurxel5fz+s1HM81r6+Kh/sdd/+ll30RL2OY2W9H3ft1HK/n9XqO\n1/P6JccrV/D9erwer8frcV3Ha4P7erwer8fr8TWNV8Xg/trLvoCXOK7zd7/O3+0njev83a/zd/tJ\n47m++yuRNHs9Xo/X4/X4l2G8Kh7u6/F6vB6vx7Ufrw3u6/F6vB6vx9c0XrrBNbN/x8y+Y2bvmtkX\nUpv/WRpm9p6Z/a6Z/QMz++147JaZ/aaZfTf+vRmPm5n9lbgXv2Nmf+zlXv2XH6/n9fW8/iyOFz6v\nXttgvIQfJNL/PeD3AzPgHwK/+DKv6QV8x/eAOz/y2H8N/OX4/S8D/1X8/qeB/w2Vtfwy8Hdf9vW/\nntfX8/p6Xr+6eX0hHu5PcQr+ceBdd/++u/fA/wj8you4plds/Arw6/H7rwN/5srjf801/g7qtHr/\nZVzgjxtfcG5fz+vreb1O4yub16/c4JpZA/y3wJ8CfhH4D8zsF3/M098GrjaZ+TAeu07Dgf/DzP6e\nmf3FeOxNd/8kfv8UeDN+f6Xvx08xt6/09/iKxut5fcW+x1c0Xui8vojS3ukUBDCzegr+kxfwWT8L\n499094/M7C7wm2b2z67+0d3dzH5WuHmv53Y/Xs/r9RwvdF5fhMH9vaz+n/jRJ8Xp8Z8Bb5nZX1gu\n4OQYcLhxbL969bkphHqMz3dP8BCHnnVX/h8+u1Xd8aufGf+Oea9G37VqiaPeaNF5Ip5XuzOY7dv8\n/KgQlLtaa1/9P8YkP3fzBty+adw8mV7/Wxh0M3N1OQD2bXs/Ar5x5e3ficdelfET5/ZH53U2g8OV\nvvvxof2qe3T0iA4fKeYr/ci9NaSH27VME2f8i3Pg0hBXGxaYVP3r2rg6XHrU02vU4WP/N/fPP3eI\n7g+pcf2d/XOOj6ROdnQ4Xd5vOdA05nXdcI3n9XBl3HtD3/2tNz+/XyHuyRXt2HrfardsPajn1Aay\npdS2PPpbVW6bzfbPrbaAK1rg9fX1Lbmy99331zLNS/r8XF59zb278NY94/7d6b1/y4HZzHz4Cvbr\nSxOvcfdfM7O/CvzzJnH8b/1yokk+bYomGSkV8ERy2G2jq+sM5jOYtUbXwnJemHWxcUoiRccAYHq/\nlIyukWzcJrosHCycISeenBY2l8btm2rnc+uGT7J72zFxuTYOV4XLTcPBKnT7Quh8zJAxvveHZrQG\nl7dh9QgefgPyDxuaW4Xj98CXA3d+Z6SEVm7bwocPE3/7/8qU7LjzKG7L3wD+UngYfwJ4diWU+ZkY\nV+c1Gce/9IcbEgVLJl3g2EzzOYy9cbA0tToH5p0aR+LGbgcU4+RYQtRN6OfOOs2vuvJKVvH8InHj\nWNKIJcuQlmLM5z61xDaTROZ6ndhu4caNMvW6yiUE41096XZbdWx9+EcWzN50Lm8lVh84T3/OmT9I\nlFWh/QyaxyPL74ySHwyD/+Fnxne+Gwba/VrOa9tw/Of/bMPxYZHucCOxbt8fNKRo8ughCj9myad2\nbTg29XlJh2UeYCwGg7FcOqUUHjxMnNyAWyfRVduiQ0T0LNz2kllN0R5L8o+ay9qVpRrl2Qy2vXrX\nvfuHG2xjnN4yjp46H9wzbp46l/PE8VPn+LJw98MMOKk1mmT87vcTf/1vjNEK/svP64swuF/Y6rv7\naGZ/aTbjb9YOqbVZY8lO10jR3Ufj8NC4cezMOokRg9E2Hs1zopHcpNbPpPqeUjSUy3resDFu3nAc\nZ9FlvvUWnJ7Dw8dSmj9cSiczJW3y5DBroG+dtilTB1cj3tfg/j8aoXXuxDJqfxvmbYbGsRHwoi4V\nLk1cL8Zbtwv37hkffQgwTdJvoMznu8Aa+PNf3bR8JeMLzW2d167jbzZtwYv6ibWtdG37wehabZrU\naJM0FvcmNu7FmXH/Xm1vDo2p82/bEIvewlu1WA8e3qiEp1cLZYSbZGqvXbRWxt5547ZNXk4Z9NnZ\npc9aBuf0LHFw5Hzr4x3DR8at6CZx8LvRfr11hq2TB5+8p5zBzbl3x/j4U+NMXXuv5byulvY3j1Zl\n6rxbPUe115HYeHZ1eGhN9zUlp++jZZIZXaeDtmSfwsrlzNkVOVZNSgwDHK70nil5GNukVuwmcfEa\nKXUz6RaTLHahTxFRak2dWKKF08+96wyj8/YHOrjf+B50CQYv+OA0jr4XSEPb4Be+XfjWtxLf/36B\n55jXF2Fw/x/g583s22jS/n3gP/xxT3b33zg+smhxoY6deZC30TUwnxm3bunGtU2owyfUpsPUdSGF\n+n6b1CmgbeL8DIe0Tkzfy4tqwgNqk97vcAHLt5wfvA+Pnxhv3JHRHQdtcnlXev+KVZRQ9rcCi1Kw\nAYaxsBsgXRir42iHHpeR9WUnwWov8Ae+bXz2KYxj/bM78C+EZ6/Q+MJz6+6/cXgQzRfDw8yDhKhT\nUl8wMydFSxwwukbG9nJjLFfRjSPJIDaN0aa9UZ3NZLD7cz2PaCpaitFYeNPRtbVLikTOzuHGibGY\nOX2W4r9CUwlj9z08fWbcvqkwK2/Bc6G4RKybrA4UqVG7pZKBVGEJnwSz798zzs4d/HrO67035OZU\nGCZNUIweo7i6LUSrqtTJWNYeNU2jNjvyTNXhIzXGrHXonO0umg4kY9ZKZNyL1oY1hSYl+kGRzjiq\nJXrXql2TouN9dw4zwxr1RjOXWLpvC23SHO56WGKkptCiBqRmtm9/BQxZ6/Rf/6WW93/ojKN/6Xn9\nyg1uPQWB/x3x9v6qu//j/7/XlAIX50w3a7V0FsvwZKMlTj3VGguvIkXDwSvGz9EkuTrc0Zg6wqpd\njjbU8ZE807bTqVqbR5oXvvG28d6HzsX7cOemukucnIRn1subMte1jKb/J2Pqp2bIWBwdVkAwztqi\nTgRNdKzIGdYZ7t027r5pfPxR/r1vzCs2ftq5lR0yKm67G9Rgc9ZBPxiMxq4FW8Cs3b9o7I2bt0tg\nentoyaIDc6qYUdEhaEmhpjo1GIuF2ut4HI5NMnax2ZbLQs6KmszBkqKfYYDTM2HuoCgkJW2QcTSs\nnc5vdSBOpjbt0dXA0fuXAocz52BlXFz8bOTMfup5Ne0nj7xGbW1VfUuF/+GY1CgkGSncRXPBEA1g\njbMbopcZapXeNOpxeDCH5PJkPbo4JIdxKOpjONZGlT5FOxUCzr2iGEtOHqBttR5S0nuO0Yw2FyJS\nVpcJ4cE+df/GYYz2Wt++X3jnHeO99778vX4hGK67/wZyt7/QMCMMrLzOJsnF7xqFg+pdL+dyOrVM\nEIAD5tHRNwde5+o5n7NON8LzWF9qkttGvY3cYdYKtzMTrvvtt2WYLzaFZ2fGzRvxvkVhaTItmNok\nMBfHSGoBkwVtzOfOMKoxYs7QtkXtPtBn9qMxXzieCz//c4mPX6X0yU8YP83cOtFtOYyTmTGOxmym\nltjeCVPLF0Y6gKaD7RYWcxcWn2rnXYPslPAwTQ6W5m/mrIeAkZJxMItI6EpH1jwYDx8I+zs/NZpO\n85kprC8Tp89g0zt37waeWGKDE11noyN0k9RROtAMdXROtm/Lg8LhroX795zvvvtCpuCFjJ92z9aU\nVWDVKPulxx2bWkw10bzRCySMttGh29S3KIZhgd0n5rOEF5/sQJMcL2pV3jWKXmrCe9c78zn67BLt\n10ddVykW60Ct0LNX3F94sppeTi4RJQc0UvvbOVhWhJbdpy7g/9q/ml49g/tTjwhDrALqAkijOoPY\nrFfYChHZT0noMLyeod+oM2g3h+VSWKE2QoJiHB1pI9Umc5vs9IPegwtIjdP3ibfe0KZ/+DBx702X\nlxyfnZLhQ6FEzzOL3mubHZyshB1W/7Zpw0AUdX/dbpx2Ht53Md688bXe6a99mGnjmEHXOTkn3J1x\n0GaZt2qyud1A6XUoHa6gTYnGlMBskiAFiwlQaKhw1BJsDLwkVtGANIW3W7Lx+HHi7NJoOzi5oXD1\n9NQYBx2Ky6Vx715h08PmPJEOC9EXUV2eCRy4xMHuNWoR9lgCk8/hHQ076OZw96bx3WvatRe0di2M\noU9pfnQQ5r3na6a9Uz3dZIJ4qvPkLlx9fiDjSkCJFlVZOeu5s9qxO+gFwwCLuTHvKkYMKcvA9j2R\nnBP+O/TK6SRLlFzwiDLFbjFSq+Qp6HmKZI2cC/1oZHdmc+ga+EM/5/zPz3HfXg2Di8IxQ65D2zoN\nEUKgsL/SgRQMn80qAAAgAElEQVS6y8uspXKlyLPse7WyPjzUJjXAs07e7aWzXAZrwaBp1biuaeSp\nNp1oSJvoELvbGm/edt59zzk/T8zmwoyaVJM8CqlK1ombXHSztgtvpyaCkg6DPMJuYyyX+9DUS3Qq\nvebDK7xj8jD6nQ6pBrFHZi0sj9Wq+vQxLGdGs9D9TiavRng9tLbPWIMgheMjsRqaaoDXzvpSDJP5\nPHHnduHwUG13D+fq/jpm4XdNZNDnrYzHs6fG4bFP9sOjCWJj0GdjuzUsEn5tJN76wel7YcKzua6r\neekqJS92pMDNserhhgEttWuuvFglNeX11o7ZKTym5Mrd1ES4PFKF8P0gaBH2kW+Jzx0j6TqfhWFt\nBDEk9s6PBUNp1snRSQY0wtmxymRyuk7spD4HvlvqGlWSrySYLwSReDa8f779+uoY3EoVCWCdCCPU\nfRNS7cAbN9WuvhZReQw4WBZSpaiYFv52J9y1ZrxTG3y/SIaYQeOahMMlHB04Dx4muhm8dc/56GPj\n/l0ndRZMCPmvRu3cmshlnxhwl3WxtmBAn+F87SwX+uwhsux+lUdzHYcrxGsawC2gIkUUJRvNQREc\ngzbc4QKaO8bu0rBDGcN6yCXT69ukDTEMRtuKYpeLc3Ge2G1gHBPLlbNa6UA+PCgsljKqlSJGURTk\n2RitBOsBDuaF4SDx8IE+a8zReTapVTqNjEHZwtBrDTjGgOOmzVsTsmO+3gepl6uQwv4A1LqOhFm0\nHM9BB2uacJ5QpDCKysBiGQYwPF5FJ6JytYnolC1nLO+MnEUPNQcrSbBVI8OfzDhYao3NuqTIKiLn\nzVYdtMcSrJKcaGfOkJnyAo6TB6MfndkyYZF1L0VzPY7Pt2dfGYOr+Ss0Kdz5AKpHxNGbzXTKWFBI\nLPnkEe96hRSLrmg6A+d1V0i63RqHB5FsqRMKE2dvvgiPJhJibnB87Dx5Ajdu6rO2G70Hvjf1BrQ4\nxZyLQQyIkpVkaZuCJWW/12vjcKlQdwzPdxw9yNjXd2MKKYr29AEBFWAxh34X3kR4vVaEnc47WOea\nHFOGulX+Q4s+sOBZp5A2mXH6JDFfwM3jQtc5Z89gfWncOHGa5AyDB7sgBc5fDS8TFlyZ8D46GeNw\nZaRZYejlnXUtWIKhFDkCGH2B83PjqIXBhRNWcvz4s5EH/dLj84UlAtVLKZTskYhWhUMp4TlmV2I0\nvGBwtacffEp4Dr0iiZooW82Mti14Ft+64q7JbCqUybnoENwk3Jz5XM9rk1FGMSEED4Qxz87lZSTb\nIrdQYn26w3pjeDJWK11odiij3tsaff7zjFfH4ObA5LCJz1fxzzYAeNxpKKIYjeANjL08oK6V15tH\nYTeEB9t7xV1rEYROxpxtgihKAR/B4nO8xAZNxuWFsVw4u0EGIgVNbBgD3jAgcKLFXOB7k8AjY31+\nAfPOadr4LBI5i39YIqEwxa/XbHiBMhqp82AMRDTRKFwsWTxcI7CzWPg16VWyQQOpky/ZEpsESK3u\n4dmzxGymNfDeDxQ9LLrE228VGtNBmlywT47QN482RSmCHxXplGzstsY333IePnJmGIvOJ2+uBL/P\ng3lBduadYKjU6jldIwL/tfZwHdyFmdRIrTo31ag1KPzPY8AOaI7yCGlWWUdOjxyrEo5Q1wlvb1un\njMjLbJxyJVPZzeVYWdK9rvj5aqHX73ZlyuvUHEDJ2r/nZ0a7hIMFPD2FcS1bUDBKcqwxVgvIsWY8\n6zOTCSZRKeuXP01fGYNrrp8KfdXD0xL7zHRQwDAZx81axRGzmUKUiiWNo9FFJnp9KY/KLEgrxaYs\ns2AGPa+P+tyEHJ+2Ne6+4bz/oTFrjIsdDKOxaGvmNapYzCiBGy1mMLrwpFLg7MKYL6FtCkOmck6w\nIsMxjs755fNN4Cs9wpNPBrP559kmXaPFXKGkZDKiomMZXZd0oIVHQ5HH20TytE3GboCnp8ascQY3\n3v6GEi7Pzgvvva+S6pMbteoLGdayL7tOyaeCiFLg8VNVtjUGb9xxHp1Cf5k4PHBGF7xgZvi4D02b\n5LSdNi2xKZ0rNLdrOyzC/zC0hSmyNIO2VZIK30cUedR9zlnzaEk5lqaJw6loTrrWWC0MLzatnykJ\n58YsyRgOwYVOwHzmjAN0jdM2CUanoD3bNDp4NxuYL2C5EHRw89g5X0M71+MlG30Us/jElxdOWHKs\n19Z+/C35AuOVgfarBkI1mrU+OsUpVjeloVN1uxHYPpsRpOirG8sYIonR90qmTAR8VGWG6/QtQVnJ\n2cmjwsK2NVoTWH98CAdz4/YtePBYRj2PtYpFi2u71YnpcSKOg3F2oYmddzXppy/kJfwqh/VlYrl4\nOff76xiG5mcc9VMz/NXgymPU3E4FDvXftpCC2+y+52G3jRIhXsRMOH0Go8O3v+EcLJyucW7fcH7/\nt52zS3j33cTZ02AcxBqAGp3s2SfDAG0n8n0enDLAyRI8wekzsMIV8r4uyojS1OpBuTif6wtnt/16\n7/XXPeTRFmX9K3tAfxGntcgpaRsdoG1lH1XbWvYJNoqKXtqo7EwOZRBs1AQ9b4J8CpqfESjQ74TR\n5jGYQlsxYHCnbTXH7jDsHPfEwYHoYymSsAdz53gBMxyK+NsQLIqyP1ByDkrgc0KAr47BpbIQrjwW\nBOp6QpaiU3K7VojeBFBeXCFHDt6cu7PbGc/OjYOVjO7QW5ywNiXmLDljsAxUwSRsqMTkeXEWc/H9\njlcen69ETYpNlrPeezYLOpIb661zdACzpgSHN2gunkhm5NE4O0sslvCzIyj15cdsDrsdE1G+erVN\nazHv8mTNkhggySLxaVOIaoHbNY0OzMu188GHMG+Ng7nz7KlPoV/JBm58823nrXcKT8/ghx8Y68vE\nOBie5Vkpcaa53vXG0cono+CuQ/FkWUgdPDlNDFsmPmky6Xl40QuyO5fncLk1mk7J0es6ZPuqVxsG\nyvYsBasQTTG6VhCbOLA6UGu+ZHq/okqueYjUDIMMZ9sU4b8RmRSicKjo87fr6mXbZJS9RjBm5F7r\nwbNxvjbm8wLZKIOgLg+bMQ7awzqUVQjjHsUtwDgEjbQ443Ma3FdmWexPyKhSiQmtec2hd6lHNbBY\nRDlnfYVXDzdNb9T3cLSKELQNjzbbVCWUUgD0KSrVwvC1rSZRBH0VRnhpSDhv3oZHD4237gf3coDT\nMxNv1MRGuFzD4UEsMmcC6yuHcLtTNdrBocRT+vH6GtxwSsAVsu120CXDGxm2WacowzoZv2TOmFOU\n5QZ00CpCcZcn03WJcYRPPjZmM+P4qHBw5KwvTZSuo/1uLu7MW+PttwubHXz2WaLDuHED0qweqsbF\npXF+ljhaKSFWs+jV6B4thc8/emIsOujmkS/IERll2KxlEFYL5SKed2O+ykPwQCSwr1hOzZMoc0Qx\nUkLOUR6036xpVDmIY2UfSc6CEpbNGcYo652JM9sGjczjoJN3ncQ2amqkYpOXC7A512EwBMvpxjET\nU6Vy6XOWLVDVmTObieJXIqKuVnwiJhRUSPUc45UxuFPBw4QLCJjf7QwKLBfyIued3H1yFDuEB5xc\nYeaQxaFdrvYUleCI0aSYmGzBWJC35CXRdkScY4yjPCmFRK6SUpeQxuOn8OABLFfO2QWslkbTKPxd\nrxOrJbSp6LMj+df3sN0JH0odHB4KJwbIr0yM8WJGk3SAdg1Y61yujbEP76OF3MLBKjwml/fZtRHx\npAjZG60Bs8IwJt57L3H/TePJU+fubXlOi855emo8fWocHXskYWTQm1bCKO+8lXn8NPHeR4njA2cx\nc07PE0OBu2+IiWKJKAUH0OYriBf+5m3j0RmcPtYSrZt5LEY3U0mxylT9c5HadRy5WHBx985SG8bI\nixLObQgTLWbyMuvpWznsKqsNCMkEP3iGfufM2zSV4KYG2iYxjlXASBWLy7nmpyq1FdMheHYObRRF\ndI0OXtELjTEXUqrFN3yOR2zFIqoWdW0YVajTmJhIOYM/J4b7Shhc9z0/DyJrGCHIrBXNQ1goBPwp\nGIDgc6Kbvd7qtOxa6SWUInzPgg42eKJrPeQAffrcUoS5tY0xEpVoxfFBi6BtXOB759x/0/nwo8ST\nc9XdH6wK261xuUl0LaxmSuDserhcJ7YDWGM0XeFoGfS3yOzmrAz65+Kraza8TlT8s1o6y5lgmdVC\n93XYCVsTW8EZNlX/QphaHkMzwYynT5zFzDAyR4eJtil4EaZ3fOicncvTXSw8Sqk9kmO6htsnzsGq\n8OChsbk0VodwZ6lMtic4PQ/OaCvKWQaVFSNDfDx3Fl0iRzJIiRlnvZN31zSivFm6vnOKSfpwEWJO\nJRgfxZ3kkEtRtNnKhxkHGVlQhKpSaWfYGstOB6NnJbO7MNqLzvfFTuje73pjMRcc0La6/11r5FIC\n0zWGUdon8+TkXGgskUzlwuZaT23oeuQoVEqmyrU2rGFlXNRD10wsmlyg5GvAw1XSwsmmyo6uhUWo\nelWIUxlQnWiUuqAdXLhf34e6VGhr5lEebc77evxxjLAmyeMVPhibK6CFzUjothrjTovEizPsjNWB\ncLt7d50ffizPrOriDoNzcqT3yAOcPjMWS7i1khfdj2FsTSfu5OL6Nd6YHnhbNqwEzh2Kb53Ju1nO\nnM1l4uiw0CSVaz54qmqitlUJriPsr98ZT58Yb92TwfzWN11ZZTcIOuHhEi4pnF0YTgqCu3F0BG1X\nJrGTg2XVXfXJ28o1iWPC7Qo2hc4Wa8WIMLmRl9dnn85Ld8FbqVVhxnU9SA0gBR0r6ZEKHYnap2iO\nbBwujKEo+dU0SYlwh0SScH9NeAflDlMxw2ImuKFK/e02ARXupKkgRTIAHdpjaFgPvXSQa/JuGIX/\nWlLOwMMJK1kqdZ4TKdmksZxqQOxMJcNVRMknHu6Xn9dXw+AGVmeIztWlKzQQEx83j2CRJk7E4g9A\n3RzaVlUhtXirZGgQpDAOTtvJiA4jNNZAW7CG4McCOLPWQkHI6Ica5sJikdhsVGPvxCnZ7DOZINwW\n5O0+20izoQ3Rk5IVVrcGQ0gG2pXvfp1Hm4Shm8OwSzQFFq2Hwpuw9NQVSknMukLbwXyp+982hRTJ\n0ctz57OHcPuG8eAhvPN21aPV/Cq5KiM3nwcticIYibenT4yDwzhkI5qSsdUpmPOe1C4VMSVQRBmT\nAWk6HZbmtQQULIyweZymOFb8Wp+jIKek31pUj2kdjwM0SK+gmxlt52x2klhsZxZqas7QS07Rwsjh\nwXF1OTTD4FLqwpl1gvj6gKDGLBH6fig4iRIVfw3ykCVaU/ZSry6tBAPRukwRSUpGi/jhu16HZF0D\nOdfoSNdmQUu0oIc9z3ilEMSmhp6xWJsGqitYaSSCFCprQWHFWKomgU/4WSmhBpRUM18rmwhWA0FB\nAp/YDeMoXMdcFU6rpbFcEPSShGebWoAozFEokkfRic4uEutd4uQQZtF5QsLnKqSorZLNBDM4wDU2\nuJP2hStUXC2d1BY2m1pgogV4dKBOHLPOmLdwclTodwrtV0vj4sz48GNtpqaBb35DsFDNjFfec8k1\n4SHyX/Fa4ACrQ9is5WGVwYPys3+thydeeaKVRhgAllTERhlXwtu9WiXYzkBi3BYG/RoPj0IABzcj\n58QwBEsIp5uHYJEzQTv9FrwUmmbPnW2TIhNhv7pjYzb6wVjOEfRWRMNszCijNBSqtGKKKsJ+cPpd\n4uJMrxtHvbZpTLxcFFHWA7Y1GU8f9+tjT/cTtFiLW2qzAyweK883s6+EhzsNqzzberrIMJbiNBYE\n5NC6TTJ50+J35bti2gQ1eNA4miTAPY9ON4vF0O83xTgoAMlVRIPwPJNH0kcL7PLCWKx04la6ynar\nCpfLS4WqxweBAXtAB1FwEdI8MrzYVOhxpU/S9RuuTPGEpaJwMLXOZiMhcGbC53MISqfkHB8Yn50p\nBF1vlHBcLuH3vSPMPIeKl2h6UZFmwoVLcZV0uqCeMWhLTQPbwUkp0XZl2swSnfEpbzAG1Wm1jE0d\n158anzjDySRonof9/Eroyqfqw/RKuTJf7ah8c1Eoq9aFT/grEOIwSnZ25jRzYbCzVkUh7iH2lOSh\nWkSu4+isN8Zbd0t4o3tBqflMiWxPesXlpeRb3YXjdjNRvLoQuS+ugzcXBDlM0I9BI1GbquVbRiNf\nMSLJBFPkwbi8iOgmmBLPM14Zg9vYBNdQpRlzdtogbZbiKr0jFjd7IRKFfU5tm1Kl9KoosrXSaCiu\nogRrYQiDPolZuwzv4CLrq7rFgiTtzOaZoRi2U6HDrNt7R30oCC3mddMZxSywRQ/4Q/zenEM8w3Wa\nl+tc2kuUa1bltng8JWfeiNWxnIEtXSpquaFtCos5zJfiSJ8+SZhljg9VYu1F97+KT48hvVdP6snb\nrcaTylYRN/r0VHzroXeaQbh610E3c2GICZ5dwumzRNuqjDy1+wKJKnaUx/CgjYlKlgyGrPDzWkMK\nFsp8Hoygpe5PznJmUsyFRXm7yumd1dJYr6VJnU00z3YuCM8At8Ru8ID9iKhTB+7BsmDFJ12EyzXM\nlsbxQvtvs5MNmM2d4inoensDaQk8KJjZo9rTwBtoihLuFr3RPJyhfgsPn0BaOmUtqujzsv1+4jls\nZt8ws//TzP6Jmf1jM/tP4vFbZvabZvbd+PdmPG5m9lfM7F0z+x0z+2Nf5EKmjGB1D712c/DQ1/SJ\nglL7XtUih3rmuGuTDUM0eIwJ63eqOquh4zDqdO3r84LvW3fJbieWwThqk81bVZwlS7RRtdaEoE2d\nhOU8KGSmaxpHcfp2WzWvW2+hH5MwpaRwuWudpn05O/PrmFfxmSO7GyRz13vRtsbBSsJCQ2/Mu8gw\nN2KGHB4Yp0/h7EwZ74OVc3YG52fa7Nud2CyjGxcbJSnXa1hvVM45iZWUPXxRslTEmihn2u2EB3pR\nZZkqiaQ2NZ9LMKntnG0vz6lWHFVJzVwit4A2UvVsS355kMLXsl9dB1TXQTNzLi50z2vZvZTdxB5K\nVxT08ihoZrtVZDdEErvEQTlk2G6ItaB9utup/1lK0jLORcbVLdHiPDt1HnwKl2f6zHEwzMskUiTP\nO3Q4ojJUVNGAkmL7pcS+YrU467V413fehDsnzp1bzo1bzs2bzzc/XyTwGYH/3N1/Efhl4FfN7BeB\nvwz8LXf/eeBvxf8B/hTw8/HzF4H/7otciAWgeRUj8ekxnyZNybR9d87ixhBld/0oY9uGMIZ0bx0L\nlfftNrHdiYDf72R0h/CUhvA+JVBTmHXC44ZB+qzzxsi9DOg4GLPW2GxkIBZzXa8oMDr1QRt7PoPF\nzFlEEUWbHKcw1gq0L3JzXsx44fNaKT3DqCTYOKiefbNWEmQc9Pfzc81Hm4TjNo1zeCh93NRosa9W\ngaMhXHC1VCXSvNXvs84Zixgr6w1sNiotBabSbW16wAUD1d5qHqFnyaISjUN4OUVh7moh1kHtqyUI\nq/K+FcGMFQMOJ+AlBi0vfr/WHAaxrmewvdx7hjURFk/d/5gweKwmnlV12U5FQgL1LYSnLtfsi48i\n2ZkdtlsjlcI//z587yPjbDA+fgoPPt0b0RLOWd/L4ekHAlYU55fAgSV8ozUnIXnj7Ck8fJS484az\n6PYG+qsoCv2JBtfdP3H3vx+/nwP/FPWx/xXg1+Npvw78mfj9V4C/5hp/B7hhZvd/4ufoA0iTqpR9\n7m8lWtyUkamVTqV15THKdgFrInkWXXLzGKWeKOst6pjoScPo7MIAb3dK3Gy2MgrrS2e30yRs1vJm\ni0u+TZtUJ/FsppN0GIzNxiglSVIwhbh5zV4HputXcd1JSPvrH1/LvDpTe5VZq4NptVDxSnaF38Vl\nLBuDzca5vBDOPet0/06OEl0j4fHlQlhsHsOgmTPijDm6CmRVry0WYqXoALXp4K70pRzdN7pOB/Rk\nQB0gheZC1M67Nmnbam2UUr2kfRK3lMr5rt6v8Zx0zS89vrb9WnMnWRVabqJkQezPCvnhgWdblPsK\n5muS8NxhEA7spRo/HY6XG7Ea+p0HP18FDuu1DN97Hxp37iR+4ecK79wt/Py3CluD73w38eyxSNV1\n6+XC5OB4JFXHiEK8RHl3VkHOJ5/C6Sbx9juFrlXOKDUy4Okr2Kw/FYZrZr8P+KPA3wXevNKD/VPg\nzfj9beCDKy/7MB77XL92M/uL6ERlPosJ5Ip9iiqelPZhuue98a2hQAW9U2BHBf3kAj4qudZckWbE\nRBerGea220supinZoTJTS3CwVOdgd7h7B979oUdlFEFyD3paYEK5FHX+xPCkaqSK11bsttJXdHBe\nRTdfznhR89pFBVCKMuxK4WvaQtca804JlPlcIeqYjSdPjVsnwlxvnRhdm7h/b2ToRUO6CJiiRCYa\nByuSwNyM2pBVKnFqMpiYqok8WqvURI0XZURzUXdeyx5ZbjFQaj8zL+KH91VusCbjDHKtxvH9enwV\nxoua16ODugejNDvB4QGcniq3MevQvDSKKqqhqonETBxSWbjpdriSwylyeLY7WCwKm23aG2KH1CQ+\neQhvv+3cOFbBQ0Ii4u/cLZwdGx9/bKzO4f5bBqghAa7DvSr7JatQl7O5gAcPnZ3D8W21ySqmiBnE\nWuityK48Jy3sCxtcMzsE/jrwn7r72ec8UHe3n1KFxd1/Dfg1gKOD6FBWvdYiftxUexaP5wJUZSYU\n0mB7snLlUVbPhKgEmjKqwestObzh+A5NEYivqjLBEd5A44IODlZ6/baX8d1udLIfnzjnlyY1M5v2\nHB641Tiorr5rtSgp0gnNUbI4jAphXuZ4kfO6WpjPZvswqhQVtqghp01aGeOgLPHNYycvjWUnWcuz\nJ1L2v3OUWB0Uzi7gBz9Uq6JxqAdZGMZRO3YcYNyKiSA81Rl7o+miGSh108TJbkVNBeOgrThkyZqf\ntoUSBrQejX0P1pRp/XiRyzvxxlPEtC9xvMh5ffO2XluFewgnYnkAjx4bd27AfOXhMOlA6oKv2yaj\nj9szjOpfF0EfZrDdqaP2bO70gyKFgyOVTDPCw4fOW/eN1czJPVgULSRkH27M4PBbhfc/NT7+uOHe\nPQ+IgoCOVC1W4aSzp/DwmXHrTeeNmVqwlyK7kUxRsrpDVyrg883LFyKvmFmHJu9/cPf/JR7+rIYe\n8e+DePwj4BtXXv5OPPbjhxO6A8GRBabGkBUPCtbCMOoF1TspgaFVlaA82qTu0w+RIKuanWHMh9GF\n5+SQXhtFeh4GVYxVLqcFOyIP4oN+9kiNELdb58YNidt0M5jPC21baNpC2zpdLIDaWqZkxyV3hLnR\nKGdDa07TvKTYk69hXoE4E/V5kVmyZEEh0qFoBkOv+/POfePZaVK5dtFh6QUOl8bv+6Zz52bi1gkc\nHDiLuTNLsJwxCdA38b7qRQaCEZyxj2TqqDkfxz3fdrvdb6bsSpqoKEPwg7w5/TThGfV90M6yoprR\n95VJwL6S8CWMr2NevRSxgMJQJoPVDG7cgrM1fPaZ0e/SxGve329h7pdrVZMlJPzkLgP79My4eay5\nOF87B8swUoPx/ofG0Q3jxlHAeGELMME5HpBOHpw3bxXG1vnBDxIX5xKEV74m9noPn30Gp+vE2287\nR/Pg+tbihyrJmEP6sZj0IJ6Th/tFWAoG/PfAP3X3/+bKn/4G8Ofi9z8H/K9XHv+PI/v5y8CzK6HM\nj/mQ/U8wSvZkdI/Kj7zvm5R9T/nB9/3payvzHBthjIkes9Fni+y1bn4pKb4f0Xk3aGNZXlLfC5cr\nJihgs4GjIxnwppPKV21UNw5GLsqQjn1k4yu255X/V+lr2tTF9yHNyxhfy7wCBF0qakzqQ5Mh3lOH\njMXMuHXiHJ44H74vUmce5eUKBzbeug/HR8LIPds+EVMIWU2nm0ujtcSHucMwKlFZkGcjjd4oDzXY\n7GJD+T5cJtZDZTx4GON5JxjETOuBIgqgDI+oS8Wfb2N+2fF1zasFXbOK/dRlPE9w+4azPIYHj5W8\nBOIGahcsOmkiGJqPXezfTe8MUUm23SWWK2mODAO8/yl88xvGW7eLKHiNIIJx1GFpniLKlQpfY8b9\nW4XlLfj+x8Z3vpv4wQ/gox8aH32Q+N73VaR0726JhrTBlDcm0fucje0Wzi8S5+diwzzvbv0ikMK/\nAfxHwO+a2T+Ix/4L4L8E/icz+wvAD4F/L/72G8CfBt4F1sCf/yIXUhA/r7hPdToZbapUMTOTgZ16\nEEUBQQXoa5nmWK5kSg2aCFm8qUCE0UZElUwbWpzYAPqD4DyMgQO2ztml46NHf6XE2TMP76e23NnL\n/Zm76rXd1FqmZmGhXviU9HuJgeeLn1cHK1H4YCH9npRGH4O87gVRsmYW4uPG8UHh6ePEwwfOH/1X\nNGe7HYyP1IRyHITx1axzFTgfc6VmGd0CsOgK4Ip26qhcWqFYYQjM2OxkSGuIm5LTRSg+ZrvSRy/A\nLhf8lIthoRAnepG/TBz3a9mvNemSizD65AYNWFKd3eHcObxnPHmk/dXO6gErR2bWyqNdtto/ucDp\naeLmiXGxViJu2WouP3sMx8dwsiryiOeVwldhQumoDFnrZwykIw+Fk0Xi+JtAgst1tO1K8PbC2WwV\nQdV2XLVAYhyMy174cuqU6M1F9mCze77J+YkG193/b348e+lP/h7Pd+BXf6qrcKbigNpXzD5HlQ/C\ncajIN02FG/YWqxqvEiFDDoNba+ybBCWFolEyxfSoO+dqbgxX4IwxdBgMF/WrFW632elnPqsLBJZL\nm5JCNQs+ZtV918VVRiOjskcz3zeicwR+vYTN+bXMK3GAFvBk4V0CSdrB3kJyJ7fGYgb9Vm2zrcCd\nW86TJ8Yv/kE4PHTOnhkHh5LNXH0q4aDKJLCcwmkWAKv23IU2JS63kWSrkJJHIi1C4ZqorUT4vo+S\n75gWxUG1rXYcGiW83Tj8C6pIapqAodwjUXR957UmDStbIRkQUon13raNc/s2PH4Kb97ZV2eaKVld\nsoVehqJ2w7wAACAASURBVLHeiG3StYXV3GhSoWTj7NS5uEx8874zjIVkiVVjrEulccnbnQTRIxeU\nJ8xWc2HFOVwEfGFABnPn4tzoZnKGxizOdZpJv7ntmDD9XEKzpXm+OX1lKs2mggavXm44tEnbiOCs\npshOKf/IxLurjehyrTIKcZuaGqhMh+JgRVnIgigfl7vqderJJYxgMji/FFbr7lxujXFIrBbOuNXz\nhsGZhQdeOxO46fR2F1MBql+9hxI8sMWXnFt54cOs6hpIuq9uyKaLCqRG+Ot8ruaB/S7hnumD63xw\nIHrO8aFxeFS4XEdr+9i4tQsEvXS8Gishq1hbWssz8aAl5eDkFrPPHcYaWgfDoENdXyBwyiQvp1Kd\nxqIDtWu0mQkjPeQaOV3vifXQmE5JUeCIRIryqDktBiSV456cGA8eGG/chkVLVI8WzJRENlTUcHyQ\naFOhawVFfPYQmnniG2+JIYInhgznF0qEluJxyMmJqUn04vs8zq4PuMNMMNBCSc31Rnbl4IZ429mB\nFlr20p65NgeYWqjz3OI1r47BRR5rqSRjwohmJoHhNJ2qHv+Gd5LQDMf/i4nlIE1LkzOLuJeiq6Bw\nCCdlycddvZJk0MS/m6xy0NWBsdt5QAgxsQXWO6YTFvYMibYRrSSP8oB1XcR11QQSLxVT+DpHIDUT\n7Y+gFJVYgeOo+5szbNeJTz5t8AKPHybu3nU228RiVbhcw7MzuFgncoh9Q+CpPaROKv9jcC5zCU90\ndHJJAT34JErdJj03TQIqFh0JomIsSd8YC32Mou9SD/ZisWajd1Zt/3L959VqLpuamPQreyyFEpW7\nsZxB+4bz9GliNYO0LPEORFgrz/L4wDlcCe774Cm8cc84WqjYqNIwGwvIxghFsaJOLtkxEqVIa2Pb\nG9kTNE5j0lBOZhwtUIQ1qHRXrZLALSAhc8pg0EVLLTd8MLLpMPmxscMXHK+EwdXEOSX4r0QoMLoM\nH3kfvvhE1A03N6Ty6mLPRR6LGYy2p5sA0UW07Lu2WiRE9HAMcUiaUVCA4+xGeLaBzU4bte9F63JT\nH6T1Rif5vlRQme1UsdtRn1HFbBSW6ntO2YZrOBwmjLsmsby4PCAX7ldKGCuchw9h7J3LC2Ozc24e\nGT94z5h1sDoqnD1LfPRR4eKysNnKex5G6SKAM2u1kSzu+zhoDSl7bQxDYVeFalx6vN7KW24gaF77\n1jEV3/VYkyl0FcYMEBBJBkLgvEJSTbLgcF7Pua1FH7XoqETrqmE0OqQp0iZCi1rJqOXMmb9ZePgo\nMW9hPo9ElRmpyeCm/nQLdVW5dcu4cVDwIs90GD2UySLSDSzf0r7wZLuFXBLFQvYzOUNfyJEDaDvo\n+6LoypzUpImJQOzPUhJj1dKOAxV8SuJbej4v6ZUwuJUjV73GdOXUFCQTdJswtha03ZzFb63GUh0C\n6nNkOtMUOuqGtolJSSiHwfPsU4sfi06xwoGqAIfq7i/XxtHBvoxz3jnnPZSeaNlTYpIAi0ql8JpK\neAA4EuDwyrS4xm6uE56HeM6N6T4M2Ugec+dKXlxewBu3Yb117ryReP8jtULZ7hT+zRcK4z99YFxe\nGOtLfcRujHlufNKpGEuZ8Nq6UUqJ2vzRJoU2zc8eb01XujQ0IYgzxrk+eTdW37diVUQ5eFRNmXp1\nPa8n9KqPWm1pSRWcc9/DdrshKdOP9ls302HYJJjdhSenxjfuO2NyljNwMyVDR1fhwy7xrXeUvJqM\n3KbCjur+of0kozuMxnYnJcAUc7OL4piKNROSjMOoqCXnRB7lfBUMyt4RcjMVNF1p3VMcSPuCqS87\nXg2Di5IWmL6sp71XWzztQ1HkFTVUEyVvJV1Z3LXs0qzyYOtffOLWVsoWIU48Im6soQxmlQmskEUV\nuOnHfSKsvl9q9O96B8tZClxZOeoScXQJOMFS7ZG1h0OuqamdRjV6mKZUXk88nuH8KRysjLfuKmRf\nLZ3lfKSMxiefOrdPYL0RrnexhiEXhfEYnqNfVpEQURWIByb63xgFDMPgE01wyDbNvyH4Z0QFMJaM\n0aFjf3CPoY/gaP48SR9Zi1KHdZ7gEhnvl13Q8qLHvp+ZchWRtcQw5jOn64y+V6NVCswOlLCiKXhr\nrNcNs2UhF2O1cg4OjftvGg8fOL//W85qAbttoh/KxDhSL8KY2+BB77IO8Ka5wqcfVbVYr22/Z3Wd\nTQdc2evVQy6RQzKXqNSuTxKXsoC/whj/7Hu4VG6q0Zqy+eJt/ojWgNcTZo/l1i6edVSu7vSQLLk8\nVY+ES3i9VU5ORTeBSWWTZ5uIkF9try826ntVci1NFP1r0RrPLgKWcNXce3jH0juLIo0QL85ePatq\ncK+xh0vFOfd4fB+H1yYbbsbdm87JQeB+pkX/z9417t5JrNfG2blI8uvLwtMz5/wiKssgumnoQBty\nhPyRrPLQw5V3G8Y3wv5+JBKYSrYEZIubfi8OfXipTQin5DjEiUSbo2imerJVKQxCtvEaQ0UQXn+p\n7ADtp9oSK5lgg24h9a/dCPmZcbhQZdnd286jh87bK2O7Eyvh1iHMG+Pf/pMNJQ88+FSQkI2w6cEb\n8e0FI7goaJgMmNeiBu3dPvqVmRVy2JA2kqYTLj+qG3Bfatl/KMyFF9wm2YNxhNTWpJn49s8zXhmD\nWxXWSxItqFokr9niK6F49WCrQTYL0Nz27yWjG1nohomOImxVx7GZ1OXNpCC092xtKtNMBrve2fTG\n8YrJ4PejWvI0yVnMjfO1yhJnM3EBpXoUOFXAHnULVobCnpx/PYe7KrIGU8LTcOYzY45Upk4OpMaU\nGmO9FWH+fA2fPYJ7bzg3jo3NFt77oeQSt70kGIc+ynNdEVAOvNWs0r6ccSxRCCNKj0N0AVb59pS8\no0YkAVvFYQwRoUxaDCFyA5Ohzm6TvGbOhDiRMN/nxfp+FoYFzatGau4SmDer8IBEapYLmEVi+fzS\nOFw4b951zs8Tb72pvUSb+KU/DicnI7mocev3NkZb4EZT2G0TG1cZ9hgVpurS4VOiZoyip+Li005d\nKdwoAfcZCZLYJGIUyesdo+qwRKELQwii58qyqdS359uxr5TBnTiMEwBWM6HVV9QjOZrS1Y0A+6Vd\nM5+OMpmgG9g0gQ9TeZgpGlHqSZk4sQt0jUd7bmUu12t9QtdEBZHvs9RNEq3pYAnP1jplu3mtZnJI\n0Yk09l/BamQzYYjXdVS6XtfIU+xaWLY+qf63kdC8vHTmnVTYdr2ig22vzg2rpfPBx+Lp5rI3Ytsr\nhQyODHuFGnKuPelifXjUxiOGSdfa5FEnswmzrxjk1OUXo9SMqukLjeEB+2Ssr9AR69V57XF2fYe8\nRd2XZET7cgQzRIQ5jrCch6GywrwzvIG+JA4S/MIfgZs3nIdPjO0F3Lwr/no5N27ebPi5PzDy/ntw\ncWZTz7MSdLyp5N8iiA1GkrtgpFzErY8JwSOPYkCK6tOU9Occ1lS/N6RUooJOmPIQyXPgerAUpu9S\nPdPp6wlCkBZIqO6bPIhKVIA9H3OiXnnZL/74N8VJTKqesvDglPYec02apGrgi7HtK4anzVjxyPoa\nCWuLenKwNNY9XG5iocXi66vWA+L+lsB/r9iPaznMhPVZtMZuUs1aA0X0rH4wNr2xWgp7u1g7Tduw\nG0TvuVzDwbHz6AHcuOFKZuydGnBJbBpVja3SefS34p+PIqTDWldXrCvb5wHM9txuTPX0XTQ0HCsW\nHSyHmODgUkeLcALzv8bzWkcRvEmToG1tKt92N4ZBAvNNdMCuxSJNEx2TF4n1hTE3uHlYuGwa+lGt\ntNIKjm4UMtC9r8+oCcuaxFYHjjIl0sKPCS9VidGrioMtgpwsOWMkznQ4V/sRe78p4IqCS9gH6p7/\nCpLcr4TBrV5ssb3Xoz/4pCs6Ne+rSZFi0eDt819eBGjqiyLMC+zFgMCbzAJPjZAkeSRDgJz2Slb9\n6DQteEi1VR5mbdtSPWwzo0sqPXWD840ElufzULVPaq9dUChahc9flm7q1zUqXS+lfcidXbBLduP0\nAk6OZBVlYJWxHsJD3Q5gA1gjSGKxMs6eyfA2UR6ci6qQtJFqgYOSYTkoXHXOExK//lGMtfj+wN/j\nsftECsR8BZvGi09RixGwQ3B/6/e8tsPBLNE0JZKDMmA+7CPuWctE10xJDkybgo6FHJL79+DxI+Oz\nh4k/+AuwfMdlkbbADg5vGIul056HdkZ40NK22LuaFUIsRXKoY7ZIoFm1lZEwl6EeszNrbNJCwapI\nUcAIRbBmniZRTJaS9nj/lx1fSC3s6xhTR94rWrfAXuyF2tZaj9c2JxNua1Bsny2dfmJIhGQvSF0p\nZzWkVCgS2XPXxF1uJYLtRRhUfb+9kd0L6xRVNUyh6mqhC1hv92T4UsI7C289pRBeucYjEvlKVAZk\n40VGcb1BesW2F4C/XKup5BjcyHlnE9fy4hz6rXDytoPdIG70dicGSdtpguq0D6ORUgpOZl0rmrvU\n1FJc21/n9K9Ru4nUA2O7kye+7iUV6JMRrx6xTeviKmvmOg6nZvbD889qfZ4RPW7WQdepkqxi6toY\nwvBTguWi0O8Koztv3k+M20TJCZs7LPXa+ZFxcLIvuU9cPSj1+U3Mp7pu+ATT1XkbRw+OsA6HCnPl\n0aVEF+9XBZRSYzStDtraG89NlL+c9/bny45XwsOFvZcL1SCyB+Ox6Yv+f+y9689tWXbe9RtzrrX2\n5b2cc+rUrauq7XZwY7ljDJhOYikQCSIkYhD2B0ABBFZkyV9sKRAkMPwFhA8EIiEkCyPZEYJYgBQr\nNgpWfAEhYtF2jB2747htp+/VdTvnvNe991pzDj48Y669q+2mLqfOpV+fqTr17ne9+7L2mmuOOcYz\nnvEMm5+7f21KNodwKb0b126auik4dKq91xtJ3Nzm922Y8DjtsbyhU5Z0ObSJsVYbMevqanLjvdHi\nXwzCHa92Hi3A9SHFmy6EMKIbXgE6M0KoSJC9iJtZJmMsxvFKWJ+bYKTrnfqdVXfqToJGOcHREVxf\nGYw+J8y6QaT13CKYuP5dAuuM7c7nz+5a1BLXPLFnwBRvffF0/7VqwRrMmcud6H/D4HTVuB7lRVET\nXXaR4109uVqX4nqDMQVDOGoXXuws5t/kSCNa6frWVUFz34fedNfJ0RlL9P0bnd22cv1W5vi0QlF1\nlxdVfUkLQx9ci6CLguZ2GnUvOXup1hKMlFKVE8CNukTdYEZX+yyXqJWKcawlf6TNbJJ7xGzWePGA\nktJDYoBPj8GtLoETExDWnBFNVhLmSp1FRGbUgXD9ww7WgzRU83caZzIBu0niw51Fx4VgK3gAs6mV\naBbVVDd5vvZ5OWvHTPF6m5N22gpyMrzdIDhDhi0y2iDtBgveppV9ddJNH7U6I7RJYrt1bh0z62F0\nOW78Fh4WmzHYnCUYdPbAWa9loC/OmeesBNfWA3tryZuc9uGniPKxqbdS3MB6S1URS7tf2oZdkayn\nEqgqgmgRTO7qXGk2uUX33sZ0oQHMj/syP54R37GJ0TS65tiEf+J7dxV6EGMjwVidHJSrqcphud4a\nft95+eXE+rTCpeHnUO9L2/bqXN7oNNo+j+OikI3mlAw+uQzkQUbLiSjYFMlIr5qoQnQs2x7b9/06\ndIvIM6hmbkA0QzD3KAH/8OOpMLjN5jTtAyC4uHsR8HYPCxNC4iTuc2sO90ZTOaCIRcjXgBo3J6fE\nVCoe0nuW95VlCSXBxskZetGKxkY/IjKkgSVbVmi7udZun2IdT4Ed7naS+qOqQ2/XwVilF5CS6sCn\ng7Dsxg6PCAXN4xQMgtVCmHfko4S/msL4MlXGWLgK2xPTCKu1cXZunB6r++75BUxjmj3RnFql4QE2\nbns6l+6zFJjeXj2M1NK0NmN67gFrEDSwKIqo4Ym3UNMDlpqC96eCjncv/hs3GvxSgGrsNnB1Kein\nMxnSZW+sV8a0EoMnm3DV7WRQnfMzsQ9yli5GKs70pmCh8UI6BxdXcP+BtKilb63NL4c2Sa4Nx1ck\nlLI6S0xxfOhVhGHxuMv7zRb8QAc7Ik0zuuSMsS73cEMIU6UDtsKHHE+FwQVmnlvjTDb6bTKLSi0J\nOxt77/HwtvaqHbSxB7x5KqVxdeNSWdvlwiNJe6/HLRZZeNHuKgftZqwv6C4OvRvTziPzuhc2SQmG\nZFH4YIE9t43CGBahyVCYqUU3eTQ5w5pCN3hU2/nlsMf/sizgfJ1Kdckk5kTft+vn9J1zf2fUB87J\nkc1td3IFqzWw8kb3Eg7vtRWh7GGq4qEkNapgYt2H2pVFciW0VUuFRafoaTvJmKit936LrIfatyYn\noJ3vTR4WeiebnbEZYXUiiK4UFSN0HWymyubCeOsdozfUXLV37h7B5ZF+v3VLOY033oSTfwh37qhx\n5PVGfOzzcxjjs7p+j7fX4vOay1ke8yz/iNZh3FbS68XkbNUa8puCPiwrKpXS3572JwjDZvpnSklr\nPQnQ+LDjqTG4DecBeZM6scMson7m2Inco6w3uHKtWWBu2pjBImgYq1p9aJGn1IRlWmmwBKRLZJiX\nETY0g5sD2ijIWAOzkW3Lqss6lyk+sEvCp1JuAEhTL4O+E7Y045s3fDTMGhcu1y0c9ySN0RZ6J4KY\nDjSvI272is/0o+O1yPPLEVJOHK+dB/ebbkZE8tb4zvumgfukmGhBBaPvRFtqBpi2kYf4SZ8bvps0\nr0GBMTdqrfO9oLWpyMfr/v67yZFLnZztRrmToyMJirdmql1ODFli4f2gtlTTBOvBWC+lg3FZnLsn\ncHykhW058dnflbOy20LqVWxUXfztlsCkOVQZfNK17nNUhJmTO4uy7GhSaaHRmwCizx16nxpRi6N5\nmzwEciIfU79uLlu0/TDjfbMUzCyb2d83s78dv3+bmf2KmX3OzP6mmQ1xfBG/fy7+/on38/7zrgKz\ntm2NBIbRlL08Kke0i42TsxtbxY+ES0TRtVh8Ft14D0IRoi66RLKtEhUqwqEWHUEbat1EjeXgXO0q\nU7RwwQUTLAb9a622aTdFZN7VFjo8Y/aZT1BCYaoe2O6TG49yXlvoXYox7qTY1C9C6Ad5l9NEqLxJ\n6cvZ857BpYEwCc/b7pQ0WS6cy+tIbBosl8JRS1GM31otQfOYoRF3t6NEpPugAGbUX23ZO4tBSldD\nlje9L/kWVDEWid+0qrbD5G4j5su7evjQ82HHI51X1z2fe/GnuyTD1RS63EXRq66Ou1QlzLzCuFPy\ncbEwTk88aHzOl77gdMvKCEwD/P6X4eycmcudTZq4Srq1vIkSdyk73QC5k3EfejVu7QeJi3fhWWPv\nZpDMCfZWGRgyg/MGHXz/VhDzUQQtH4QW9pdRj/s2/irw19z924F7wA/F8R8C7sXxvxbP+/8d+oL7\nL9qqd+Td7HeVlG1uXaMspHayFOo21V0tqwOmaV/ObM9CyCnq42HmelYQVhv+leqxFXIeL42hg5OV\nsavG9U6fO03S6dxuoqKsfY/awkwZZfVP21PD2nO8CorYd4t4YuORzus0tqy2wkxzGdndqGTXbpSm\n8Nml8eBCC6t5FeMkLHzfgyxoPRjbUVzd6lpQRytVKV2P2ih3k4mPWVpjRxlvZ4/7tQV0qNfhcU9R\n7YDOt8eGZ5ZK4NItrG30xeItU/7E8aJHNq8Wm1yf96FDKzKwHBWBoVFipk3Si7NcSvVt3MHtU6l+\nNTbIvevK+RncO6+MG+fFl5yL0fn8F+DqPMlBCu9WmgoW64dIahrZjEUPy6UzLLTOU1SNYqGn2xyz\nyPHIKWie7F5Dm3a8saOsQZMPNynvy+Ca2WvAvwr8d/G7Af8S8D/HU34S+IF4/P3xO/H3P2/23qfZ\n8LvG0/TwZFufstkYO2xGdUlt2Kr73p9IxMY0072UshHf1uYL17iZxYl25dqplU2VEVh2YKYmc9mc\n07WELLZjUxBTdjRnecmxVBnL3luvB14QdY/nHuqnPqnxqOfVICrLQjQotcQZbEbjcmNsohtD10kk\nuomQTGUfDchb0i5aqhZFv4CrjT4+ZxWnDDnUwyyC+cB9G7NlNxqLjoB1dD670ubkkONpqkKq+xLu\n1lq9VGYKWyvxbRzcaTKoe/7ukxqPfF7tAL4Jbmy7hgmj7xVltu7IY3H6QY7RxaWMdZecbUhvbnew\nXsO9+wZULrfOtDXunsJzL8EffFWSjmPRBrzbQRcQhhApwYJ9L/hPlW9x76UGKaSIrPbcaVUM+pwU\nB92fZdJ75rxP1Lfv/bDj/Xq4/xXwH7M/r7vAffdWf8WXgFfj8avAFwHi7w/i+e8aZvbDZvYZM/vM\nVMIrrHsOZKtScQsvoqhTbq3CYJvYjK5FgOXNqLYv165QWGERoltiLZGy6vy7LDxORGkR8JcLTRo0\njVWj1Qt3nXF+pYVlaLHPtfRh7Fu1kaE6/zlpU5m/YzPGT3A80nkt8X1VAbb3BMEYi7McnMWgMLTr\nFBJuJmczeuC7wuArNZgCPiu5rRei9VxcaqEr9NRG2od+hTfs3+VRW/J3eSktilTHgPn8Aahh7Wu0\nSS/1INScV+f+sea+SQLe7HlVI8U9Nt7Cb9ObQHWmUtmNEifvOkV821He73qlQpfNTi1wpiKBm3fO\nlMAaBudsI0O4yMbHX3PeOoOLy6SWOeifRIqCU5sqXZZDlLIx9OLBdyGfarRWSfP3iaS72vaUIqer\nhg0Sf3tv1HVb+ENXir3n683sXwPecPdffcjPetdw9x9390+7+6dzsAZq7JQtf9GEo6cqPYJkFnXt\nghUaoWDfcsdn3lzw4OXZeni5vmc1HCbkzFoSzbi8bka4GfCYqEMakTu5V/XR3MU1DMpUgtpU9X6q\nJNuXGU5hONw50Pl9/OOxzKtp7sYpcM1IRGxHlX7OHgThHSVtQtOka7edWnWgyO2lzDssuHO0Eq56\n/wGArnXOraDF35VEmzzKTQNjB0ETyWA7xps6oXWx/5jWL6s27xbps7aS8LjzAteVfSv1XTfaYx2P\nY15XizZfB8VDLjimHMA/liB1HvxqaV70WVDdbgTm6GAfJSZTcm01OG+fiRLWm/Pii84Xv+ZstsbV\ntZoBTJPNc92kFY06C1vhRpkSu41gjcPEl3sNp6dCijU5OwQAac4heUAZiYcXJXq/bdL/dTP7PmAJ\nnAL/NXDbzLrYFV8DvhzP/zLwceBLZtYBt4C33+tDpHeg7L7PbVci+4gx5H2iovEkRc+JPaMdRyFq\nih13f+83SKHtynusxiN0HIvPXk+tqiop7EOoZtizKZl2EYYkBeQwuZ5caisT9hlnmkJwYw5Ffe+5\nP6HxWOZV7UpiMbo209VCJNYxJC4tEoweCYxx0jU0LKhzRsbJWax3MQf0vndOlXg8u4Dbp3pdznvD\n6t6gG1WL1cDYxWTYb7o0UfPqB1oIwR9G/xvjZnDkfTViYj2IXGpggU8QwX0s8wr7aE6Po5DAYNpF\nkjq0hHeTopd2Pbdbp4/y7b7fz3+j3w2DmDzDQptu9USmcnQirP/Obck0ir0SrIKIbhs+e3WZuN44\nlp1u6Wx3xrRJmEkPuUZi1ZOird2o/MLqyCFrY20aueZiRymKkuH+sOM9PVx3/0/d/TV3/wTwF4Ff\ncPd/F/hF4N+Ip/0g8Lfi8c/E78Tff8HfBylRNK+G5cpg7aJKaBaVbjShIjX53WjRpoZQEovb3P7w\nDd+y3g3jSUmdJGrVpG5H8LT3uizcYS04uUpNi3cMCISkuvpSootANLMbS53xv4b9NaX6po5vDtQn\nh/U9jnlt4WayaD9TFTm0RGUKhZhSYVsqY9VCvNy2uvUWKcDorVZe169G9wavsBrUWfniQnBP7g5y\nAF7ZTbDstUmmbDR0CItNMbWNuoXJDflv5ymPu9Z9u6Y5CmNPPSM0c5sRfhLjca3Xw+is6UtrF4pt\nKEMpCTxJVGaUgtd2G62lXEZtnFoiWUZ6GsNYG6wGZz0IM9/ujFvHzmZyLi9C2H+OJBPTzri40MZ7\n78ywrrI6qnR9NAvNleXKGZawWsNyXenWkHoVKB0dyzCfX0ZEFTdJi5K8wQwPSeN8GEjiPwH+ipl9\nDmE+PxHHfwK4G8f/CvBj7/cNjaazqS9sBAeWuOEddlVtNchO18sr3Y5aJHoTn6kc7V97vSXDMvpp\nyipvRnkkywHWQ5SYEqC/xwYghyzk//YsiT5pU6hhGKb5+UkSqi5vG1fn4HESIN886nJw0z5F4yOf\n16noOuzbkQd8QGU7tcjCGHJi2cHFBq53ibHWufdbg5ZqrTPdDBqbxek6ZxsloO2imin5pc8OmCG8\nz7Y5Ty4Vq7AVmvtYVFFkqNAz4A4zw7K928fxvScd+/qNn9fGQMAP9EBcDIKUPZwZrdOU1C1ZrBKt\nwwYvjdUV/bVWVJG0qp5Y9Myl+nmAy+vE88/B22dQSw76qHF2YWwrsgkL6IYaEKDv75tgIACxcVro\ndHisVW3a/dI5u4Crc5Mgz8Qs45ry13Wg+RDjAxU+uPsvAb8Uj38f+NN/xHM2wL/5gc8kyMWtJFOc\n2X19ukdrk6kIB+qjjK+J0uwmKRHtS533mA2217WsEQpORVjdom+tdkIDNzKYHpUl1ojQoxCiUrVL\nFWd+j91kUWPtQVtCHX2D8tLhjLGLT9FUsTZ2xVOwMB/VvM4JlaAIKXKxmf+otjgSCEq257d2PZxd\nwp1jPdc8pDs7o5gqywjqlhM4vcFiKWO9GvZCRdud1McaD+xQN8PDKUs5ohgEfcgDf/fctM4dAK1k\nXNzwPZtiTvR6cwCe7OQ+svUaG09T76oRrZXaWAIN01ZX3BZx7nawGAo5W7CDIjdSjSG6IddqXG8h\npYIjEairC0l29n3l6tq4dQu+/Lrz0ovq2twP2rwxQUKlyMMeJzk5hLGswSxyl1fuKDcwY/bhRHVH\net3VRt8nLSup0z2VHzWk8LhGjSRTS0TU0nRNmaGGGkpTqe2uOMmqqD8ZNmMITrQFyQztymDL5Zzb\ngE5q2wAAIABJREFUXHfzjhWlu/pvxvkOuZUtNI51G10jRJrfTAct0lOCxGxM3Z0xMt3Vw1B7e3/j\n4vqxX+rHNkS/25fapoBrGs95LK0MU512d5PC9OXCuRorlxsLMW9TKxRrm7CFwdY1LGHlFj2hjbv/\nHPEv9xBTaRguMvg5Ko0ac6TdD8yMA83dOAa0EAnQmVo0G1h/18/rJ4fNP54RiWgzm+GFxhQpgdc0\naHCc4PIqqHsLY9oxswvkmFjgprZnJUX/v120TVanbCf3lc3opKXx1beMzSRoz5tHHeW50yhIw2kJ\nPWaveipK4I2jzYyH9hyQd94nZ7lWQcVYjO3GSCS++tbDubhPRWmvH0wYMAvViF6lYzUSWinSZCmF\nxxEk9j5Ba6S6i6qxJs1Y6j5ZUqqUwhr4PdWG/zYvK4xk0IhwSCTxbMP45pT2N1gkW8aiLLhF6FIa\nY4LQgG0kFNOEY8Ip37l68p7QoxzSsGjeo2J3lXGHWptJ5D2eofANOFrDgyttpOtFvBfBTvHGHEHz\n41F2XeUtn18ZRyuby7KZjaHN71NKSAh2mqtS2mbvM7TQtBl2xbBcZ8PcWrwc8nfbZqpKN554BeGj\nHt4uUngpzWPUNbaZ5tn16uoBieWq9RzzVtQlb9hgKBGtZBWwiBoaJfyTChjcgKrOzsPg3HsAm6vE\nMMxntV+/k4lvkiIKDuNPrMm2UdZI5nKQUKcxXBqHN4fnPMFv/v7DXbenwuC20UJ47TjN9WdOlOw9\nUp9pGlKQj/JLQh4u5NRarJAz6vpZ955pw1dbjX5THGsZ7Km0+mvhrxWFGV4N6xrjQOe17OFqI1EW\nCehYtAGy8Ib3lLLGEp5G53o0Lnc319iCvq+7eky18L82A+nKGHvMQU7KBncm9oIvnbOroJAlQqBk\nT9GpLvyvS/pdSbBKxTi7lEFcL3UWsIeBtpPv+1Q1OAD9LeWmbKZ8wTgZfVAE/SAZ27wlr3uopDEb\ntsW5ydPavmfzcMz2YlNt/eD687gTdnt6IgNdQie43RNNTW/cOWUMbnuqsw5tYx1Mgf/O0qjVOT2B\nr75eWa4iSRd7QMuxmMVmXERDm0JmtVUHTpPmFxOLYqaXxnfsctiTWknZ+OoD48tv1z90PT7IeCoM\nbktMtUoGP6gIaYLGXZRnSoZP9A+i3LIB7l3smvJKnZTTbLib+o/j++qS8DwtPM9W2ifjKDV5hRw+\nG8oSazRF+NjEclZLuGp4YXL6qmx4Kx9uWqpd38qKjXcub7Jv2xYms5Fqi9HYMwMU/ge0YFJX89gA\nV73I8ZcbtdguceFTeCElNBek4BVwQ5HI9bB0rjetXbYM+2ZH4PnylsycXRW/t7Eh0qiCl1YwMfQe\nUYwweEeva4k8FcuIe9q8q8vp4MvfwLHZQsQb8fMgmeQHov4Gu9FYRiPJUhWxuJvE6EdVBlo2rrfO\nyUlIp1Zjs2Vuc2MJLi91Twy9zQ5Mzs7t28brbxh3X6hylEZFNoL1LPRPdL5l1DpskYubk0OBLBlz\niW8gSljdJwfdjd/8/RCneojxVBjcptaUYcY/m3svMrQddPJlXsFNFHiOFL4OmtAvvufflpY023uz\nfd632NFrZbE94kcLTKnVa7fkWMExT3jwa/vYJHZVXXzdA0eqwiEDnoaiG+1qB1fTk5Y4ebRDjI59\nRWBso3PkUYm59r084l6nQPO2XvAuL7ei3lIZsKQwr2kWEwZ7LOrwMPRwtVX4v6uSA+yz8ONkqmoy\noKaGqwsauthJlrHW6Lgcb9/myt2o095ryp1UsWbJR/dov30zZ/d6B5tNYrl8t/Xx8GrxSDBHSxq1\ntNEaGiPK0++KBkuB8yt48U6VfsaoyGXcias9FRW1bHeKJEUdc9ZrJcw8w9k94+RWK5qBOjVcPslG\nRN5gimO5q3HOopf5zNGWDeh7dI9OStx+5R344lvOw67YpyJp5sD5hn0TvkYDKkEjMWaKR7OW0k9w\nihtWxbFsSY65hUpqFKC9ETZCrDi84blWKHbBBk2ZNb0Em3dE2QOf32fG9By8qqTUXXzcqbaGdrGj\noizpdufsRuftq3dDDDdxOHC1s3ljxB2vdb7GDVdtEUjL9pcINycXXajrnLfOnAeXjYYXr6+ReZ5s\nToa1TboUYX999Nha9Zo7QQppVpxqGzBVuh3ZnD47mwkCqAi8NpgPVQt3swVMHM6hj/umwkWIj9/c\nWRW081u/F8aJOhcZ6VrJgE6Ts9u0300VaKPK3He7SuuIPE2xIW4UmaQso1qr2D/jJMGozVbRxnJZ\nWa0r1sH5uUEx7txxLib4yldgs4my+urBSoqeZmODEpJ0GBq0Nznj5DOEIbhK0XINoLdOiV/73RCh\nf8iZfSo8XICznbPqjL6XO9lUm1KrGjLmNjwtA1qL0mIp+lQ1TNaJK1f3hlXeqxSKakyGdptI1zgI\nPGhddlvvrJDxi3OIHIwqW6pHdlWSjs0P2u4Usja+bXXwMSqtKjy4hs3EHHrd5HExwjLr+szcTYhk\nqEM1jKTilqw5SV9nkBOCFDDnwQVchRGttUE2zqqXJmvzPq9rw/Pb+2hhb0pL4O3FahqEJMhDG+Yu\n5muKluj1IBStrvsyWfTvKk4tzlUU4txoaxvjNz5f+ZYXjRfvhkNkTQvF8QnMEsMgRbftxumPbK7c\ncmc2cKVC2QmGSLHILq/CG94FdBM9Bjdb1IQg4L+xOuVSNMLbR/B2Md5827l9muT5xpptnnYZjdT5\nDDN5YMG1RFGM7fHCMjldNvps/IPPV15/4B/JvD41Brc43N/CHTNybiW2CbzOWe3m6UJ4wyjkSxhS\nL9fBpLhGhpjmmTbvOdgL1ef3S9iM5zb4IFvQkcId88CgpK9Qw4AAIWDjjUZW9lziFlLX0rLr0lO9\nv2mchZs/isP5Dm4FVgf7BddoOE2ruE1WARUo2J5NkHtn6J3VQkUMyeTVdllaCOcbKYydrmQgp1ER\nR8o+96Qbg8bVtfbY7VxinqYCV6Ogir7TXHa9znMbnT/aubXopkmJTgWu6h+feb0cnV/7R4k/992J\noyNRxATDiZlgeBQ4qKX95hrwEP+PXbdMgKlV0sna8GLBGAlRm6hOrOh9vQT0aIqCcjY2m9amyjga\ntBG/8aZzsjbWR9qAz87VZSV3sFxrPYuf71hV9OUVqu2TstkSGXhwYfz9P6h8VPuovY8qvkc+zOwc\n+J0nfR5PaDwPHLn7C0/6RD7q8Wxen83rDRwPNa9Pi4f7O+7+6Sd9Ek9imNlnou79Jo5n83ozx7N5\n/ZDjqUiaPRvPxrPxbPxxGM8M7rPxbDwbz8ZjGk+Lwf3xJ30CT3Dc5O9+k7/be42b/N1v8nd7r/FQ\n3/2pSJo9G8/Gs/Fs/HEYT4uH+2w8G8/Gs3HjxzOD+2w8G8/Gs/GYxhM3uGb2r5jZ75jZ58zsfXcR\n+GYZZvaPzew3zezXzewzcew5M/t5M/vd+HknjpuZ/fW4Fr9hZt/zZM/+w49n8/psXr8ZxyOfV2+6\nBE/gH5I1+D3gTwAD8P8Cn3qS5/QIvuM/Bp7/umP/BfBj8fjHgL8aj78P+N9QUcv3Ar/ypM//2bw+\nm9dn8/rRzesj8XA/wC74p4HPufvvu/sO+J+A738U5/SUje8HfjIe/yTwAwfHf8o1/h7qtPqxJ3GC\n32i8z7l9Nq/P5vUmjY9sXj9yg2tmGfhvgL8AfAr4t83sU9/g6a8CXzz4/Utx7CYNB/53M/tVM/vh\nOPaSu381Hr8OvBSPn+rr8QHm9qn+Hh/ReDavT9n3+IjGI53XR1HaO++CAGbWdsHffgSf9c0w/nl3\n/7KZvQj8vJn9w8M/urtbaxP79I9nc7sfz+b1Zo5HOq+PwuD+UVb/z3z9k2L3+A+BV8zsh4YOVguB\nIUdL+5FvJM3z9Yfdm64ts0jtoephe9iOuSQu3yXbCCF+DRzKPR2+5o86CQ+BK0/vfj4H53P3Njz/\nnPH87fnYLwN0nXnZNxpsj74MfPzgk16LY0/LeM+5/fp5PVolnjtVE6QXbqUfmRvfQii7sdcsPvhb\n+rrf9c/2L2R/jWeN0vk1tn/Owc3ghMbpgT4yzF12AKlRWSL6rcXrQmqTFNrHwGvPJ77lxY6PP5/1\nd/yXqye6LnlrI8MNntflAk6O9bdbp/YjdjCPTXHrcP0koonou95z/xxDymBtXba/t/VN0y2O546j\nBOXnz4jXd2HRvKmM+f592uvbvLbbpMZrc4aXX4JXPma88nJ8n8Qvmxn9YD6N86l/6Hl9YuI17v7j\nZvbfA/8oGaff8fGQZQzZw7aJ6JroCiVTex1dPQlZ16rWGYtBr+87aVu215pVspn+dXrfWoztqLbq\nQ1aHgEUvqb/dzui6g0VpkgKcJo8GgpqYatJqnSbjwT/Vs3uuY3wO1m86Z5+E9ddgOnFW55ApfOuv\nTqQkHdV+MH7994z/81dCjNv9rbgsPwP8aHgYfwZ4cBDKfFOMw3ntOjv9l//smkUaWRkMDj1OB/Ru\npFJZdok+QW8S/+6ymnwuOuhcOrrZ9douJfpuv4ASul/UNidhqaoVk6VQ5m/tmCShmLsElplGZ3c1\nwgA1w8YBEsng4qJSM9DB5z7pXKyd+3eMkzfgy/+E88pXDR+hYBxfGq9+AaZklGQUjF/4rcTP/1/3\nmvTkjZzXlDj9nu9OrBZqGdWZdGb78FqWSzXzTMByMBZ9SJyOiZwlmdkPkkg96mG51DEjsbmGo7Wk\nFFO0Q6oTtJZYrWXadme89SZ8/OPOsNBavN7C7VMRAbbRLSJnGdTLK5Pk6wBf+FZjd2ScncLpO/AH\nLxrPvWH0VrmInoqf+q2RfjCWvbHsnf/7Hwz8jz+9e+j1+igM7vu2+u4+mdmP9h0/m5O/e0eL7gtN\nQBq0Q5VJqv3SodWu2S1gOzm9Q3Yj9eoG2lkIhXtrUhgLVE3PqJMmoO9hs9PnLZZqvdL6nmGaKDq1\ny/botdaF21WTc/o7BVtWLNqA3/msDAu9kd1J+Ny6w6u8rO/6NuO3P2e8+WYFaJP0cyjz+TngCvhL\nH+nMPPx4X3Pb5nW5sJ9d5krnRh1bV0CbM7bmNgvSpt7mDruWXJ0CzMk1YbHRWqq4SU81hWZpwtUF\nolRy50yutkkpdHXtwNOt7vQUcu/0p5mr8wnrJDxda5HQ+Tpx/6rQdfCtn9f9M+XErlS+7QuZoagF\nTMngXuM77EXT/9Q/mfj1zw68+fYO95s5r4sFP7tcyhExi9bmRY7Keqk+ZR1ab32S3nQ/JGonreL1\nQm2ocoLrK4MNHB2jbhvRqkia2Fq70Bq67j3R4x6ut3B5LoOesro0bDfychXFGsulOoGcPYBuJZbA\nt3wVfCHjvRudk8/qHug6aSa7R89E1JJnLPDPfafzy69mvvTFCR5ivT4Kg/v/AJ80s29Dk/YXgX/n\nGz3Z3X9uvbS5VXnsIICMUwlr28KNLstAtrYeCV2c9QJwp1Tnege9QbeEIcKYlNTdNWejmrM22BIX\n15yjFex2gCf6voljsw9lXAZ1Dmual5Ucpkq/leAypi4BKVfyzuawyTso8epphNXC+Z7vNH7+baNW\nL3EtHPiRj3Q2PtrxvufW3X/uuVuZzER2yBhlqlqgOMmdPiXMFcJbrVg2ku8XmcWCzjkdtENSQ0d1\nW6hMW0h9xnJi2kzqj1YLfWewTLB4Vze86ODhMgYDjMDQJXZTVWNLkyHAnWEMzzhVugpW0UY/OWV0\nijmeDcxn52DVjXzvP33E3/7FHa52ezduXk9PtaGl6KAyLAS19L06HHfJqFN0027wQIU+1T3kgF5z\ntFJXh8210UVTWGITzFni4l0XTR7dSQW6Ts7Wyy84n/+CceskscjQZWeaoOvVAy059NmYcLpO79/1\nRtfD9qqyHdXJJZ07R8dgk5GK0w0+9yAso3NxAX0u/Pl/oeNv/M1CLR9+vX7kLAV3n4AfBf4O8Fng\np939t97v6y1AsjIJLmi7aJeNZTQBbIr/DW/tOqczZ9HB8RLuHBuLBVzuYLvRzdHl2CVjJ+s7ixba\n8XuOyZ9cE9M5fecMvZoE5pxksC3+Rajapdgsop1LXIT5h7vPXYndLXpjqVvtt7/iPHfH/sjr8DSO\nDzq3hktNP/oMZVP/OZ/UPrtMPj8nRWPIHuirIohMeLJ44IJGskRCHu1243SLxGLlrFaF01Pj5Mg4\nOsksjzuYoGyL3j8u8+QenVudNCRqqQw5segzORuWYbnKlCIIIyVTIxF3kle9V2CS2Zk99DqpnYSP\nlT/5icTdO8Mjno2PbnzweQWiHZWj9kKKWMCqUUd19KiTMe2M7bXmOqVoe0TrxKJj66VxfAwpmnEm\ngz422RxOVjY1El0tA25KMqbPPw9feV3dOdRoNKl9Vjhk203l4kw2IqXI1VR1i8g5MP6kppHuil6t\nNQndOG+9Dlejkdfwp7678Opr+aGu9SPBcN3955C7/b7HDCNEI8DcQe48ov9EieaD6cA+uUfjOTfM\nKl207zA0ccdL2IxweQ2nR0CKxYuMZd+plfo0abKG3qgLdQUdBk1aG62Nckt0paSWOaAduiVnLDUc\neg+JtO6k5q2FtMLqIcF3fTLxS29/6Ev92McHmVth7K7eb4G9dcnoY466zqij3MCx6Dolg9GNVCEN\nMsLZneQGqXk62pCHIZM6LXSmihcjdwnLRrHK+ihzfTHhycgLKF7BolUTHnkCY7wc6RcJTwlPTjdA\n2mYZWTOSO6kaVhO1VqwSvfbUVDL6QssDS8Y67/ieP7ni7/wf20c2Dx/1+KDzSjVSJ6dnd+1kN0qG\naeksB6PrtD6sg9ypT1mXjG7p5JTIJqMn4xowXYJF71xdQj4OqCY8VxBOLziKuJ+MO6fOxYWz2yWO\njiurrnJ1JYdrqmAZxtFYH8vGVKV/6Hs9Tj30W1oYC6ix5PWV8eABdGtjWMqgmzv/4p8b+Km/MX3o\n6/zES3vb8PjCDqRs9L0mQw5EJREXjEStiVqRx5ucZHXulTQnIMMTPVrAamVcXatnkh2mw93ok3O8\nEhQx7rTb7nb64BSLszPhUTkrSZCzFmu2ZkQUHpmFsaWFw/Fzvsw2L9LWbfY7Xnusl/nxj8kwT7NX\nmKoMZJ8Ti95YLo3lylguE32X6HLHMCSoxnQN43WE79H+eBor47USNf0QG3VxKJD7JBwdQQ6enNVJ\nZtpVLs9Hxl3zYCrTdaFcO11KDIsOK4nxquKu1y3XxjQKJkrR4TclGQvdEwppPDxc3KilUmvFS+HT\nn+yf4EV/tMOdcDyc3c5xM4YTOLkNp6fG0co5Xjqnx87JGk6WcHILrq7Uxrx5ks3jrNXk5MQl6wa4\nulRUm5Mw29wpId53MtR9wAY5w4vPw4P7ThcrrR+Mcac563PCUvQddKfLMtitp16fdI5lEoSZqiC/\ne/eM1a3MyZHPmTp3+N5/pn6jy/K+xtPSYgfYMwBSXOAyRnYTGdOcBXonk4fZmUe3XA2PhIt8SGG7\nlpyTHq4w7l85/cY0YVlGb+hgvTJOj2BX4fIKMGe3NaYUTQc7tcNulLIpMpm16Dz6bNQiz9k8mudV\neek0Tyo8+FpM3pGpaWGfH24Cn+ZhCEez1ku+6rpgiW6ATCWj0LK6Y52RrdAZdIOgg1yE05cpmklm\nWAzqymoovPfqpGUwE3wPQ7Swd3Wa2E4D223h6mxi2Skp1y+MLlfhjEOidIntVGEw+q6yS1DDNW/3\nnJgqNifjvArvKxRSjgQciXXePbHr/qiHoVC/TMJtFwtnSBahv8+OBmi9GkbfORzLe110Ae9VNQLN\ngzMMDTs3jpbGxVXl4ty4fUv473YDDLBcG7kotzNW3VtHS+OrkzaBajB0lR1qzd66LadkkbiOBGoR\nxFBjs/aIPKs7Z/eN1YmxHIQ5d72az263sLIbYnBTsujGKc/UqjKfFi3Q5+yV1bnrJ+i5JP0pxa4Z\nh+Rx4kyjIISXnrO5k6vFZxlqbf3OA1gPcOtEYdDmOrFeVTERKmy3ieXC5+xnqZUJIzlKmiRRWCyB\nTaJaqI+wUaq848NRC1D+8PEbN2rAMkUbHCnRDYKA6iRjlbI2tZR1rZKDuZNdxjVrLZC6RLJKZwWz\nCC+xuTtsSkkGECO3jsskqhl0laFPmHfUsx1miXELdaPk13CsDbbrjc3W6VbGamU8uKz06w5sohR9\nbpe0YHszRM00rDq5kwGx5BySrG/cMIXqdYoO18lI2cipYq55NNs7Ps0JWnSQV2ImdLltnh5rVs/u\nsyKJO7fh/ALu3Te6JCZSGR2ujNXa8UnOUpnAkrFcaHO0pPW5GpxrtznvYjA7QrUqVzMV4/ramIDl\nkZJym0vdsneOq2xRp+87Ta21+w0wuAoB9Tgp6cs0RsiRLbLD0Ts+ycvMLdtpSngJKZABdNciLDVa\nm2Osl2ptng2o8qC7pM8YesNO4WoD79wXbcWSducuA5286+trYzloYvtON1zfx84a6yu7Fn3uI5EW\n564vGh67G+OkkKg2dvZNHK4Embs2nj7JsJoXrETqpdNN3VsiI2/TknZQ61K0UJdxNfzdsFEyyqQo\np2F7pTqWlNzCBGdMRVFHrY7XidTBYmGkZaZS2W7g/HzEjzqsl8ft1Zimyu6yQG6L1UmdWBa1OtZB\nCsOf3bAq2ppXwhO4uSMyFgfceZfxC+6sxzo2wItBcNAXS+Pywjk/g9u3A+N3oxJhf2ygCefuLa1P\nJTBhs3Wur5WfafTRHFi6RYJVvpggpy7BNtqjE4nuGg7AGLmdNIhnm9yZdsbZGTz/kiKocUIhGhHZ\nlr3/8GHHU2FwabBqZJlKkQnqspGqws0u7Rda4+QZYhscnWrit+dQi2gknSkcmIrR90FBSi4al9t8\nY+Tg5SaDo6Xeb6rOO+8YQ6fX5s5ZLIzlUmHONCkJljrdbO7CdkuRMfYw7BbgPrQMuwxCGXV8GcUW\nN3ZE5jqbzdlprOFpVZtp0iJLBJc1IoMsoi016ILJjGw1aGSx3OeSJKdMBkkJrTrViHiMZEUJLdOx\nsTirdYd1TqoTE2IrrG3g3uVEsYrnxDRV8iJx627m7XsjR6d9bJaFEhtI+24lKGWlVcYkQQ03eYxb\no+t9pndRFc6nYe88jVMknjufy/lSJSJZ4517yo8kbE6CDVkebN/D8dpYrWVsca1tD6+1eqXLSU5V\nUdR6fWUcH4UiV9aJXVzB0ZG85ikiT9FHjaNj4bi645zzczi9I16vARON6aC5tAylPtxG+nQY3LYT\nBtWreniHcXz+ioEPZdoihNUann9BT3zj0mcqCY0RYMHvi4ozm+8QE+ZKjYSHvM1F55wMsByct99O\nHK+VHOsi7Dk+csZR7IfdKOJ93zuWjc1OBrSFUI2lYIG4m+uGGXfGchkY481el4AWnQyqY6UqiWE2\n85YdKKYKs2zaZK03+n5Btx5I4zW2Lfpb3hu2OlW6WACOjvc5kVKOMlxl6jqMLsmIpt6wXBlRYUSp\nRgX6BdzqO3YVrqeiSKUU8iKzOuooxVmsOzaXW9ygWyTh9CXqIB2sgCelxm8yoqA16vTBh225DUWd\nHhxd0bxSZ/RRdeYVxg3cumOc3AIvcHZP63XoBRFMpXJ1aWyujbPLCsW4cwJ37soQXk/CbYdOXnXO\nMOIcHxnjGB6uMdNLIUqAXUm54sZUE12qZAPv5DzdexvGKXFr3WhuipB9EgbsDn3bVB9iPBUG9wCS\nDcMbmVBXGNLCyObptCWWDNad0Y2VOgY9LAf1whRuNg7mIe4LwSCIMmJvvNmqaplkxqpz7tx23nmQ\nePmFiqEbrOuiiKKDuhAMYZbooqy4jMYwOLvJGPoA5OMzWylyoyU5zCHOTRwOuNco+YprXgEqechR\nNx+carRBBlWaYpm0PoEuYWkk7SaolZQS2ZQM2W2dbmGi9iWHlAK+qCQTdmtxnygtDf2QcYqIBbG5\nkxLVA09OzmpQgczmOrE5n1gdDdx7e2RYZXLXU8pETs5YtTj1Wfq+Gd233zy6NR98uMOw2DsXXY6I\nLtZmnSD3qMw+aJjuSjodrWG9AiuV5JrvoZNT48VZJFjdEty2XBgV54034f7njVvHxvMvOg/OxSY6\nuaX1MyRjvYLLInvRkmJDb+Ls0yAlPX/cGYtFokRe4Y03xLVdnzibCxnYyWF0bQap17027hJ1erh5\nfWpoYQo1rUVkczg+h4bh8SaPRRqh5uYCylniwTtKuDTOJ66k22KwYCwEFOGahJyYcb/Gm+06cQVF\nijbuHKvq5O23kxJvaOdeDEkk6yzDa2j3G5Jj2WcGQ05osl2fVaoz7QRReITP7vYNL8k3+2hJE3GQ\n9yW5+hmVgpYiyWnznOecQv8ikbxgZSJRIzkWWfIq4rsF48OQB1Vj8yxhwM3EqxxHyH3eJz283ReG\nm1HQAswYnRteKt3C6bqOer1jfZzZXI/krhVdTIy7hu8CVJX6VsdrxfzhkitP9QjstI1kjTbZvH1B\nSG3+IYRlEgzZ6Kpjo9ZuTvv1qvVvM8+2jGDFeOE2vPpxSEv44hd0r1xunO1GZcStmnMxiD9/UBMl\nuMDD2QEuLpxEpe/0GV/9CpQOPvYx5/aJY53Tr+DoBI7XsJuM++8Y9x8Y1zvYTA+3Xp8ag9s82Pl3\ni4z/gfcT8Iv4cnGRr6bK599yLkZmb9W9Mk2wXLQKM59f1wosUpRwzp9rMsi1BtgfYdGLdzTRr79h\njJu4YBU6gZLM0gxJRP4+YgbdSMKRcYU0486YJhlqWeWb6wVpzEoGe4pY+4vbnOBoVVsenqEZpDrB\n9i3S5h5+vZsTMDV4zFZ9FqWZF5cDJAoHecqY3NwbdS6uZr+wEd5aAlIqk1bmVCulVrqVEmhHR4m6\nq4q4ktEverqhI0WhBXawmLx58jdzNCp7S1SrIMnmPIwqMMPTbJHEZPRdVFleO5dnsGsQgKuwANcc\nuNdgEjBDM9mdO8fwymuJd87lRZ+dO9utUUZjvRRkoGIVre32s1aYdrDbyUHqeig7+NrXjLygIVME\nAAAgAElEQVQ0TpfGuDHGnbxtJrj3pnP/vhyn1Ylz5wUl2seHrGV5KiCFNixq0kVO19JsBQ94o3d4\n1EnbntnQRaVKp4yoJUhFXNtkCnvmvKp5LDY7+Ey5z403mxJzKa4BL99x7vXwxdeNF56TwI0H/mo0\ncrY+YyrMimU5JSVYXBnOzTWsjoQ9VbTj3uRktkcyxQMz58Dg1irDZoGNu1UspdiHRA0bN1Ms7DBo\ngtulq4HC/hKLtJVWe2yo5olCUNGyRcif5ijKTEbW3MJDdXnISZWDUzEsK6TtFok6FYYhU8ZK6gyL\n4heCbZEtYVbFK8VmT/umDmHwEcVZY6K4RGyyB98awNltmDeqUitnG7FQuqxihVk7xaPcO/ixNdZh\nyujvRWX9r74Mr7/hrEjcvy+H6ujUODuXYRc90+QYmVOKijNqER1te+W8+Q6sbgnfzVnU0bNLGAZB\nCusTC/GjylQCRumTeNkPMZ4aDxca1SPCCuLkvLkwHmF/C+19FpjpcnAB4ybwGhYaAill700lGchG\nL1PmU/hrClzXvSXmVEmz28K6g9dedd46E4/weGWcrGG9DAWjlBiDytakHlPQW7wa11fKuDZyd05G\nZzfY2gJKCgZJWvwNSg3hRGv4LWGL99eiYlRSJB2FDzqudwiJvl0kxJqOhQuXCE9XVMJaJKNIUtnu\n5NJS2NXKrogdUd3xWshegg4Wp5ucEpBT1xllB4tVx25b8M6kgRysl2z75JFOI4p2bugQFMQMDzX1\nt+yhf2DONFVKFAu4S0TKW4VljR5fiN8q3RSYqjGVRC0Nbw0P16FMVUqBk7Del16Aq60clxF4cB+O\nT5yzs4CwMPGxs3HrNHH7VCfuBd66Z7zwCpysYL3wOWo9PjLx6aMwKlXlZ1rRxEfBPHlqDO4c9sfk\nYczhiAG9xQF8xmjK5PPuSsge5uDfzhxXN7zqa1qsCm8gETKwXQ4RlRaCTMY0SnMzJQmjrwZYZudb\nPgYPruDy0ugMht5ZrpwHD7RRHK8jCZAEW2DiAva9s+ilPJWURp295Js8zJkjCslb7qEjI9H5PslZ\nw9NJrkVVq17X9lzBQuEBIdpOSY5MZd4/n6Ao5ahjc8NJ8nKACdgVZ6yVXSlsi7MdYwN3oxRBQW7B\n5R4nidgkp5oSQp58/iLuleIeXFLEVig3me9Hw28EGrW1StAcXX8ZN6EhklRVCfvEWokNcZoUTZRJ\nnmitkl+tVZvwOML1tYVBFvTgoSj38ovGW+/A0cJYrOF4lfCsMt2+c8wqXXaWg2hmfQfv3IO7L8Ki\neeYHEOKig2WGLlTNZEP20XGL0h5mPBUGt1G1bM50NvqWzdhtieyg1T3cEIFIPG64aMABgfHWOnfe\nnMF5faYyoV2n+nhgpmxVgKzk2moR4hoJtqPRG/yJ1+Cte5KUSxlO1nDnrnily4Ux9CoNJXDjycX5\na1VwtQq49yp9h5s8GmcWV7IjmwV+a0Hd8Xd5DgYwBQZaoYyVsaJFWAgD7KpKK6J17dzZ4QEvRK18\n9flf06+YamXySnVVl1kCz4ntpLLuQtxnxRi38tDICRsy3ZEWuRvU1MpFdd9U9gnYMjrbawls3+TR\nIsYGAQ49B6psLRIxhr5JcsaGW2xeFzKee179NMUSrVBLYhq16aXOGYux3dhcZDSNibpznrvrfO1r\novpur5wX7sK9BwbV2Fyq6UApoqNdXxrdInHrOMp84zxLlPm6N0WzoIm6z5u8HDef7c2HHU8Phhu7\nIkSFGXujabHImpNrQReLvFXUa7f3UdWJV8NTDYaDQvv2JJ8TNjazFRrrIWUZWGhQgV4zJKdbBVRg\n8K2vGl/8Cqy2xumplMmmbWTakzzf6y1cXYlHmA44fMU9DO/NxnDlwzbs1qPk0wIHkFBMyQr+anN9\nI0rp+iSallksvETqLMqyYxOOyp/aScwoY+QAcs1aGFhJYRWSRbUQ+r0GvlwQX1RYVZx36pimSp0m\nUm8yDkl6DR5hdA2P15I26e2mMo6JYQ2VD68o9c0wDnw+dXygVYUJPy3FQpgmqsFqXKtIpE2FYA/I\nmCWLCj1nzqkMHSwDQszZsd4pblxdR0FFcqobR6fO/fuJ9bFzhJEG52tvqrnAUee89QbcOzfyyviW\n1ypnD5TcHifmIptpUilvo26CzqVtLNbsU03sO+x88PFUeLgae9w10cTFq8KP9mdzQlpVSbTGo4X5\n5xRGLVullMDo4p/quvdsB3PhN+1vOemNm3HPyeYKt6GX1sJqUMJumZ1PfptKj996E+ooVsQ4qpjC\nzONmM9WLxxzuxvDyaF76HwdMwbGgfylwKUGVi2gEJb+mAp5k+FIkLN3AOsMGo2RnxOd2NjUnJhfW\nWt2ZvDK5kmMVZyyV4sauiHHQHOmGF7ur6i+lLMjDhRMXg5KctDBsyNQsMeVpVxhymrH+grzbUpzN\nFZh3LJbigNpDViQ99SOiSdyZxhZ9hJdfmGmYZtqsSvVgIogXWybhu9utsd0R89aKnjyUwMLTLBIv\nmnaCm3Kn546jqGOLDLl3lkvAKi/edbYFTo6N8wvncoLVMbzygqCE1VJMBsILp+Vuqlpm1YiuLHjC\nqkaTQtkcsX3I8fR4uDSs7zDMsJmh0AxyC8sP/3+4a3gkW1qLDK+qdddii4WQBNRjsKvyvDz7bMhp\nDL4MREKtTzbjsjboGcPSWb5ivP4OvPk23H2ushkzOUlkJZlzvLZZlGUcU/Ruih0zPWyA8k0wvCm8\nHai4GYILCLGTaMNCnyk4xYxC3YdzpkSaGjnGJoqU2aZ94DLTwRpUMRUxCtyTxEfieDXhsWXnUUjh\nTFUYrJvvWRWpsR+cRU501bhzu+faKptSua6V4pWry0o/JBLqo9X1ezWxGzviopfA4Ksr0eyxfnJW\nTqUJRxkwTkYN+cxpYqZ+io4X174qp1KRPGbsv8GPl5NUg1HU9TCOTr4wFivn8tJYLeHiXFHobnTW\nJ4kjcy6vhOWen+tzm8ZHgyIVNWmDhmAR1cgM0s7ZKeXh+H7v6eGa2cfN7BfN7LfN7LfM7C/H8efM\n7OfN7Hfj5504bmb2183sc2b2G2b2Pe95Fr5/kBuJ3Vs2VMcUayhRlaNOOqWG++q6dE0WMRgIUg/b\nCxbTQoYiDuBuaur/ME1GKWmGeWsVqNdAddhnrQ2fhc8XufKxu87t2/DgXN5OCSFsTEryVGdzLQpK\nl/ZYtc7jyXhCj2VeAbylkpiTolH0Re4SfZ+wlMVntcZQUDGCmWhZFRjdmYDJ9LiEJzrBrG2wq85m\nLGzGkd1YKNUYIys+EVlwy5QUSbYuQ29z4UtL2JTikaTTfB8tB146HXjhuOP548Rzg7EKUaWrc9HZ\njKqqqz4MPjd7Xg2xN1La+ygea6kizDzlveBM05guRTAASO+2rYWG+Y6TSuYtmdg+neQb8+Ck7OQB\nlitYr53VUtKq/WAsFsbRkQzoJozpapl44Y7TJ7h9DONWpb0tAQcRTYcXXmvLH4UWd61R5ss+D/CQ\nJdvvB1KYgP/I3T8FfC/wI2b2KeDHgL/r7p8E/m78DvAXgE/Gvx8G/tv3+gDZM4X1zKHYQaKLwGHD\n8+1SuPrNa43ZbiB3Cq1Zs7aY4uZ4V0+ymOxgJ7hLc7MJEWfaDQKbTWK3kzEeg6C93TYeJyw648U7\nERoXZblbNrXrYDe11iHyqEoxtqN20vzkYoxHPq/aOC2gnqhNB5XSpmZMhYVCmimBZqK3umJ33FLc\n9MFpdp38iGg8giMk99joReMk3NcJOlgkzAoBP0imjeKV4oXqlZwTKWe6XkB912f6nDjtndtHiUVy\n/GJksXOeW2a60VgtMsPgWBaMUdwZi8p+n9B45PMKLQZEeLmDB1fVzMgh2F6aUlwlKIAtNyK3pgQb\nockljqOikgrqrDwF5j4Z1YWTz1AF+ty+U9ltGSvZnNNjOD3RWh965/JcHlmZnPsPggFTFXW2ZJ3X\nSPJVGWxh/3pdg58MfS9/SCrnexpcd/+qu/9aPD5HPY9eBb4f+Ml42k8CPxCPvx/4Kdf4e8BtM/vY\ne34O8WVb6NhKXmN3IehgtC6pNAWqRjVSBjQFlptQZruRqssU3qpZ9EqLDHpkzVOUJlZUBtp2Mnej\nHypjMeooDKfvVULYJBm9SnjjO78dlgufm9x5ZMm7LvDmdh6ItiJBrCezMB/bvDoz97Z1Cq0QwtDG\n5LqJW2RhFmwBd0qtlMBjSw16WHBrR4fRK6Mrm3xIF5JH65RgJBCLrJKYikUFU9ugjbEKE27JTI8E\nn6fE8iTx4otLlp1xfNJTz4salNbCrc4YBgtja0xF0MTkRvEnkx55HPOqe5uYMGjVoHMy2iXVuN0I\nOpg9WWw2wG3zrIGbTrPEqeRNS0iYtnkv00FvwNoSplrHy6V0TnBjORgna3Gjcbi4dIbOY8OvnF1K\nGnK3gd1W3V3G0WY+8DQKG57GFJxhZo+4FpqX96HHB7orzOwTwD8L/Arw0kEP9teBl+Lxq8AXD172\npTj29e/1w2b2GTP7zBSe4rx3hJB3o72Z7ZkIjebTqF6NhkKVkQwBKlJUu0jpJxIrNXAYrzOh2kP+\nsRQtetWEq2KoeMMDRW8pwLgTuL5aGMuVgP2jI/jEa/DiiXO6hnVvnBwr89p1Co3cVUCRe1gu6lx8\nkR5yx/woxqOa183kB4bUweS9tJvWXUlO90g+1aDiEIlSh+oxFyYjK2OcKDVRqmlhVv1tqoWxlCDd\ny6v1qoRNdbBmtCOknWaICGHKkz7THEk8WmW1MI6PNYcnz3Usjwemq4pPxvOvLFmEPnJzCCylaHD6\n5Gt7H9W87kYgQu+cWxFSayTpUG32bhtLxYPSV0oUOszJMEWjtUZ3hiJPtxU+uKuzw67IgdJjwRJT\niRDfdS/cfUFO2DDAcgmX585iZVxdxhybIs5t1Vps8qwp70XQuw5Sp/zCVFuXiKbxcFie8+HG+za4\nZnYM/C/Af+DuZ4d/88NKgvc53P3H3f3T7v7pfhZ43jMUIMKG+K26djR3mz3VGXVw7UC5ebfhCbf7\nvmFxUxDjawlhxiqBm7GEtBsC/lMr/a0W2VcZ+s6c41VlMTi7nXQ5j4/glZcT60HGuktG3SaOhsRq\nYQytgg1jtXIWOTQcGnb1hCsfHuW8Lg9aDLXiB1UYKXEo2l8YWHcqNeZZ17sQGLurJXordtgbZXml\nu+paRFNiV4ztpCz1VGEswVxwicqk4iFor+hjjBLjFJVE2pzVyLJWOD7KrBaZ9WnP6UsDyzsdm7NK\nv8qQ1V+rdREA4bpWn7wo0aOc12EAkqrJaNhnEiRUAodt5dbCwgkDSeRMjO1ks1ZCmTwaBRxAUEXw\nQil77H83wXYrPHi7CWgARZAvv2K88mKl742LM4tK0cQrLzqrpQdkqZ+r3piqsd1GH7qpNQNVMjtB\nNJWt0QNNVW5zFeJDjPdlcM2sR5P3P7j7/xqHv9ZCj/j5Rhz/MvDxg5e/Fsfe80Syhffhe2Ztw/Ra\niCD7lGbhmRqhjSY3gIkIM7EIc1C4sSdbKwQdW9spZy6SqIHdyCDo6k8lvOlEKFjp80rRe3cGZcrk\nLrNYOdZXxp26l+LazYclLNfeMnBxnk/c2D7aeXXDDrbN1nYFDm9cmxNUghhUflvxOTlWG7c6OJse\ngG7LWI9VYkXCbhUmlgl2JbGrSqbNnxyJuBIGUYmQSqpOFxEPVdnoqVQlaL2SB2M4ynivlizjtrK9\nmCiTmppOk7Hd1kjEPlmy3+NYr23RuLeioT0+W+bqLDFKpjGKD0IgqLIvRlFZr6Cg1droBm1iLUE1\nRpg/jc3RirZUUUlaqxJmn3jNOFobp7cqW3euruG552B3qc1vcy3nadzJBnRdZbvV60u1kANoP4kk\nYHCAhYcIinzIjfT9sBQM+Angs+7+Xx786WeAH4zHPwj8rYPj/35kP78XeHAQynzjE4kvFRBtLCpw\nrzN9owHvZjWSXXqtuuaaXquYlTnZFnxYhTP7KqSihmaYhYBFCmgBn6UVuxTJs7pvYtgysSdruHsb\njo4Sy5Xx0seMT34HfOITMCTjnXeMkyNp43Yd9OZKZzh40cRKx+G9J+lRjMcxr03z1wKjNQIa8jnl\nMs+HpstxU5sdT3uYYcL3lK9WgVYigxxwwVTlcW13NdgmCwq92AieGKveVxtxlP/qSuBNO9d0hkq0\nyftNXSL1ieLONBa6RcduQswKU+1/g0xyyJfVEabdk5nYxzKv7eL5vhor/hIQmc2P++jE2xJkLXI0\n9tTNWpxajBduJ77rU4m+b16t8Pgxyn5hLzRUvTJOghZunRh3n3dWx/Keh954/q5x69Q4eU7QQQ3v\nNAeeTIVhcK6ujHEbDplr+0+EIwc6Tiu+CKroQ4z3kyP/s8C/B/ymmf16HPvPgP8c+Gkz+yHg88C/\nFX/7OeD7gM8BV8Bfes9PiDlqt7tFvXWrKANo6QxrJE7bA/R4a9jY8KRgJLTWEe5qX+37csKKkltd\nUlvnzZa5dXI1WPSB2SQVPSx6ndM0qvb7+MhZrYx+abz80v/X3tvESJZl932/c+97LyIyK6uqq7un\nZ9gzwyFFw4BXhkEQMuCVBRiGbJha2IYAAyYEAtzYgHc2vddG2sj2UrAM0AYM2TAgiDD8JUDWwhBM\niAJFUqRIeWbY89HT3dX1nR8R8d6793jxP/dFNjHUDLu6KmuScYDuioyMzIx4991zz/mf//mfyunb\n8OIc1tm42Or9fuWdyoutFlW4YINBhHM5Nwr1vfp1BeDQJWixGaVBG042BQcaAmhQkSw5gnRMkEzB\nF3peK55Wh7n9/tCzLTNYv4L+FNucME2PyUG6VJQd8IJJUYxwwrP7or9RK2CJMjkXL0YuOotJFVB2\nwvv2+5GxGOM+Me50ryVTdAbWRmHdhL3ydfUqTq3S7chEiap/lQPVbDcVhiU6pO6uVhhv7dDRXMg0\nw+NHzje+Juhtu3XwFJJHUXhL0kDOwb+di2oyCZeS2GDcOVOdZdqBl8pmUCabUxXmXxXcqcbjrE/g\n4tLYX8HmRE696XW4h7OeQ3UuNHRfxn6kw3X3/4dr9aw/Zn/hh7zegf/kT/tGmg4qiyBx5Pmtbuza\nlLmqDXNp+DBC5tAXvVUPeEC4YT1ssoAV3IkhkwLeNZwyLrY3qcUmsSjMTyO6NccpRVtg38Em+H+5\ndx5+7Dx6rFbfWpx7Z86uKHIqsyL0LltMGSDODX2+122vZ10jiuXaJwwH18WTS7VadHa8hcKeaICa\n7gI7FFE9opAgqS8HdcqQDU8rqk8MJw+Yz68o9RwzQRWZ1p0YUXPcJ03IpFqFmmOUPbw4L4wPMj5X\nxsuJZx/NlALjlbNbA0OlX8HuwvBasD5T081BCq9jXZtOdZk0Jp3us7zl9tcbQ6Tx5E9PpCsyj0Hl\nG32BAzEVxsrovH0fnj6JkfSxV5pe8TTDepWYrYbIPFyew/4Kur1jM8x7/dFHnyYePKhcXjr7fZvq\nUZf1rgZUZ71SneDyCjZrlNnkwJ1n4c6tI/21YLivw1pxq3UlyWxR/NLzUe1ur696TQkeXVOd0gsO\nFelW+JpnLep+VBfKNIvofnHlITzuy9TghRnhghuaRm6ZK0OGoRNWSFHXStmJ87fb6TT/8ruKvPe7\npFbP2IQ5BeGbVjy7WRz31ZrHNYw18ybLF3gZ0WDiobmAQzpUu5vIfLOKoiEwzSkz6E3i5ZhpvFKX\nSB3i5+7PSaksN3kTn2kZlSekCOYeTp8F48XAUuLFZeHpiz3jbFxeGNsrKMlglbjYzoz7CXOnGwxP\nme2uBhPmZotmr9r6Diw52/2hscirMe5ZBJlKMBEaLroZjLfvaHjq2QkLhTMuN102vv+h8/xJK5JH\nR6GxZLpjgattXX7GHc53lU8/MS4v4NlT5/JKUWzNzpNnFvixpvA2CljCDv8lTY7oBymTuaeFndAU\n/dxNxdCX5Fe/Ea29S7BqttSUFtEIYzk1m76B8CE99nCQgUGgWOgQQoYOMe6BwxUVZzoIXNbY7yUu\nPHTCW5fxL0FPSylpAN3seBXDIZmEbHZT5eEn2sH7cyNRIx0G3Hl+/tnDI4WGqj6e/8mxyK0xX5gm\n4liCnG0Ncflr6nCxtoIVAA9oIeCDpB/QmqMKtZphAn8zpZ017UjWUXef0qc57h0J5rTDFA+uJm1j\n6UbrE4yGRv8YeM189HDkXjLmp4XaZUo/c2XOZUlMoRNg7qTOySS2o4Pd4imSEXn2nWGDos6TVeND\ns+xVsX0i2JkkGp6zstHNSRSlTHti6OH0DjzbwnbvizQrxPrEQdvabz1ahlvR/KOPFRQ9faYuUnA2\nm8rDRzqIhz7kHWsTzvGlHlOjEJsx6uCMxbFi5M4DKtE+LV9AweWNcLjXraUm+Vpo0wRkuhTtuoS0\n3xIrNQfdMEKgBk4Xm+u6U29mBMzQIAxvsEQjakeUa7rwXRsRMkrW8fIS1g4ff6JGhkePjcePlZJ0\nQ8LTrFlOHdRZgsY6ORNN3OZlp4C+6dauXZRH5H4DNboeerorvfcoiszV6INyVarhcUjVUPdaFmhp\nhJGQkLkx47hNSx3AUyJbhjphod+QG03NAoIyo3cJ1Pc5qVAXbcHne2esRhphO03UlbEfKyOtUNuw\nSsNSZVil6CS8vSbsNCJd1Cwg56n9WYsCizIdRPdrbChtNx2OHhNS3n3X+OpX4fETeNAbH3wgneNS\nYsBAp/2aI9jxgBl2o/H0OaJuBeB/tYP3Bo3D6Xq9bj9aqNXpZtntosXcArePuk7KxjoLRhijAaqL\nSQ/VGmb9+ffsG+NwU4DnSjNiCJ1DJulCm07G9jxmIZjRNnSM2WmjPEpLY+OkNeizIGLL1+DhFoFF\n6KMR5+LTwiHSVjVdxQKzSnUjZ+meXuydszview6DselhvzPmq2gZ3lsjTdDeVE4S57D0cgv4xpsn\nPMnRNjFq4bBRnOhUYKruZIsROwaZquglJam4JQ5SiBWmmBbRxIiEzRqQyRFBYxUj47am2qhsJQ7U\n5ZqbYAVSu4+MOhaKhbBR10l2MKtld1+g77IUyGbBITmyF/NQRavScL3NViI6TBWI7r0uDtBaGsZg\njKNqIn32ZVCoc9CgNYPVCn7u65mf/YZz8S5854PD/kwYc6n0XdK9UzX1ISXVTrrk5M7YFuODD+Hy\nUtq5T55U7IEEjqYxBsxGNG2NIxwMl1qbiJFwfpULQrSqj+ciw3rZIPeNcbjuB/2CJQg1gBpk6OgK\nckWMXSZSucB4klKcFCdfgyLaievhnGe06RplJUeK3xl0UQnvssbk1NLqNtE1FsIkKYkFMU/Gxblz\ndlfO+moPDx44T57Bdiw8vwRMgiZzhToK05qrTsvccXDCt9Ja9hB4bauehONsDSoSHFKk6rPRpvJY\nbowCwTpLMSZSmnZmdtdoZ6SOnDeqhXvFbIXVLcYU0aw1Phheq5TdslgK+1pZzYnkCesWEUc8GfOu\nsp0hrbK6F6Nr7QCBeRTgAo2+zZlLnFcL/pqUlmtShpxbxnTPu0NRRtFauodBUqY//TVjuze+9Jbx\n4NTpinN2Yjy+dLEgEpodh5xitlawk06ChfaBoYkwHz2H9amxyfDkXIyRYQ37ySlz4s5pZT8Z5lVw\nZOisNC52u8FSkh+ZY/xWZ0Fdc16aO/9GOFzBd9cAaW9gAUuVmsDn/FrjgJlS+64NxqKdQL5AB42O\npPHYhs1OdkXLfW/BRFDThTQ49bN1GYZ3GPncUih1xej0dqA8Fza13yXu3XUePtIEiO0u+Iamkzit\nooliUpeUTurXcYVvyiQ6XcPH1Sg+uLdsRpmFIwdsrd3ZDZ8dcmCpkUoaxkywTohIOCeyI11oA2eE\nCl3kkolz5jQKs4uqmRcJ1eQQFq9jUTGty6ROG72VpT3DtK2knJinyt2zxDyrUcKqAgGCB26uD9mK\nLLfd6mx4VoAyTxXLmj+XslFn6Rh4QAIWfHTLcLKBu3eNt+8k7n210iHsvEwGWVN9W10mJ90bfae9\nmeygMFbxZf+cbyGvjAf31WDx8FPj+bmzHqU2tt0JYixRw2ltwx5i9VI9UyHWm68wDlhy+KTD3OfP\nZ2+EwwU5vNrUe9wO2F86RJ2txNzI8skOgLoFvlJCJSxnkONN+jegCOuvRUZxg6w6XxazS4mh19c5\nHHzOxmYtJsQ8N3FrnbJTMbajottSULdREaYFzhzzleYKHkMALBleothzmyMhGjH+0OCxjC2JrKVW\nFhW3SqTlKWE5yenG0DNxom2BiMxZdAtyhQ6jNoaB77E6YsSaxu9OJKzCsNLgrUrFQiGsgQxTgpUp\nW/JkjLvCsEpcXhTWQ+sqjOjYVdE2nMkl0yjdv3RjokSvxZzQjlbDAjFVd0FpEnLEvbGPphZBcGo2\neO/dxL/+C8aLJ6JxgfPsmdEP8PRTOL88RLLQ1AE91jwcJR6603rRxRW8/140LGXBAWen8PAx7K/0\nu9dDzMmrrZkqMYfehxzqwdHXVoBPOqCVfEuW9WXsjXG4S553LcduU08sKFl0AXCHkHQyC/ylnVLC\nVedZKZ9GcDdhCmFAnbUqNfRd6NqaE/K1dOasO6mBNXG26hWvidUAFsLVAOshkTvYTnA5qqts6I3V\nSqnxZlUYZ0lDehStp7kJ7sDCbbm1du3DNV61t4wD8MRBZ6EeosIqDV0bYqGyxewPPzjb5nhxkmnj\nNVpgc3WVRv3RPVInx6MIYHEYtw3WFB6mwJUzMG8rfd9RkjPv4ex+wooKqz1G9Uqq0m7tzBipuhcj\n/b2tFgEfXRcTVnZG36vrq2Hk2TQ0tc9QRjUqnJwYpyeSM105dLnyO79nDEmiNNsxcb6rYhnEGnuJ\nJpm0NO4DCpY6xVJsr7THnr9w3n5bmWedjFXvvPu2c3kHXpw7H30Mmw2sTiOiFXQvvQYIRTmnynEE\n5n9dc7sV6z+/vRkOt2EzfohWm7CLe9tQ6uTpczhTUMEiHcRRLIClvpOTnSZbMKAmGH0JlM4AACAA\nSURBVEMUzxQ1q9uslVGGSI9UeBNU0bpppskZ+sxqUKTVlIoKipJPT2qwD2DVy+kPa1gNsBsV2bXI\n22cdJmNtQNjtNeHnHtWqgzNUL7027QECanCDk3JS5NQkMM2jmCoIgeoSKImCqQfWnqPwObsmdAjH\ndSj62ZICfzUj6tKAWsATaJxOduYx2DHZ2F06q3WO1FnOOjvk4JcWqW5HRtYiolvsceEQzVo0sgyC\nEbyqK9CLqGB9B2mQVGOKNP1yC7/1TzTz76OngmYweHZRYnyOCsoeUfG0g7w36to5PVX20WUVtJ4/\nh6ud5hCe3hMGMI+tTVd7/e5G8Ee+C588Atsm+nXF/QAdNMjJOcAVtQTuDAscVcq1guvnsDfD4V6z\nVgAzC1UnkiKJKJJgUTArRl7bcuqkZBhBog/n23fE14IIDMdcVe+cgrWQ9Pu8GqsBhiyookO8XIsN\nWlxUkjt3YnOaXr9ey0FcPoMH92CcxdsbZyNnY72CyyvRWLQpY6ge0FW7hknfPousU9QbWodd8Gjt\nWrND4GSGChO5CzqBysWxtrLkkcFi6lZb2AmBs1fHcyvIRYroh3sqoYkRtcET4jO0P6UD3BNula5T\nhXzaO5vTgDvclp9pfOypnZsRJFw/WG6lNVwzUkXLTeDFudzqOjeMaJq0z/pBxbRxD9OJ8+FTZ7dV\nIJOTQzXGGBJXCwyryDw7W4a3jntjt9ck7Rzqgs/OtSfvnGp2YLvfUjamSfoZlgi4sPKVd+EHPzAe\n9LpvvEZnaWRfKWoB0xxBgflnOkJfcsLOm9Np1oBriAtEdNYHrNBEJ1q10CwaEa51MS1gTBTUckSq\nIIy4z1EYsxCmSYo0+0gJk6mlL0UxLuBftfVGH3ifFbWebNppKLX6PifWKxG4+8wi7tE1kndJzNXY\n7ZvSWZC+b3lxpQZiLsX++M9aUbQxTxpBPhY2shaiglyKY/XA523ZT7v3G8yQgipY4trTRIpSh6UQ\nqOFaEdZSaBI7eFqiZK9Qc3BsZ4dayKkNMG3l3Bg6Ga3iHvQwzV0T9HSbzTxgvmrMI1xeaV/euQOr\nwdhspAm92sj5dVkZn1voIFTjYttEZXStr7ZK71eDcP0UYv+ic6oFd54PQdbDR86qN87uJB7c9ZgO\nrI6zkxPxt4FFOwHAi3PvQeXxkzjkU42akC0HyTyzzEAkbkk1Lb38Xn0jIlxtIpbIx0MbU1XKg+hI\na0io0RhR63WUUFfG2ukbWzp10bPdKaIqwcFrzRBdVnSM12vYYA3YIiKsLGzWUBfMegOWbVmci63x\n1j2nzqhbLRt9JxyrVFHM9jnmmSGhnDIHB/AW+1tHGJ/GaAslrRDjSyLn81hPkANcsNiAk7I6+3Ju\nUWNUisO5WryuOed2KJsh+GE43A+J4PLmRJ1Fik8YSoz0DuscAy07MUr2F5WhTzGZQJ48uUUKqoaW\nnFqxLjrjUMHttpq45bqH+w5O7xhDkg4BfugwW/ViApVJTrTvBDPsdiyTeS8v1AWWs+DBrkcwoLUp\nKXwG9zcUhT5+Bvti3D2Fs7Wz36oYjcN2D2+d1EU3ZQ6VvmmfsFRZNZjjysgrZa/XpPIXnBgsdI6B\nGsyal+TNvxEOF6IbBX3w1PLvwP9ShJpuxFgaLY5XiRqvwsm1BSmzFj1Ho0PuJa+46hu7wUN+UQuk\ngLRBBRKy6LAFxhhWFm2+wmZLFY5UcZ69UCEgE22Cxbm4SNw50Q1zXbhYItji/za+cH3JFOVNN+kf\niNbl7oeoUGkMyYILG+m+hogeEq+UnK620fKtx16/OMVsu0JEvxElpxa5RmdRiUhMoiUS3eiwGBEC\nfUrSYI0irE9O7o1xlr7tnbuZOtVDhB1YSUrS2s1BKfI2+6G9x1tqjiaXpC4caWpC4lqvfhCtz6vF\nddIAx/0Vi6JYjmr/6Sn0va5hjskLTuC94fpSZKNzcbqc2O3h6XPj/n14605kQe6UKRxkNSgKrGr8\nuxrg8dPKsNbr756pQ/SuGbvJl3ZxDKYiXd5qB+lJRx2mr0Oe8ZWbBU5HleK/UZbKdXUVpa53kxGT\nb3MvMBz4jNCJRKPjdyewqgjXUKSKW0zPlSNtPNmuC75fON5h8CjEqR+7yxyq2wbPXyhBPVlrU+/3\nmlBwfgFfeqDHXmCcpNGae5Z2Rk2kqC9d9XzTrUb00DIWsxABSbqu2dTh06rAyVR4SpHFtNlzdayS\n97MYNomKYrmiIlXwd3Ofo2GCFrCQOrCaqKWEY3Qohak4wyphWWnrHI0LtdcG3F8W8pAi41JkUx1N\nc8XIqZJ6Rblxp4hPzO2uhbaqfWdtaED7ThQvTdevFK17m+pQHSwYAeuVsp5hJYlUQYSQV+1ekE/I\n1gqq4s+ue+fxU9VOelOhbo5DdS6VaWp6CYL/KtpvjrNaG9utZhSaOe+8C598Ag/eUY1F96rhxbm6\nMFbrgLninil2GEbwee2NcLggjEYfzZYuMTgwC+yP3cUWmGsUOA+vJRxxAPC5B5Kcdq2ir5Q5uLkm\nwrW4vhaCONDG85gLe9JGgvU6cbV1nr/QjTLNiXfua5EbdjjOcHWlSaK1nfxFlLRkEsRoQikSxoBb\nuz294ZviYZpHITRVadG2FW+slKrgJOHLooabW2gmXqKg6o5NCUuQc47NCUZirtJbsFrDaSs7SYBP\nrvuhS1iGWislYIgyOmaVvIJ9EVPh5F5euskSmvJLvFePYGDnLToyEonklVsOzcssyJzmS8t1ztp7\nEmlSZmkGq5PYk040R0QdpTtEtk3gP2ckg2q+dImmHnrgxaVxcWV84x0VRJ9fKkquNe6UqvtDk4Bj\nhpofHpNgt4fVylgP0u6Y94l+3eTyK0My/A5cXspf7EdpNqQu9CBuA6SgarSalVtLrmGRWhwqhhmN\nuWiRoSWB450fimWifCmtqCUaHJLEqaH97nQY5Bj/7UfRwLokbMrjxDODobcYH2LkXlDCg7e0kGo/\n1iDKD75nvPuu8fFTp4zG6V3BBn1ukENE3B7qYbd4Yzptgq6R3ZkjM7EsCMazNkZtxSr3A/bZ8DqX\nMoIggkSfnGSZOkttrOsz87XiagP8BUok0fOynHZysJUFzigO8H5v7LeFUo0ewRDjXJjcSDnJGXhi\njh6jnEzCLWYatW6NJaG21lrrZwei3lJrRepSDM8tSIq92tIZJRLkTrq51RpFM/ZZDkZD4KL9EEXH\nAtEOSjfQlGXoO7jcOvfuievsDq5TlloOrfKtuOUOU9V77DrHYvLKxdY5v5AC4L378OgT570vOTbo\nzVfXYXB65lzujDzA6Rqenjvj7uVW9sdmKZhZNrPfMrP/Nb7+GTP7DTP7ppn9T2Y2xPOr+Pqb8f1v\n/Fh/oFUFI+1Emb/4cBEN5jjtru2s6ED74+9VeO16zSLd1+QUqRbNDtJWUJVaDnG1cnZjG6OuRorS\nIm+DF5daxL43VivNRZrnQnXY7Y3Hz1RA+6l3K1//inP2wHn4GC5fGNNoXFzA1WViG/OVzKOd8Qbt\nla6rRettVoRvWQT1nJMGOO5hHFtRQ9FuXlLJFBi6Jhz3luiAwZKcaxfRLZXBNOBTkazGL7UmiGEw\n+i4x5Ew/JHIy+pREN8rG6qQn9x2r047hLHFyJ3Gy6fDZWW1SiCnJmba0K7tBUVR+KLigwmt87pdW\nOXlJe+X7FXGZm+Ld0q7dKUI1E681d3KEOamm0nXSUug6yF07KD0yFQnZkKDWFFQuX+odn3wMd+8m\nvvpladuOs3F26kuTk7u0SlIs1RKMRpbVlvBkJSeaezhdOe/+lPPoCewv9blaAbQzWOXIplAjx+rk\n5db1T0ML+8/QjPtmfw34G+7+c8BT4Jfj+V8GnsbzfyNe9yPN4FAR5MBMaEISLW2ROIaqnS2F0fdY\n+Ha5YUBIpciSUpicxe/L1v6G0WcPpoIoXXdO2+JrEfsg5u9H3QTjDNudOji3W9FUzs+N7//A+O6H\niT/3df2+vjce3Dd++uvOcAIvLhJDb9y969y562xOnNXaWW1uHE54pesKLBX9QsKTNmU/JOGjDuMO\nxi2KPIFUW8eSCqR9YLk1mh0GMwYLla4Dur9ANTl+1pKwOZIaYBrsoCnRctrFq6T/8jXWSIqKd5JD\nbZMHdBvaNYgr8C4CUgonpILtjTMuX926NgfWCvjeGCK2FIgX7YzamCQ1IDQLzZJDu3cKDDdFO/4w\nKBtUwVMH6w8+NrpV4ktvORcXguXunQU+6ypMW9KbKkUZa4UFYmhvXINiJTi+jyLYZnDe/gpcTvDp\nx7C/Ssu0YM3La59PLIqXsR/rrjCzrwL/DvDfxtcG/JvA/xIv+TXgL8XjX4yvie//BbsuQvvDfn/8\nr0WjHpiYOomaelissFXMRSnp+1Y3DlwvXt/FaPJhpR7qrnO8qF33ZNBo81qNix2h/KRf36VQHMuJ\nlAPbNWecRDXZ7yoff+J893vwwXeNP/ow8a3vwfc+gW4N7zyAk3XIwJlT5xp/13nwdmHc+WcghOUw\nuSF71esKHBS6aBGGxpBXHJKTh8SwlgecxopVyAun+iBUUsLLWQqxoWSkLuOeSCmF+pjuhy4ca9dw\nWzxew6L0loJnay2qjiyoVg2nrNUbgKzI+VqtwFGBzmMyYSHGw2DCjk0FnZuyV72upYraNY2HuolX\nQS3jqP2UUlqucUqH6SYH3nyk/galRgYByooSrNbad7ud8/ATQQsP3nLOL5xqxsnJwXm2QE3TXeLQ\ny6IbzsFSaHQ1gT9AtAuXWUXYPsPbD4w77zjPruDJ4+iOi8NgDv5mfsl6y4+L4f5XwH8OnMXXbwPP\n3JscC98H3o/H7wPf0wXw2cyex+sfXf+FZvYrwK+AnGAmqpHZmCfNo2/qUa2C3TZfIk4th9QpbRRO\nUxcxi1ZJNYNVEhe3T8IKc4KzO85cjL7P7KbK6lqc1IdmQwYutokywXd+YKQO7tyFdYYvnzgnG32W\niuhij54ac4kx3FEsyJmYAmrkwZnGhHUlyNR+ow6XV7yu675FO7pJ20DA4pXZjS56zQzoBiOVzLRX\nRlAjFSwuOM9MOYlqocp0TAKm5JToIopy05awlkO2tu8ohqboRhTpPQUjJjZli1bD6fYRJh+YmeqW\nKrQDgYUDXFKMeor774bzlle6rkMvp3u5gzKJf6vxU87pxjg7O9C+hsEW2M46UGTlhzFW+dDI4B5Q\nQyj2nZ2obfdygq+8qxrJXI1NPrRzzzNgFgwiZZ+nG6JG0oRz9F8q4nWnKphysxIlbNrCeqODuU/G\nW/ed55fw0cfG/bcc64kxQf7Srb0/crub2b8LPHT3f/y5/8oPMXf/m+7+8+7+86s+CiQ4GY903qII\n0d5IvOEoUrRoSbiZAPh5bBigLxFvxqMamlQp18wWuoRaBA1ON07uq8TFMboOLrZweWW8eGF852Pj\n/fedn/uG8+W34M7a2awJGEK0JuG8JlpKkWJ8w4NSFt60ip5y47M0tpuw17GuQ3dwPh69g8Ut1MOi\ncWFhbEA3yMFqDHkTntEtasmi2zBRLeFZkUnO+i+ZFMa62MCp0/MtNF1EjDrDuuAEz4Xkdtj0ro01\nzYIO+mRSBAup1GIap1MdSpLDDoUkUtZn2xbYGuxvaBzz61jXvlPX2GajBoLZJdO4vgMT8OQpPH7o\nXDxTkbheC4xyJqQWG6SgA6vvBcN4HKbq7BTj5/5dBTebdWUYnP3o7CfdJ7P7kvK3yQ2582UemqJs\n/Q13Y3ZfhmCam5QCewVsjRdvOHfXzv13K4+ewPlTSakuVMOXsB93TPq/Z2Z/EVgDd4H/GrhvZl2c\nml8FPozXfwh8Dfi+mXXAPeDxj/ojSz98UG20GJoUUOuB77dADOHkWkteNzh1NsbRySdAkOJTEl8z\n9ZWq8Vb0WZFPl1W8Wq80FHK7F1bbuxoqLq5gPxn/8s8oFapFPLx5TnS5jdVuAumH4kGju1xsPdLZ\npvWgny+TmA6Hz30j8dCrX1ejTcBpI94W2TvttYgco8rhDnnQjLnVkDRapYH5RKSbbFGHE4sl8CCT\nM8YEBbk1KEApZg5g11LS/LFk2Ky0MplhtWqS7OxszyvdWhzfbM5Ym8yN2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QAAA75JREFU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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHh6gtf0yvK5",
        "colab_type": "text"
      },
      "source": [
        "## plotting images of bad category"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNO3p8vqy0xI",
        "colab_type": "code",
        "outputId": "eecc97b9-2c02-41fc-910a-cb09930c6329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "plt.figure(1)\n",
        "for i in range(0, 9):\n",
        "\tplt.subplot(330 + 1 + i)\n",
        "\tplt.imshow(cv2.cvtColor(data_bad[i], cv2.COLOR_BGR2RGB))\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y7m8DesbDafuM57D/G876nQ9r58lSEOBHgd8zs/95eOt9wHfH8+8G/uWw/bsi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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g-cWDNE4msud",
        "colab_type": "text"
      },
      "source": [
        "From the above 2 plots, we can intutively say that the good images are the ones, which have less brightness and the nerves are clearly visible. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9R9aHFRuOuDU",
        "colab_type": "text"
      },
      "source": [
        "## Combining data from both classes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eujck95tO0o7",
        "colab_type": "code",
        "outputId": "77bb4166-fd23-441e-8e3e-c925eed4d914",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data=np.concatenate((data_good, data_bad))\n",
        "print(data.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(194, 512, 512, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zi7j9_1cOfL_",
        "colab_type": "text"
      },
      "source": [
        "## Boundary detection using Canny Edge Detection in OpenCV\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XzO24Mgdw7RV",
        "colab_type": "text"
      },
      "source": [
        "Here, I am checking the boundary of the ROI image of this dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SFf7gqzxiwR",
        "colab_type": "code",
        "outputId": "a592d0e9-95c5-475c-c4c5-202988dbdaef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "img = cv2.imread(path1+ 'drimdb_good (66).jpg' ,1)\n",
        "edges = cv2.Canny(img,50,100)\n",
        "\n",
        "plt.figure(1)\n",
        "plt.subplot(211)\n",
        "\n",
        "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
        "plt.subplot(212)\n",
        "plt.imshow(cv2.cvtColor(edges, cv2.COLOR_BGR2RGB))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CmBGjzsI4G5sumAaNABEeW1LlFDXjoLf8CA1ap0LD4CqBt7uSlbGEKD63pZCq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\nXilsCewWXin8/wW8DONGQIObAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZS75ITFf6dZc",
        "colab_type": "code",
        "outputId": "b2b24651-064f-46e9-9873-01b649cbcf5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "img = cv2.imread(path2+ 'drimdb_bad (45).jpg' ,1)\n",
        "edges = cv2.Canny(img,50,100)\n",
        "\n",
        "plt.figure(1)\n",
        "plt.subplot(211)\n",
        "\n",
        "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
        "plt.subplot(212)\n",
        "plt.imshow(cv2.cvtColor(edges, cv2.COLOR_BGR2RGB))\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uHs2RnPl+3Ack7hXeg3Fs2qAvL6RKgAnHnYGpW65v3MsKTkiOm/o1lpVGqsv+\nPWGqUs2QidwDd+57efaohdWXREoMTK/Cm2HVbhy4tWNekclkivNr4nuDwac2Bsaxsr0VTypobe7C\nyDEMpk4X3N7KClgXhlXajjArDdbuLQL/RG1VaIMdTG4MZx0iMFEzy6Xh1o1NO3exoLmdaJ8r2CqG\n7vlauwqJwmRwVZVRR9AhDYpIRlbCidAk19S4EHTs9+EipW/83kOA9pt0E8EmUfF7h7TTGpi5+ZOD\nWSO6lXUNMLXfBsD7Iid8BCtMhfeCYApRlI3NAR2a9ySYNj0UNYIX3hc1rOZxR3HpVVoPhiXcT86Y\n+Y99qI53fNUy6u4YKTXj0f3kVJ2q1D72XUdLvIhyOm0puNoNfFdAw4tkoWM7p3kHMsk7bqwF1erB\nALGCb6O6f7kNUBg2zfthbS0lVoNZYeautGoHH6ZJJr1lbktWSjKvRQ5j5WxJaWYuL8kJEq6o/r79\n/cEjZh18kIkbr08wisAOsDQmDOFiS8x9gw5X+3VsBiMVS3CaTISdtDYMK9DKkKsX3xWgmIupd9xD\nY1BJSWDfo0a+assDbX5PUNyZPpHI3oJUjTFxXJWKTCRxkLnN/EylWxPVcCrapo5WoY7xYg1+iMCw\naMtRbOmM5MYXvcO+j/MbGbQZMUZtEh7wPISqinqlwFHDNzr1j+Kei6pt2Y5Bf9+LK0YpvPwN8Zjk\npot4acfwaowY0hx/7uiehryVwyjN0d2d1odhPbxKsKdOF+I10BY9RRuMCnX02d1JzRqOgZRUASlK\ndpwldAeOi/M8b0CVxYrlHI+uoyfhxD4H6qHIAuHX7NW2jV243tqrqMDGIJzZtP1zL12pTUV2VQ57\n+BFkngsSA9oqDP+rknhWie1AJSdAXN+MLWd4NNdcgtdACFgVjRcmqjuWwsRrLZdWFmqIiTZGdM7b\n7e1bkUHensJuyxuD1oZhoVNHTJeujG5Fi0ZY1ddaBf5JTOjuoQJarfxNGXr3lxlHtXrVmGrbg0LJ\nMuYt5u7lkzrnu0nMkkYIOlDVE8B9xFpoM94J/17TuxBZ+gJtt8Clcv7WwnIcubJ0xpLAjJ1bq6rd\ny99dpXhyS+9JsHbWyKsVO1cM4HaegZKBgMmELIOVJFUF0SyGEWMk4QrzUk/Lqh5Wt3favgpnThX3\nbatF8SD7ILZNQiVhT4MmJwESKJ5nKc3GEHuJUgVZROQrXqh4Vn+oX03pSXQmrtJGfK/ZZiFDgwQh\nhfvVCkniy6+7jCyo+ZzR25quq7xW6bcCTcONcCVNw48qcGWpXLls0bUirfx6vwI3ssGaceSG09jW\n/mdlQW+Y+j1iX69g1ow2deok2QsAACAASURBVCWYjGWK37f6PT0vS3UCR+yS7p07+FEVXqq2g3ru\nc+YQpXkTwgDr78sJYdiOwjCQSu7b6q8DdIaPuvNZQ9W3yn55jmPALFJM+3/E6gMMamv9h2GXSFVt\nNVSFVkXaZcEOP6wNiuy4T37OYz65nHtqtSzdF19aWvQrDLO6U1I3fNgYL1VtTRTRzpGGC2svgWlM\nDwEb8JoHFlpNvu/6zhi3BT16KJVIQmCrVrab+MxcjniXpg00j0mfB7oHrQfDet+JVxqMhDTFEyzU\n6jBFmcjAVFWVRTfrW3hQ851bh8asDuZyRqzKUKLDGrP3lnmlQo3yQoYtraMtWyue35hg52BolfTU\nB9MsCuY/3Va0wGU3tMaxeRvCQIqVC4G7gS70Gd4H9z2r6w+veDdqC7WGt6IVs7A+j10V27uUrq3N\nQGurN7x/PEomvthRJcaO9IKAS3La/ftJZ1DAljO3TfB2p7Vg2Jht/ZaPJqmGtsW8tvU/Lfq0y7KQ\n7v/RdXA4AQYvPRnPDPZVDcPJy0EWs2oFYNtwIi5xreCWtWMZyxusNcn0eSQxpzNUupMMhy6XvmGw\ngV+WmBGXidTgqxaiDypai0lC6AIadNGsklrArO6W/aUpfSX/18AIYtUUp1g4mLs0IeD3ngRKJCR5\nytg2Dr4je6XdUzqp36RuPE8m91iltWDYFuWbSsvsEEx6JNDPFzIfbaExc6vb7zNapxJzsi08If2U\nWtT3gW2qdrm1QHRAFltW+dod7Ii5h6pmPKvhQO29G3a61iYdQwLWEZfO1taom1wlclgdeFRt12Oe\nCPvJ8wo0PB5tslbwhBkzupoPWBrGdVhQR6Co5alK0wbBx8nQ2rQQtAiiithScnU7IiX5VCOGTRKT\n2MjLRGnr8314FVgThoXojJ1SKhi5qsKIW/VNdacCiX2qgvGlphpt9yGvJS1S78CRtneFeCBjUIos\nWY5NHAsmUUJiRKaSau9OW+11V+Wj5jr9xs9+jTYjCpVmRCVz0TwHtqLQJ3Jze401Us89pyB1fDCi\nvZymQWXtiUnQJPC0v4JnoUlCwFFDZ+TRYE+rMekrgfvwrrb7ZV/EBD0pDAvOtNqYqRSxBGGLibL7\n9DPPgk6C0W2mjm6hbhT31WIMFhkLFdxSiZpdGQNCpKYf0rww4r7FKa5Lv6fULrnb2hbG21JtS/vY\nX3YYWpg2arkmY7s2afBHPbPKVGYupUkYYBOzUpIbQlgV1yBKgxZjrhQI0QcRtu3fh/DvZmG9vs14\nARBnzNKkvKVzKG27gilM6MtzThh3R5LOTloPhu1ns5rU8f0yMo9U0vgo3Q4ztqTGmBD6SoT9smeb\nwOo+WBiyyoW0rKWqnmXUIkXNFdbUrbrerDUGNdrdKtJArCQ1I80SPmrb5c9zHWIvsXGJM7Bli0UZ\npnTTqRtSaVD2KYSSfRaaKDwtaPHl3Q4PamBzcQktCRkmHnxnwqo1XfgqLgq0JdQXDZ+yOuPavQOS\n9PViMhiiADXbP4WBveTdndaDYTsyQG4vFiHTBhPa5rr9Ltg2Vs6ocXJ3fQjc6IxxjNxZkzxhLgAT\nJgRhuawN+1b1zSWapW7XGKOVYqmCMXkmqlVoe4ZJl9cqMZEMY9bRJJNow9tKS8VrhlJTreEVGFck\n7wQS0CJbBmc8Q8oZri00lNYX8d37tP9uYeMu1VEDHnQegtBi6Zlo94z798cPYlZYI4btk0lsOXMN\nozQxl4ikI1+Btv0lvvmEq8cazGrukpQ44HsbuIQZfalKVtoOCWAh1lpt+TV0GVAuclrijGT2vYhv\nLqGag9SBFC8SIr6sOtrkqtfDy7YawbFouK8CP9fGFEGK2vMDfoytD2LD5z6ftRJLr6fwSdEJjAlG\nino8qslm3ZMdwrktEbkb0Qe5TKdT9ZbLXNzjEs87GAoErQXDhvSb5qaGUaXujvLjnuDd3CyBrXZY\nai6NR4s6SYtVR8ZXFIRbjB5JQ5tli6RB06JsvUEYFremB0OLeiaSHR/pQsz+TNTOC8hiTEYuNbHQ\n7pB1sSLcaRjWGN0G2BndDScSd4a1Ha6ozgoPlRt2qMMGUFrZTFfX4hlnAGLl8EUtAhb4X9WXqyNe\nJ8wnl2s2qfEWIVEDozsGSR5d7du9aS0YFgDVSTlNMAEzhGzq9GsWAa5ibiCUYc/laea6ETSjVqU0\nzIfYUudhxX+KM0VY1n4rH2THf31USmISNfcTymQFryNxY7Yaalxyy82qsSgxU2d8FUF2EQ0WuOEX\nZ8YxDyC0333ySRcFq0399ondDTIEFiYncPiPLSzeXU/AOIckfjCSx3t8HeU3MxejH6V+Eu1D68Gw\nCjqK7dZWFRkko14w3c/ATyfRfJGUnslXK6mAY1UvlttyEhq5MSA9axizjstYjtOd7VjaT0r8JTSG\nAGFU36TDL2peDE2MHocmzSk2IaNs05gGX5PU0S+qJnlrMun0fiEpAy7Y40PK9UttXLoG44unPA7W\nL7FGK7WJP6DPAQB/12yvdlov/OPanT/lzNW270XrwbCC4R2XQUV1h4MfjIlyxruhULqkC6jpLgqy\nBYTiC7fVS8PHb6GWG17OZ1W/rhss21ZUo8FTwwZSooDhtFGrM7Fj0pRKnsEUWBFfo+bvJFqpKm7h\nN0Mrne7a1bPKuaMpOU2yFk+8DhixO7P1fRuwwTwkQJbZT/dGA+Q0pu+DJqsDG4fSb95NwN2k6nQD\nj520HgyrZLa5AXULT/bJJdbBY27GEWrIJmu88Qp0kDjXwAUEflqRwtWlNPbMMkQ0qni5dUxKUrM+\nP2hupKw1RKlDQjHHTuT4mkci3E/QdvPuDBxwg6zk/UMFh3QMA9CYsi8W0gplxDNCPatDgN5v1Rir\n4z5zdtmqDm01wopHn0dtEbOe6du9oq3kPdu4tWcHLIr3aJGzg5kV1oRhrWM1wTrYoKrPdHuxyBIK\nIBZqcZr2Ft6EntIDAXmdpBFm+QsmsXWHmgtmNSrUsSKDxBo6Z47WLFH1VQ++5GWi6jrVTK8GO9db\nbYWIdyuPmRJULSnbwqiaKYnh4Cc8CmlMRU8LGoaWRt+Y1I/+sW2i3NsSI6JtUrS2aNc2WfncQ4P2\n7DYmu8OAEwEJBCwrPbOt4rg0VbyiP0zpxdWNbBDCAItixM7o6okmXdXn3lljxraydbVdH+t28vmd\n1F9qa4W6uhTctdQtdWhM1tRnqlIlVbV4AokSeDIGu2MAJA200RveG6o2Ez1vf9Rk4hAIJnF3Tsyo\n+B1uqTA8wwhsNQp2MtWkZtguKj/b3gmTKbO38/WkLPMGcQMnjAGJqT1hwAi9lCLue91pZLVOi+os\njsWKu8n6SRF86JGmoiYxhZYUXj0xREq1ImbdMxsEMRVffURLN3At7BpSTT14sSqZzL01CmjswdDB\nBkt58Hu54964qiV8w8q+uC5JS7GE82AacxmGIie1QcGZvMOfVSQ3o6J7ToNgdP0pXZ902Df7KMbU\n37fzWEDLFdmL1oNhE2ep70Vgn1dKMtng5orNJrFkhQG1Fq8yMmUGyyHoIUPXOWnMiHeaeyjGsS0O\nVKUMJdtmKyRkkjQT7ZyqfcWKnHVSTZuqVq8yGBGrCBeT0jEko+W2Gm/1z4j3aHh3eyXPQESRKulS\nCma1L24vqLJ078aYJeIkTywJoyJ3VbtJGDfrtGHXtmlVxt3hQPbNPrQWDOvjN3GyhwEhaSH7jNRY\nQYALYVfFaa0WxrGyKO1YdoKSPdurtrBeI72w3bBNCCAzG7PqiatXu990ACdwICJz/fzooj/B1KOh\nFgI/aneR1iiKEdGwNgknuLjCcrR79T0c4WhrtlIG0yrb2+bJyLJOAVPiySKoVq/REH0ITbusjGU2\nJI7shGz9ZL5WOkwFbnusyCAifyYiH/Hv94rIo2Kl4X9bRDb9+Cn//pT//qZDPsH9ia5ac2FdH3GK\nQnEFtGR0aBr/VzY2G04yhrNfzCHeq/LOgi3kxsbjKNQlMJq0WCysZKfEwHqSSyw5adJGJm6ucHuF\nUdUfj7JE8Tf6betoKw7GsflXx3xPLDSc+bWS59QK49IYM4pdNEkvTKECnNoQTm8Im0NuMtWYyd/T\nmNQhiHUtvuZzByTqJWbAoEj+6TPBXCmxg5HrlJn3okMzLPBzwOPd918EfllV/wbwPPAzfvxngOf9\n+C/7eQdSr776kkDpB6xQfOeRttiwdUbU1TdcVLv7kvdwb5MVcIgOCkzKQCRUiyfS5B5UtVV9Cfgx\nVmXLi0VMDSq777gUgyYrXRxJLjE4UVUxC7K5lojfo8Li9qhsu8peVm0LEuPZVViqsF2FZb5z69u+\nnGcdseJj28q4bfcyI06AgSVifmJf7cDo908mU7LuQncsxW9ngEU7V32uE83nIxDjuR8dimFF5G7g\n7wG/7t8F+DvA7/opvwH8r/75J/w7/vu7ZNXPtAupj1BgtbZuqb14nLc6sxt/rrpTGrjX6mU2tXYr\nRmmfowO1E6XFl4EXP+zZWKPC9jKkTC/FWjDC2kDHKF6v1pNcltXcUhEcqB6osAibumRVlhrJKiUZ\nPTbIiFKfY6XtI9v3Z0q+JpFjgl+5Ci9dUbaXFpUbI03RBUfkLqR3wft31dW2y0CitR8jJpI9+qWH\nMJI1ChKT7UmHxbD/GvgXwC3+/TzwHVX1DduzLDx0JeNVdSkiL/j53+pvKF3J+DOb7SUidVAU6lgp\ng2/aAI4zpzM68Ffka64aYP0+p6PHDwqNyc1IaysXLG9TGQbfryq9Ffg9KtvLkPBTKdOW9XjrumcH\nUwbDqRd7q2rO+VjdGgwWNWx7tNOkUkuzRCRzEIKzlMgmI6V2YHFxTFqRDIqolP4h0JxkQOOh0EVN\nk+jkl2kbp7cMUbJrcKCSfvCDGPYwBY3/PvCMqn7yoHOvhVT1Q6r6iKo+srkRNk57U9tRJlSSgadl\nZtuH6hlsUMJzUPvZ3zozNjmrHkWqpUlu+ystTVEa3kOB0dc9AaiwdFXfw4DYqshWuzrTB+PGmqza\nYEgkPatGNpfkfSzAYbkPsSpAVe3ergHqWBlrbVsWeTAg4ZNGPa2S/lzv84mmEi0ULSto0jPEtO+f\nqZaw8YkxaH3T41BVLxFVW9GTGJImnFyj+Jy3RZr7K+PDFjT+ByLybuA08Brg3wC3isjCpWxfFj5K\nxj8tIgvgHPDtfZ/gUqV0EiF8oDJq4kRBqV5O0/bXsqITSmcEVLzocXtxKdiCQV/n1deOApss42gZ\n9GBx/OIJIBoeC8RcPZ7P0PamcmmrilVb6fBsZzym4acRUeoWTKbHItR2i7kDns9gnWNME/DD+mBM\niT41+jI/IfqmU05Vxb+7QdsJyz5/NqWonzu4D3jA95INSd5RemC0sfSINMziB60STZPAUYRjPzpQ\nwqrqB1X1blV9E/Ae4L+r6j8C/gT4ST/tvUxLxr/XP/+kn79vM0IVJjNFmchYhpJXx+ceD/WrD1yy\nVbuhxN1Vc4bXEerSmGkcYbmtWee1OqaNJSq6wjjjWCZBCvNaBCSwPwtyNAMw3jAd5N6+lizd1a+q\nBoNMUpmlH2o8vShutdsyF0ljLST6ytjl/7HUKKV02Aba9ZWqw5i+h+2dLCmnvQN0TN7dI9YRS9ce\nde8PLnE1XCLOvJMePMDauRYvwSr9AvB+EXkKw6gf9uMfBs778ffTNpzblxKIj5rWvDnmGwO0HVCa\nektmhyaVAfEsq2L2v5fNDLWlthLWmSIHRSLaVqwMeq6zagw3scwDl+7Y40Cn/3ewRbw94UYKd1Xi\nW5d+uaHH2KzsSKJpcKNPqtnLElefFGYM2QqBwuBMH0HdZO5sMAk8DT54FNCDjVWB0RnUfxMXFBEa\nxt+vEr/ZGJXw2Hg/5JarMdz70DUFDlT1f2AbyKGqfwn88C7nXAH+t2u5r13nFa3V9psdxPYpME1S\ns1qeRb9CmplKMulRcwv3WEoj7mgPR/9QFMaSBeFsQeLCVZh5D7auCqdPm+81GHK5DcOStO6qbxPe\nqhLku/sHQH1lg5h7rI4tBLr0jK/qPlN1rWEYzrO1fAmJ5ELYht3jfHumTKpih1GlykQbRBpmY04N\nOc0SS2KPHIMqbhw544VwVHfS9RNkQNqKhwnsiIhgU/upLCWe3kLYKWwOwARrEekKVS0og5cDCgM7\n6mu1QW0ldMALAVdFBhLPGnXVRvtOIWCEVTrx+Z+S2XhCGYYF47hERBgW5DKdcdnabGKiTJ6hgVH9\nswhI9RWmagyWVcEdUkwYvUZRCs/zdW4JX+2qmrd0zGCieAG/nZc7CpdRY+pQ7c7M8WxtfRZJjoQi\nyj5ltUQsdMZYFtIQncCCHg/nipHUBHkGB9F6MCw2oyPkWmukuIGKp177y9tS7N5901UskbDyE1Zl\neDbdTzGTW2+TmBNYLDxRZFx2a8laxnzcM4wQ53A7qzeUHK6kbzHaq6ElaAwZ91dX2XRW+IRZpb2L\na5HYDimXbrsIU+fgNAB3YYZ0dWkLeYeNEO48l+nZ5pDyQvjAYwK1LmkQSLpn0b6nfdFoFc7sRWvB\nsP7KielKaVIjpJ5qq+eEeiLHUNrVVaiDZqdX6asWNqOslTvXjpENUC02bDvOMsCpUzAUuHSx4y4A\nqV1oMYyhaU5u/J+rSPvREMvGKnVqmPQeBcYGJQzNRIpj32mK1JL3bku6JbXEqk80J1v0hrRk+CgG\nlyUbss/cYE0tN/WyxJIw82eHN0d8wWKEie1ZmcGW/dn69SBGDVoLhoXWma50iDi5yLRGUw5MkexI\n+03R0cS0FMNCJnFbGU5j2E6ySKwTU2wrevcZlsrmmWLb2dvVCUkCazXqGZjJAr/p4CulDF4pXM3H\nWk1jpDPdXVS12wU1VT2dNIsSQTRN0k17Y66JT7pvRzt38kttWqiXyG0i7FPoIiU5bcMUkVye1N+v\nTei41oWOY+idZZ6mtB4Mq1Bsh9e0SMMAGMfK4IzVM1jFol7iI1YK9MsAEo+uPKhZzsJiQ1gMkfjh\n0QFPdtlYCC9tW32C1Pz0UMInVpi+rnqtOZ0qdChRq2UniMbzphJwgkNX+Er8hcrQJpu6YSSBZRti\nz/fLNnRM0kvZmLzxyCbvJM+P8/rvDXNOvxf3rMQzp0vvp5Cp9WloSBPVJwISgDGo4mBdfHlJNYbR\ntG1CUkm6PwT1Wk4NhxKDOlH7rpaKJZRYqaBKGaQL39q5gwg6KleuaJedH4Me95rq2j7hJlrmP7aB\ncuYlMHck92QyjDaVTmcc+bNKZ6QpvukzVi1mnELGiYfAm9h9l+537c5fvXYn98ThtrGJaxyhuaSS\nsfvnd58jYLLLOQf5YdeKYX2ppuUThDQhsJJQFh2WJJhcHOLppLIh7MRsYf2UArqtVka9VqSYw2Zc\nAmpur62tka2XeigwvacNXKtxJXj0qCqx1j8wXs2EAHHmbtKu3y4JLbYUHRyDhkFZcjqKpys2Ke3R\nMYdPsbdt9lFi+NY3q5PZYEk7bze1HDUxdIUZxQ2sEsas32fpUbdpIeP+jitjuSLN96K1YViwmqtF\nm3El0tRKqM/GLAK5X6krZ09cqVUZhmLr6nsJR6GOymJoe50W9wREzm0ZFAa4cgWWS2nPoQ3SOIYL\nrDEyLpWt4FohDLku3uafHF/7wQi1Zn5rnOswKSR8Xu+h27DSG8aVSTJ5zwEBZ1YlZhjzKXdlZ3KK\n3apBHuuH9hu0YaDzK0v1AJC706Zwwtq3m/rXru92o+8l0nX9SMm9oNoLkX9hbFUPmmt1A8t/j62P\nIiIFZCIJNCYXMUYWMb/qUNqWR1tXLYf11GlAlO1t0rXT2mV5hsauZfIc9fq0ZcAqIHa+Y3GusIHr\ncnX9n4AztsO3JdJoltmW7hmRHOLGUf/8uF9Iurxro7iS7rk5iaLPJ79HJE59ktixEt3SjREescuC\nIa5dJrBV23X95/a7dHkMu9PaSNiigi5pPlbHSSHJwI8txf2jCkvQwVP8xGemtllqEqPL7lJlGCoM\nBguGwdbhX90u1FHY2FQWUjzjv6T0tQkFUbU6qHdl6UQVG7yJbZqymiB4okqfOGP/RY2sYJzYwXyp\nmTjYVi90z8x+0ZophtULGxf1SomdJIwJniaBz2aZtAsXciYdMzi+IhGnxleT6ukzTjujaal2D820\n4zHGTJTlchex29HaMGysGBDxDlWP+KhH+535lgJabDWsUM16xrKIwlWlVRk2ZDKgxW+iWHFjGWCx\nELa3rLbpxqZy6pR5JS5fVZZbrs5Vc9ukcawIXQKMjEwy5L2vS1QxFHOxjWoBAV06JhdjJlUYHUog\nscQ6MU8yddTiKgjbiY/NQKmjGLQJxgt3l4swwTd4ownDQgvSNEmpbf74ZnUqsCHOgHGKtLyVGBfL\nwdCEaqE5ulR282j4cVV8JW6vGSwT7tuX9ueTtYAEofInYceAA1VSSoFrn9okWs3O1IkE7F0qGXAQ\nU9fFXVfLJYzLAZHKxkKQUlmOVpdAa0vQCMt9yG3hNX3DfTZV90ZEHoIlgYfAsvPEVw/UhAyOWaWp\n8WlGlz112eWywgrelJZxJcBQ2kYj2XE0td76vos6qXiKZQcfoh97DKpNOLe2tmhk4t0KeJTOP3qx\n4y57zeYbBbh8VXj+pEhYsBdYLqtvEd9hNcdvWcoIYGzx/Qjr1qo5A8elqf22pZR6uXarTVoUtq+K\nR9UsMUbHwtaWMi41ihMgCzV1FZ3ralIMlKa0dfOuSwyXZDqDA0IEa3EJhmsDdQwYZFlZpDSCloAy\nxZlRSiQkcgvd5mZuBIPHRIYmvbtbZbvziMGHbmysbaSolizEvCosmsAB+0kG8fTJ/qC13bSn8K3L\nq6t9d9J6MKxgxkQM0NKNho3oaHVXl6vaUSw9cNP26ZIijINSakXdLRVxgIJJ1WGwNVC12v4CdYy6\nrMLG5kDVkeWWsLyqMJpxVatBjShMV0cPt/puhea0D2s9yv9o+nnbwBVPgDGmXkIufhw9OXsVm9qU\niIwxQap5F6roRERGoEUm0MQ2l64REcvfYuVFU+/i/R/SMfyxfVChp7xOZIXBjdpOMKY2iltotStu\nDCBRQl9tsnz7ElxaxmzYm9aDYTs1oi6FajUALmLYTRJL2TERpW5BHdSMKAHba6qTIpU+o8P9i5WN\nAZZLH+xiaYV1CVcvd2pcQuK7ld+7C9QAmoi5nQp4Fe6+9GWDDNEeKHmOubKqw6HaGDZXMjj0oKlZ\nVDNtsog0xnAq2hJuxHFy+OhVycT0oHC39l6w/t3Bch6A3DnH1LdpO4l7dAk7vWRdhEYLo07b+6Tn\nBri0Bd+6AiqOwXeZKEFrwbAmkaKRXsyh2kttb8Fiw4ymReBGfFAUDzhYb1apiETaoHe+Rt1CV50q\nbG/ZcplhYdb/9lZhe3ukLktaBpMsJPFaqUjuB2bWdiYBkNPCebtEbFy71bLVEkxsvVowq1+gbS7E\nc+O+0oWcCfXbgGe2obnIoqaWJGNYk1eYobsUXy+Xjnvt/qLPxdqSk0osLTTyxRq0EZfCeB/EZs7d\no71fl6PwrZcsJ3eKrnentWDY7QpbV4XN0xGeteOiypA7A5ouExzfagodk2jiM9RXvIa6KhWivHYd\njbGHTaueMlaLTuEqXLyUe9sko7ZkF5UEcP3uMvGcVLse9bEiH8aYpvZNFYdEinCvdMwyJYMlCuYx\nibVhKvRhVGDyvQVYWkklIhGIZsF7U9PbFEWPiy2HmPyGXxOejQjLjv0YxHtpw/exoMOk+3RCxkT6\n9ktw0ZOMDopywZow7KjwjReEC5vCIDUTk6uYdGw18QsL34gjrXWRlFJSYmvMTtJWkKWgvgARiu0M\nA15lxZPEQ26LkCFcKbatkruaFCxTqsNwpJur2vIeiGBUrmqo4cPVSJYeUQ/rSucysrKdg92LaT4E\nBBPuxJY7MqBoSToh2TKxaNWZn15UZdGp+nB8rEr9ST6Cu6oqtnp3sTLntHr0T6foJbDypavwrS1z\n7e0Aw3vQWjCsAl9/Sbnlu3DbrVg8unRJw5VWmqzSHNKlDUyttslxdaBVSqthUAGq4ceNhWUUbV8N\njExKt+rMa8uSYyBXkkhGl7jiNVk7VWgDXSxqV6EuO4yXg21GUMIZJBfoiSfHiGN4bTUD7dnahYI1\nzFGbvIk5AdwQrGMLAffX5GIdZxywtkhkvE1Ea9NW/YQI+GFS35h9t4nU8XfiW1Fhawu+/pKwHeGO\nw4hX1oRhAa5q5cvfKWwMwtmb3Pp3KSWhzmql+hKZ2Fk70wIxhpPim3CkBaq27t3dRLVCvWp5ArVG\n0oYNevUSmOLx2pquIbtZGAnxIXfLXtbc0qhWRWNlrktWU+u0uL2HMFVDGivU4vvZ2gBm8bXWdBI4\nAqbuTRPRHXUI7osOpypb/F129wS0KNoE92qfSdZ4v58Jq5lh2dQgWblOhW+8VLmsvrLhGmhtGBaB\nF1X5ygtw36KYXxSQwY2H6uuMRHNt1KKAZja+bbNZ1LKPBsQ2PB7I3hOBcRyBkj7SEWWBol7tYVSB\nccxurLUxq6pmEMI+B3Nplj0KtR9ZUIEFgynbnqvi4rcFA5r6bztrQ8CfgBMm0TSuwd5DQuK17rTQ\nMMlWDWczTYfs8W//3aR/fJfut73Vd0yEXmCmAekS9pnLygvjHjc4gNaGYSOG/vw2PP28ct9gmVOb\njgliN5RSaL69OlKWvkxYCpSKamUohinDWDJjQnI3w1qjkooBtS0FPNlFRGwfLWmDEx3e8gKMIcca\n7dBpQQkVilcUpAskNE9Aw2xmOVvegUmwTpo7kFyEUZUM3OAANSYEuS+YeCxf+pUJ+S47ma1nrqx9\nsHL+blKU1qQdn3c8w3/79lV4ZgvfJOXaaS1Cs0k+js9cqTz9bbGo03bv/oHUS54IUv23lCweKl0s\nHL+mHo9B6CzqCjoq49Iytcal5H1j0EYPLmhitpA0NoDjUlspI7+nFesgC0bEyohUs+mzDWe6+4z9\nvIwNxP9dXDPLAUEzcSMP8AAADqhJREFU2np8WYPpvat06qKD3TwAnfege/7qqgVDJPuxWX9++z/u\n/eIV5ZkrvhfDPnfZj2Q/J+0rRSJyEXjiuNvh9FpWCtcdI61LW17pdtyjqq/b7Yd1gQRPqOojx90I\nABH5xNyW9WwHrBskmGmmA2hm2JlOFK0Lw37ouBvQ0dyWnbQu7VgPo2ummQ5L6yJhZ5rpUDQz7Ewn\nio6dYUXkx0XkCbF9vQ5V/Ph7eNYFEfkTEXlMRD4nIj/nx28XkT8WkSf9/9v8uIjIr3jbPiMibz+C\nNh3x/meHbsetIvK7IvJ5EXlcRN55nP2yJ/Ux7Ff6D8se/CJwH7AJ/Dnw4BE+7w7g7f75FuALwIPA\nvwI+4Mc/APyif3438F+xwMw7gEePoE3vB/4j8BH//jvAe/zzrwH/xD//U+DX/PN7gN++zu34DeAf\n++dN4Nbj7Jc923nMDPtO4I+67x8EPvgKPv/3gR/Domx3+LE7sEAGwL8Dfqo7P8+7Ts+/G/gotufZ\nR5wBvgUsVvsH+CPgnf554efJdWrHOeBLq/c7rn7Z7++4IUHu6eXU7/d1pOQq9W3Ao8AbVPXr/tM3\ngDe8Qu2L/c8iderQ+58Bsf/Z9aB7gWeB/+Dw5NdF5CaOr1/2pONm2GMhEbkZ+D3g51X1u/1vaiLj\nyH19ckT7n71MWgBvB35VVd8GXGJlM5VXql8OouNm2NjTK6jf7+tISEQ2MGb9TVX9z374myJyh/9+\nB/DMK9C+2P/sy8BvYbAg9z/b5XnZFjns/meHp6eBp1X1Uf/+uxgDH0e/7EvHzbAfB+53y3gTMyb+\n4KgeJpZg+mHgcVX9pe6nfm+x9zLdc+yn3Sp+B/BCpyK/J9JXYP+za2jLN4CvicgDfuhdwGMcQ78c\nprHHbXi9G7PWvwj8yyN+1t/G1NpngE/737sxLPhR4EngvwG3+/kC/Ftv22eBR46oXT9K8xLcB/xP\n4CngPwGn/Php//6U/37fdW7DQ8AnvG/+C3DbcffLbn9zaHamE0VHAgleyWDATDcWXXcJKyIDpuJ/\nDAPzH8d8do9d1wfNdEPSUUjYHwaeUtW/VNUtzAL+iSN4zkw3IB3FEpndnMp/c/UkEXkf8D7/+jDA\n6dOn2d7e9qXYM71aSUQ4e/Ysly7tWb34W7pua7pU9UN4YrCI6F133cWdd97JJz7xieNq0g1PFy5c\n4JZbbuGuu+7i3LlzPPvss5w+fZrTp0/z5S9/mQsXLrC9vc1iYWyzXC45deoUIsLly5f56le/yunT\np3nggQe4ePEijz/+OF/60pd2PEdVef3rX8/m5iZPPLHr2tOv7NXGo2DYa3Yql1K47777+NM//VNm\nr8Xx0YULFxjHke///u9nuVxy8803c+7cOUopXL58mbvuuovnn3+e22+/nVOnTqGqPPbYY/zAD/wA\nW1tbAJw/f57Xv/71XLp0ie3t7T2f9eUvf5mHH36Y22+/neeee+7QbTwKo2uBGV3vwhj148A/VNXP\n7XXNLbfcoiLCxYsXV+/lxd4O2FpkputCb3zjG3nLW97CxsYG999/P88++yzPPfcct956K+M4cvbs\nWb761a+ysbHB2bNnJxDu3LlzvPjiizz33HMsFgu+853v8KlPfYrlcrnn8zY3N3nHO97Bxz72sVXm\n/qTusUr3uktYVV2KyD/DsosG4N/vx6xg5YMuX7684/jtt9/Om9/8Zj72sY9d72bOtAstFgv+4i/+\ngm9+85s8/vjjXLx4MQXGMAycP3+ev/qrv+Lmm2/m0qVLnD17llIKly5d4syZM2xubvLd736XK1eu\nsLW1tS+zAmxtbfH444/z9re/nUcffXTfc4PWInAgk30wpzQMw2yEvcrpB3/wB7l48SJf/epX49Ce\nEva4cwkOpJlZX/30+c9/nnvuuYfNzc0Dz117hp3p1U/jOPLEE0/wlre85cBzZ4adaS3omWeeoZTC\na17zmn3Pmxl2prWhL3zhCzz00EP7njMz7ExrQy+99BJ//dd/ve85M8POtFb01FNP7fv7zLAznSia\nGXamE0Uzw850omhm2JkOTcMw8CM/8iOcO3fu2Nqw9qHZmdaLbr75Zi5fvnzUEchXLvllplc3vfji\ni8f6/BkSzHSi6FXDsBsbG7zrXe/KbPiZXp30qmFYVeVrX/vanN31KqfZ6JppHenk5sPONFNPM8PO\ndKJoZtiZThTNDPs90sbGxoFJxzNdP5oZ9nukjY0NbrvttuNuxg1Ds5dgpnWkV5+X4PTp02xsbBx3\nM2Z6henEMuy9997LG97whoNPnOlVRTMkmGkd6eVDgnXc7nKmw9GpU6e47777jrsZ15UOAwmWwD9X\n1QexbRp/VkQexPZx+qiq3o9t3BCl4f8ucL//vQ/41eve6pkORcvlkm9/+3rtjLQm9DJ2G7nu211i\nO7vMf/Nf/H1iL165JqNrTba7nOkGpkMnj65ud2l7tBmpql6r4bRSMn6mmQ5Fh5KwR7Hdpap+SFUf\n2csanGmm3egwXoK12e5yppnWYrtLjh/kz3/r9ben0TUHDmZaR3r15RLMdGPSzLAznSiaGXamE0Uz\nw850omhm2JlOFM0MuyZ0+vRpLly4cPCJNzjNDLsm9MY3vpEHHnjgUHtV3cg0M+ya0Ne+9jU2NjYO\n3O7yRqc5cLBGdObMmV333L0BaQ4cnASamfVgmhl2H9rY2ODWW2897mYcKZ06deq4m3BNNDPsPnTP\nPfdwzz33HHczjowWiwV33HHHcTfjmmjGsPuQiLAO/XMD0oxhXw7NzLp+dEMz7Llz5zh79uwr/tyN\njY25CMjLpBuaYW+99VZuuummY3n2XNr+5dGMYWdaR5ox7EyvDpoZdqYTRTPDznSiaGbYmU4UzQw7\n04mimWFnOlE0M+xMJ4pmhp3pRNGrkmHPnz9PKa/KV7vh6dCjKiKDiPyZiHzEv98rIo96afjfFpFN\nP37Kvz/lv7/paJq+O5VSePOb33zi8jxnOhxdixj6OeDx7vsvAr+sqn8DeB74GT/+M8DzfvyX/bxX\njGqtfOxjH5uz91+tdMgy8XdjlQr/DvARrELht4CF//5O4I/88x8B7/TPCz9P5uqF8981/H3PJeP/\nNfAvgOrfzwPfUdVY4tmXhc+S8f77C37+hETkfSLyCRH5xCHbMNNMhypo/PeBZ1T1k9fzwXMF7ple\nDh1mj4O/BfwDEXk3cBp4DfBvgFtFZOFStC8LHyXjnxaRBXAOeJXtvTPTcdGBElZVP6iqd6vqm4D3\nAP9dVf8R8CfAT/pp72VaMv69/vkn/Xy9rq2e6Yal78VZ+QvA+0XkKQyjftiPfxg478ffT9twbqaZ\nvmeaVxzsQqdPn+aBBx7gz//8z4+7KTcq7bniYGbYPWixWMx1ro6P5iUy10ozs64nzQw704mimWH3\noZtvvvlY6hbMtDfNDLsPvfWtb2UYhuNuxkwdzQy7D21ubs5JNGtGM8PuQaUUrl69Ohtfa0Yzw+5B\nd955J88888zBJ67QnDh+tHRD9e6ZM2d405vedKhzT506xVe+8pVrfsbDDz/M3Xfffc3XzXQ4Okzy\ny6uKRORQ533xi198Wff/9Kc/PRd6O0KaI10zrRX5xiRzpGum9afTp0/z1re+dd9zZoadaW3o/vvv\n57HHHtv3nJlhZ1oLunDhAuM4cvHixX3Pmxl2pmOnxWLB6173Op544okDz50ZdqZjpx/6oR/i6aef\nPpR3ZS0Ydk4wuXHpta99LSJy6CDNWjCsqs67qtyAdPr0aR588EE+97nPHfqatWDYK1euzFuv34D0\n0EMP8dnPfparV68e+pq1YFhV5fHHH+dtb3vbkcbiSyk8+OCDnD+/o67HTK8w3XnnnTz33HM8//zz\n13TdWjAswLPPPst3vvMd7r333iN7Rq2V559/nm9/ey6TcJx00003cc8997ys8PfaMCzAk08+yfnz\n5/m+7/u+I3vG17/+9SO790wH09mzZ3nkkUdeds7FWjFsrZU/+7M/44EHHji2HQpnOjra2Njg4Ycf\n5lOf+tTLToxfK4YF2N7e5pOf/CQPPfTQcTdlputMjzzyCE8++eSB0az9aF2ytS4CB4c5Xhl6LVYi\ndB1oXdrySrfjHlV93W4/rEs+7BPrUsVQRD4xt2U92wFrCAlmmmk/mhl2phNF68KwHzruBnQ0t2Un\nrUs71sPommmmw9K6SNiZZjoUzQw704miY2dYEflxEXnCN6I70mrdInJBRP5ERB4Tkc+JyM/58dtF\n5I9F5En//zY/LiLyK962z4jI24+gTWuxYZ+I3CoivysinxeRx0XkncfZL3vSYfbpOqo/YAC+CNwH\nbAJ/Djx4hM+7A3i7f74F+ALwIPCvgA/48Q8Av+if3w38V2xfsncAjx5Bm94P/EfgI/79d4D3+Odf\nA/6Jf/6nwK/55/cAv32d2/EbwD/2z5vArcfZL3u285gZNjek8+8fBD74Cj7/94Efw6Jsd/ixO7BA\nBsC/A36qOz/Pu07PP9IN+66hHeeAL63e77j6Zb+/44YEuQmdU79B3ZGSq9S3AY8Cb1DVSOP6BhDL\nH466fdd9w76XSfcCzwL/weHJr4vITRxfv+xJx82wx0IicjPwe8DPq+p3+9/URMaR+/qOasO+l0kL\n4O3Ar6rq24BLrOz+80r1y0F03Awbm9AF9RvUHQmJyAbGrL+pqv/ZD39TRO7w3+8AYkXcUbYvNuz7\nMvBbGCzIDft2eV625Qg27HsaeFpVH/Xvv4sx8HH0y7503Az7ceB+t4w3MWPiD47qYWKV4D4MPK6q\nv9T91G+Gt7pJ3k+7VfwO4IVORX5PpGu0YZ+qfgP4mog84IfeBTzGMfTLYRp73IbXuzFr/YvAvzzi\nZ/1tTK19Bvi0/70bw4IfBZ4E/htwu58vwL/1tn0WeOSI2vWjNC/BfcD/BJ4C/hNwyo+f9u9P+e/3\nXec2PAR8wvvmvwC3HXe/7PY3h2ZnOlF03JBgppmuiWaGnelE0cywM50omhl2phNFM8POdKJoZtiZ\nThTNDDvTiaL/H74I/xMP9tHjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Mqfjqb6606L",
        "colab_type": "text"
      },
      "source": [
        "I have plotted several random images from both classess. It is clearly visible that the nerves are more visible in the images which belong to good category. Intuitively also, this is correct because an image will of good quality only when the information contained in it is clearly visible. With images of the bad quality, this is not the case.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "38ttySPsT07l",
        "colab_type": "code",
        "outputId": "6a2e7943-2b83-4710-e38e-47e093e19f25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "img = cv2.imread(path1+ 'drimdb_good (66).jpg' ,1)\n",
        "edges = cv2.Canny(img,100,300)\n",
        "plt.imshow(cv2.cvtColor(edges, cv2.COLOR_BGR2RGB))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fceb5b2bcc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ADURPtfPMLM0vKgSO+vZRYASAX94fqO+WakVEQhDLXe/BZpbn25lAObCHaGDe44dVAKt8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\npYhIAAWliEiA/w+gkUZ0rLW4VgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDIn3pZq9r40",
        "colab_type": "text"
      },
      "source": [
        "In this case, we have changed the values in the argument of Canny detector. This is done so that we can only identify the boundary of the ROI of any given image."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kI4GKKKTD5_K",
        "colab_type": "code",
        "outputId": "0af151f9-7bff-46f4-ec72-cc4f49aa03a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "edge_canny=[]\n",
        "for i in range(len(data_good)):\n",
        "  img=data[i,:,:,:]\n",
        "  edges = cv2.Canny(img, 100,300)\n",
        "  edge_canny.append(edges)\n",
        "\n",
        "edge_canny=np.array(edge_canny)\n",
        "print(edge_canny.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(125, 512, 512)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PXyPZHlELTi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "avg=np.mean(edge_canny,axis=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1Tu8QNfEcxp",
        "colab_type": "code",
        "outputId": "bacea27a-6d03-4582-8bc9-59bd52528b33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "avg.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YiuxCT4QIVr",
        "colab_type": "code",
        "outputId": "92f72252-8be7-418d-b1ca-86ea234377cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        }
      },
      "source": [
        "cv2_imshow(avg)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7ZFhF3hPkSLa7b4b1bcmohsQucBtX0AyNAAQ7sSVpd2K2rkmbqcUSYc8SoV9G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28OjV5posDQ4kZtZY8a4Dj9lCGmiLqItCWKqi0Jl0zEkMhqCloZIxDRDkf+XA3CHGKc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\nFJ0BoOLPqP+fsgBGCYR8ANXp/NXAcFL5wuZjda2e8q9pCj/f9gPInl81k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l3tvkIJ4nXb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\ngQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=512x512 at 0x7FCEB5BF15F8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmwEuqfDQbOF",
        "colab_type": "text"
      },
      "source": [
        " It can be clearly seen that there exists only 1 boundary, taken in average sense. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oFTtJx_pEqjX",
        "colab_type": "code",
        "outputId": "983bcc14-0602-455e-f062-8f2bf38ce096",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "count = 0 \n",
        "for i in range(512):\n",
        "  for j in range(512):\n",
        "    if avg[i,j] > 70:\n",
        "      count=count+1\n",
        "print(count)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "705\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G-tgt_k5FKKp",
        "colab_type": "text"
      },
      "source": [
        "There are only 705 pixels whose value is greater than 70"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wRvI_TFERQNX",
        "colab_type": "text"
      },
      "source": [
        "# Task 1 Complete\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b4xuE06iR_Mx",
        "colab_type": "text"
      },
      "source": [
        "# Task 2 Relevant data loaders"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z1GBlDEDpuKR",
        "colab_type": "text"
      },
      "source": [
        "In this task, I have defined my own data loader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDR0DV2cEuLp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def LoadDataset(data_path):\n",
        "  label=0\n",
        "  data_path1=['Bad', 'Good']\n",
        "  X=[]\n",
        "  dict_Y={}\n",
        "  for sub in data_path1:\n",
        "      path2=os.path.join(data_path,sub)\n",
        "      for img_path in os.listdir(path2):\n",
        "        if '.jpg' in img_path:\n",
        "          img_path1=os.path.join(path2,img_path)\n",
        "          X.append(img_path1)\n",
        "          dict_Y[img_path1]=label\n",
        "      label=label+1\n",
        "  return X,dict_Y\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0B6SE6r5IPr",
        "colab_type": "text"
      },
      "source": [
        "In total,194 images are there. \n",
        "\n",
        "High -- 125\n",
        "Low -- 69 \n",
        "  \n",
        "We further randomly divided this dataset into a train (124 images), validation (31) and test (39) sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x7_9ZGrM40w-",
        "colab_type": "code",
        "outputId": "b7336dfc-bbec-4ba0-8e69-41d07c96fa37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "X,dict_Y=LoadDataset('./DRIMDB')\n",
        "random.shuffle(X)\n",
        "print(X)\n",
        "train_list=X[0:124]\n",
        "validation_list=X[124:155]\n",
        "test_list=X[155:194]\n",
        "dict_X={'train': train_list, 'validation': validation_list, 'test': test_list}"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['./DRIMDB/Bad/drimdb_bad (1).jpg', './DRIMDB/Good/drimdb_good (58).jpg', './DRIMDB/Good/drimdb_good (28).jpg', './DRIMDB/Good/drimdb_good (59).jpg', './DRIMDB/Bad/drimdb_bad (41).jpg', './DRIMDB/Bad/drimdb_bad (18).jpg', './DRIMDB/Good/drimdb_good (27).jpg', './DRIMDB/Good/drimdb_good (119).jpg', './DRIMDB/Bad/drimdb_bad (17).jpg', './DRIMDB/Bad/drimdb_bad (25).jpg', './DRIMDB/Good/drimdb_good (78).jpg', './DRIMDB/Good/drimdb_good (80).jpg', './DRIMDB/Good/drimdb_good (68).jpg', './DRIMDB/Good/drimdb_good (36).jpg', './DRIMDB/Good/drimdb_good (25).jpg', './DRIMDB/Good/drimdb_good (4).jpg', './DRIMDB/Good/drimdb_good (9).jpg', './DRIMDB/Good/drimdb_good (32).jpg', './DRIMDB/Good/drimdb_good (43).jpg', './DRIMDB/Good/drimdb_good (53).jpg', './DRIMDB/Good/drimdb_good (76).jpg', './DRIMDB/Good/drimdb_good (120).jpg', './DRIMDB/Bad/drimdb_bad (65).jpg', './DRIMDB/Good/drimdb_good (12).jpg', './DRIMDB/Bad/drimdb_bad (42).jpg', './DRIMDB/Bad/drimdb_bad (7).jpg', './DRIMDB/Bad/drimdb_bad (36).jpg', './DRIMDB/Good/drimdb_good (86).jpg', './DRIMDB/Good/drimdb_good (48).jpg', './DRIMDB/Bad/drimdb_bad (40).jpg', './DRIMDB/Bad/drimdb_bad (13).jpg', './DRIMDB/Good/drimdb_good (64).jpg', './DRIMDB/Good/drimdb_good (30).jpg', './DRIMDB/Bad/drimdb_bad (23).jpg', './DRIMDB/Good/drimdb_good (54).jpg', './DRIMDB/Good/drimdb_good (84).jpg', './DRIMDB/Bad/drimdb_bad (59).jpg', './DRIMDB/Good/drimdb_good (100).jpg', './DRIMDB/Good/drimdb_good (111).jpg', './DRIMDB/Bad/drimdb_bad (2).jpg', './DRIMDB/Good/drimdb_good (15).jpg', './DRIMDB/Good/drimdb_good (101).jpg', './DRIMDB/Bad/drimdb_bad (45).jpg', './DRIMDB/Bad/drimdb_bad (57).jpg', './DRIMDB/Good/drimdb_good (89).jpg', './DRIMDB/Bad/drimdb_bad (44).jpg', './DRIMDB/Good/drimdb_good (106).jpg', './DRIMDB/Bad/drimdb_bad (4).jpg', './DRIMDB/Good/drimdb_good (94).jpg', './DRIMDB/Good/drimdb_good (83).jpg', './DRIMDB/Bad/drimdb_bad (26).jpg', './DRIMDB/Bad/drimdb_bad (54).jpg', './DRIMDB/Good/drimdb_good (26).jpg', './DRIMDB/Good/drimdb_good (123).jpg', './DRIMDB/Bad/drimdb_bad (31).jpg', './DRIMDB/Bad/drimdb_bad (56).jpg', './DRIMDB/Good/drimdb_good (56).jpg', './DRIMDB/Bad/drimdb_bad (12).jpg', './DRIMDB/Good/drimdb_good (99).jpg', './DRIMDB/Good/drimdb_good (41).jpg', './DRIMDB/Good/drimdb_good (22).jpg', './DRIMDB/Good/drimdb_good (110).jpg', './DRIMDB/Bad/drimdb_bad (46).jpg', './DRIMDB/Good/drimdb_good (35).jpg', './DRIMDB/Bad/drimdb_bad (39).jpg', './DRIMDB/Good/drimdb_good (115).jpg', './DRIMDB/Good/drimdb_good (65).jpg', './DRIMDB/Bad/drimdb_bad (28).jpg', './DRIMDB/Good/drimdb_good (66).jpg', './DRIMDB/Good/drimdb_good (70).jpg', './DRIMDB/Bad/drimdb_bad (29).jpg', './DRIMDB/Good/drimdb_good (10).jpg', './DRIMDB/Bad/drimdb_bad (67).jpg', './DRIMDB/Bad/drimdb_bad (6).jpg', './DRIMDB/Good/drimdb_good (16).jpg', './DRIMDB/Bad/drimdb_bad (66).jpg', './DRIMDB/Good/drimdb_good (113).jpg', './DRIMDB/Good/drimdb_good (122).jpg', './DRIMDB/Bad/drimdb_bad (43).jpg', './DRIMDB/Bad/drimdb_bad (47).jpg', './DRIMDB/Bad/drimdb_bad (27).jpg', './DRIMDB/Good/drimdb_good (114).jpg', './DRIMDB/Bad/drimdb_bad (48).jpg', './DRIMDB/Good/drimdb_good (74).jpg', './DRIMDB/Good/drimdb_good (96).jpg', './DRIMDB/Good/drimdb_good (20).jpg', './DRIMDB/Good/drimdb_good (82).jpg', './DRIMDB/Good/drimdb_good (92).jpg', './DRIMDB/Bad/drimdb_bad (69).jpg', './DRIMDB/Good/drimdb_good (11).jpg', './DRIMDB/Good/drimdb_good (75).jpg', './DRIMDB/Good/drimdb_good (8).jpg', './DRIMDB/Bad/drimdb_bad (51).jpg', './DRIMDB/Good/drimdb_good (38).jpg', './DRIMDB/Good/drimdb_good (87).jpg', './DRIMDB/Bad/drimdb_bad (38).jpg', './DRIMDB/Good/drimdb_good (33).jpg', './DRIMDB/Good/drimdb_good (62).jpg', './DRIMDB/Good/drimdb_good (50).jpg', './DRIMDB/Bad/drimdb_bad (49).jpg', './DRIMDB/Good/drimdb_good (42).jpg', './DRIMDB/Good/drimdb_good (91).jpg', './DRIMDB/Bad/drimdb_bad (35).jpg', './DRIMDB/Good/drimdb_good (112).jpg', './DRIMDB/Bad/drimdb_bad (19).jpg', './DRIMDB/Good/drimdb_good (17).jpg', './DRIMDB/Good/drimdb_good (31).jpg', './DRIMDB/Good/drimdb_good (47).jpg', './DRIMDB/Good/drimdb_good (55).jpg', './DRIMDB/Bad/drimdb_bad (61).jpg', './DRIMDB/Good/drimdb_good (81).jpg', './DRIMDB/Good/drimdb_good (93).jpg', './DRIMDB/Good/drimdb_good (45).jpg', './DRIMDB/Bad/drimdb_bad (64).jpg', './DRIMDB/Good/drimdb_good (124).jpg', './DRIMDB/Bad/drimdb_bad (15).jpg', './DRIMDB/Good/drimdb_good (63).jpg', './DRIMDB/Good/drimdb_good (44).jpg', './DRIMDB/Good/drimdb_good (37).jpg', './DRIMDB/Good/drimdb_good (23).jpg', './DRIMDB/Good/drimdb_good (77).jpg', './DRIMDB/Good/drimdb_good (39).jpg', './DRIMDB/Bad/drimdb_bad (33).jpg', './DRIMDB/Good/drimdb_good (49).jpg', './DRIMDB/Good/drimdb_good (21).jpg', './DRIMDB/Good/drimdb_good (71).jpg', './DRIMDB/Good/drimdb_good (57).jpg', './DRIMDB/Bad/drimdb_bad (16).jpg', './DRIMDB/Bad/drimdb_bad (53).jpg', './DRIMDB/Good/drimdb_good (72).jpg', './DRIMDB/Bad/drimdb_bad (11).jpg', './DRIMDB/Good/drimdb_good (52).jpg', './DRIMDB/Good/drimdb_good (90).jpg', './DRIMDB/Good/drimdb_good (3).jpg', './DRIMDB/Bad/drimdb_bad (62).jpg', './DRIMDB/Good/drimdb_good (98).jpg', './DRIMDB/Good/drimdb_good (5).jpg', './DRIMDB/Good/drimdb_good (24).jpg', './DRIMDB/Good/drimdb_good (105).jpg', './DRIMDB/Bad/drimdb_bad (9).jpg', './DRIMDB/Good/drimdb_good (19).jpg', './DRIMDB/Good/drimdb_good (116).jpg', './DRIMDB/Bad/drimdb_bad (20).jpg', './DRIMDB/Good/drimdb_good (13).jpg', './DRIMDB/Bad/drimdb_bad (10).jpg', './DRIMDB/Good/drimdb_good (69).jpg', './DRIMDB/Good/drimdb_good (104).jpg', './DRIMDB/Good/drimdb_good (107).jpg', './DRIMDB/Bad/drimdb_bad (21).jpg', './DRIMDB/Good/drimdb_good (79).jpg', './DRIMDB/Bad/drimdb_bad (55).jpg', './DRIMDB/Bad/drimdb_bad (32).jpg', './DRIMDB/Good/drimdb_good (29).jpg', './DRIMDB/Good/drimdb_good (67).jpg', './DRIMDB/Good/drimdb_good (46).jpg', './DRIMDB/Bad/drimdb_bad (3).jpg', './DRIMDB/Good/drimdb_good (34).jpg', './DRIMDB/Good/drimdb_good (1).jpg', './DRIMDB/Good/drimdb_good (2).jpg', './DRIMDB/Good/drimdb_good (7).jpg', './DRIMDB/Good/drimdb_good (85).jpg', './DRIMDB/Good/drimdb_good (60).jpg', './DRIMDB/Bad/drimdb_bad (63).jpg', './DRIMDB/Good/drimdb_good (14).jpg', './DRIMDB/Good/drimdb_good (97).jpg', './DRIMDB/Good/drimdb_good (118).jpg', './DRIMDB/Good/drimdb_good (121).jpg', './DRIMDB/Bad/drimdb_bad (68).jpg', './DRIMDB/Good/drimdb_good (73).jpg', './DRIMDB/Bad/drimdb_bad (24).jpg', './DRIMDB/Bad/drimdb_bad (30).jpg', './DRIMDB/Bad/drimdb_bad (60).jpg', './DRIMDB/Good/drimdb_good (18).jpg', './DRIMDB/Bad/drimdb_bad (34).jpg', './DRIMDB/Good/drimdb_good (51).jpg', './DRIMDB/Good/drimdb_good (95).jpg', './DRIMDB/Bad/drimdb_bad (50).jpg', './DRIMDB/Good/drimdb_good (109).jpg', './DRIMDB/Bad/drimdb_bad (14).jpg', './DRIMDB/Good/drimdb_good (88).jpg', './DRIMDB/Good/drimdb_good (103).jpg', './DRIMDB/Bad/drimdb_bad (5).jpg', './DRIMDB/Bad/drimdb_bad (52).jpg', './DRIMDB/Good/drimdb_good (61).jpg', './DRIMDB/Bad/drimdb_bad (22).jpg', './DRIMDB/Bad/drimdb_bad (37).jpg', './DRIMDB/Good/drimdb_good (108).jpg', './DRIMDB/Good/drimdb_good (6).jpg', './DRIMDB/Good/drimdb_good (117).jpg', './DRIMDB/Bad/drimdb_bad (58).jpg', './DRIMDB/Good/drimdb_good (102).jpg', './DRIMDB/Bad/drimdb_bad (8).jpg', './DRIMDB/Good/drimdb_good (125).jpg', './DRIMDB/Good/drimdb_good (40).jpg']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0cw8MUg6FtB",
        "colab_type": "code",
        "outputId": "e55d5495-35bb-4ee8-9b24-319db90a018a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "print(dict_Y)\n",
        "print(dict_X)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'./DRIMDB/Bad/drimdb_bad (59).jpg': 0, './DRIMDB/Bad/drimdb_bad (41).jpg': 0, './DRIMDB/Bad/drimdb_bad (43).jpg': 0, './DRIMDB/Bad/drimdb_bad (17).jpg': 0, './DRIMDB/Bad/drimdb_bad (31).jpg': 0, './DRIMDB/Bad/drimdb_bad (39).jpg': 0, './DRIMDB/Bad/drimdb_bad (18).jpg': 0, './DRIMDB/Bad/drimdb_bad (3).jpg': 0, './DRIMDB/Bad/drimdb_bad (33).jpg': 0, './DRIMDB/Bad/drimdb_bad (32).jpg': 0, './DRIMDB/Bad/drimdb_bad (2).jpg': 0, './DRIMDB/Bad/drimdb_bad (50).jpg': 0, './DRIMDB/Bad/drimdb_bad (19).jpg': 0, './DRIMDB/Bad/drimdb_bad (28).jpg': 0, './DRIMDB/Bad/drimdb_bad (5).jpg': 0, './DRIMDB/Bad/drimdb_bad (66).jpg': 0, './DRIMDB/Bad/drimdb_bad (63).jpg': 0, './DRIMDB/Bad/drimdb_bad (37).jpg': 0, './DRIMDB/Bad/drimdb_bad (45).jpg': 0, './DRIMDB/Bad/drimdb_bad (64).jpg': 0, './DRIMDB/Bad/drimdb_bad (58).jpg': 0, './DRIMDB/Bad/drimdb_bad (62).jpg': 0, './DRIMDB/Bad/drimdb_bad (30).jpg': 0, './DRIMDB/Bad/drimdb_bad (14).jpg': 0, './DRIMDB/Bad/drimdb_bad (48).jpg': 0, './DRIMDB/Bad/drimdb_bad (60).jpg': 0, './DRIMDB/Bad/drimdb_bad (16).jpg': 0, './DRIMDB/Bad/drimdb_bad (26).jpg': 0, './DRIMDB/Bad/drimdb_bad (36).jpg': 0, './DRIMDB/Bad/drimdb_bad (34).jpg': 0, './DRIMDB/Bad/drimdb_bad (49).jpg': 0, './DRIMDB/Bad/drimdb_bad (6).jpg': 0, './DRIMDB/Bad/drimdb_bad (10).jpg': 0, './DRIMDB/Bad/drimdb_bad (57).jpg': 0, './DRIMDB/Bad/drimdb_bad (13).jpg': 0, './DRIMDB/Bad/drimdb_bad (53).jpg': 0, './DRIMDB/Bad/drimdb_bad (12).jpg': 0, './DRIMDB/Bad/drimdb_bad (8).jpg': 0, './DRIMDB/Bad/drimdb_bad (21).jpg': 0, './DRIMDB/Bad/drimdb_bad (11).jpg': 0, './DRIMDB/Bad/drimdb_bad (54).jpg': 0, './DRIMDB/Bad/drimdb_bad (56).jpg': 0, './DRIMDB/Bad/drimdb_bad (4).jpg': 0, './DRIMDB/Bad/drimdb_bad (69).jpg': 0, './DRIMDB/Bad/drimdb_bad (42).jpg': 0, './DRIMDB/Bad/drimdb_bad (46).jpg': 0, './DRIMDB/Bad/drimdb_bad (24).jpg': 0, './DRIMDB/Bad/drimdb_bad (55).jpg': 0, './DRIMDB/Bad/drimdb_bad (68).jpg': 0, './DRIMDB/Bad/drimdb_bad (27).jpg': 0, './DRIMDB/Bad/drimdb_bad (20).jpg': 0, './DRIMDB/Bad/drimdb_bad (67).jpg': 0, './DRIMDB/Bad/drimdb_bad (51).jpg': 0, './DRIMDB/Bad/drimdb_bad (29).jpg': 0, './DRIMDB/Bad/drimdb_bad (23).jpg': 0, './DRIMDB/Bad/drimdb_bad (38).jpg': 0, './DRIMDB/Bad/drimdb_bad (47).jpg': 0, './DRIMDB/Bad/drimdb_bad (9).jpg': 0, './DRIMDB/Bad/drimdb_bad (40).jpg': 0, './DRIMDB/Bad/drimdb_bad (7).jpg': 0, './DRIMDB/Bad/drimdb_bad (25).jpg': 0, './DRIMDB/Bad/drimdb_bad (15).jpg': 0, './DRIMDB/Bad/drimdb_bad (65).jpg': 0, './DRIMDB/Bad/drimdb_bad (1).jpg': 0, './DRIMDB/Bad/drimdb_bad (44).jpg': 0, './DRIMDB/Bad/drimdb_bad (22).jpg': 0, './DRIMDB/Bad/drimdb_bad (35).jpg': 0, './DRIMDB/Bad/drimdb_bad (52).jpg': 0, './DRIMDB/Bad/drimdb_bad (61).jpg': 0, './DRIMDB/Good/drimdb_good (121).jpg': 1, './DRIMDB/Good/drimdb_good (23).jpg': 1, './DRIMDB/Good/drimdb_good (6).jpg': 1, './DRIMDB/Good/drimdb_good (105).jpg': 1, './DRIMDB/Good/drimdb_good (77).jpg': 1, './DRIMDB/Good/drimdb_good (27).jpg': 1, './DRIMDB/Good/drimdb_good (117).jpg': 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'./DRIMDB/Good/drimdb_good (101).jpg': 1, './DRIMDB/Good/drimdb_good (86).jpg': 1, './DRIMDB/Good/drimdb_good (15).jpg': 1, './DRIMDB/Good/drimdb_good (10).jpg': 1, './DRIMDB/Good/drimdb_good (116).jpg': 1, './DRIMDB/Good/drimdb_good (112).jpg': 1, './DRIMDB/Good/drimdb_good (72).jpg': 1, './DRIMDB/Good/drimdb_good (125).jpg': 1, './DRIMDB/Good/drimdb_good (96).jpg': 1, './DRIMDB/Good/drimdb_good (115).jpg': 1, './DRIMDB/Good/drimdb_good (31).jpg': 1, './DRIMDB/Good/drimdb_good (55).jpg': 1, './DRIMDB/Good/drimdb_good (89).jpg': 1, './DRIMDB/Good/drimdb_good (66).jpg': 1, './DRIMDB/Good/drimdb_good (40).jpg': 1, './DRIMDB/Good/drimdb_good (3).jpg': 1, './DRIMDB/Good/drimdb_good (61).jpg': 1, './DRIMDB/Good/drimdb_good (54).jpg': 1, './DRIMDB/Good/drimdb_good (88).jpg': 1, './DRIMDB/Good/drimdb_good (91).jpg': 1, './DRIMDB/Good/drimdb_good (42).jpg': 1, './DRIMDB/Good/drimdb_good (104).jpg': 1, './DRIMDB/Good/drimdb_good (69).jpg': 1, './DRIMDB/Good/drimdb_good (35).jpg': 1, './DRIMDB/Good/drimdb_good (85).jpg': 1, './DRIMDB/Good/drimdb_good (4).jpg': 1, './DRIMDB/Good/drimdb_good (22).jpg': 1, './DRIMDB/Good/drimdb_good (102).jpg': 1, './DRIMDB/Good/drimdb_good (47).jpg': 1, './DRIMDB/Good/drimdb_good (93).jpg': 1, './DRIMDB/Good/drimdb_good (16).jpg': 1, './DRIMDB/Good/drimdb_good (53).jpg': 1, './DRIMDB/Good/drimdb_good (25).jpg': 1, './DRIMDB/Good/drimdb_good (64).jpg': 1, './DRIMDB/Good/drimdb_good (51).jpg': 1, './DRIMDB/Good/drimdb_good (50).jpg': 1, './DRIMDB/Good/drimdb_good (79).jpg': 1, './DRIMDB/Good/drimdb_good (107).jpg': 1, './DRIMDB/Good/drimdb_good (119).jpg': 1, './DRIMDB/Good/drimdb_good (114).jpg': 1, './DRIMDB/Good/drimdb_good (108).jpg': 1, './DRIMDB/Good/drimdb_good (65).jpg': 1, './DRIMDB/Good/drimdb_good (26).jpg': 1, './DRIMDB/Good/drimdb_good (71).jpg': 1, './DRIMDB/Good/drimdb_good (98).jpg': 1, './DRIMDB/Good/drimdb_good (87).jpg': 1, './DRIMDB/Good/drimdb_good (5).jpg': 1, './DRIMDB/Good/drimdb_good (82).jpg': 1, './DRIMDB/Good/drimdb_good (59).jpg': 1, './DRIMDB/Good/drimdb_good (33).jpg': 1, './DRIMDB/Good/drimdb_good (80).jpg': 1, './DRIMDB/Good/drimdb_good (45).jpg': 1, './DRIMDB/Good/drimdb_good (100).jpg': 1, './DRIMDB/Good/drimdb_good (14).jpg': 1, './DRIMDB/Good/drimdb_good (46).jpg': 1, './DRIMDB/Good/drimdb_good (81).jpg': 1, './DRIMDB/Good/drimdb_good (73).jpg': 1, './DRIMDB/Good/drimdb_good (38).jpg': 1, './DRIMDB/Good/drimdb_good (52).jpg': 1, './DRIMDB/Good/drimdb_good (124).jpg': 1, './DRIMDB/Good/drimdb_good (49).jpg': 1, './DRIMDB/Good/drimdb_good (18).jpg': 1, './DRIMDB/Good/drimdb_good (13).jpg': 1, './DRIMDB/Good/drimdb_good (48).jpg': 1, './DRIMDB/Good/drimdb_good (17).jpg': 1, './DRIMDB/Good/drimdb_good (84).jpg': 1, './DRIMDB/Good/drimdb_good (120).jpg': 1, './DRIMDB/Good/drimdb_good (76).jpg': 1, './DRIMDB/Good/drimdb_good (92).jpg': 1, './DRIMDB/Good/drimdb_good (39).jpg': 1, './DRIMDB/Good/drimdb_good (34).jpg': 1, './DRIMDB/Good/drimdb_good (19).jpg': 1, './DRIMDB/Good/drimdb_good (28).jpg': 1, './DRIMDB/Good/drimdb_good (113).jpg': 1, './DRIMDB/Good/drimdb_good (106).jpg': 1, './DRIMDB/Good/drimdb_good (9).jpg': 1, './DRIMDB/Good/drimdb_good (123).jpg': 1, './DRIMDB/Good/drimdb_good (29).jpg': 1, './DRIMDB/Good/drimdb_good (1).jpg': 1, './DRIMDB/Good/drimdb_good (60).jpg': 1, './DRIMDB/Good/drimdb_good (44).jpg': 1, './DRIMDB/Good/drimdb_good (12).jpg': 1, './DRIMDB/Good/drimdb_good (7).jpg': 1, './DRIMDB/Good/drimdb_good (63).jpg': 1, './DRIMDB/Good/drimdb_good (95).jpg': 1, './DRIMDB/Good/drimdb_good (37).jpg': 1, './DRIMDB/Good/drimdb_good (68).jpg': 1, './DRIMDB/Good/drimdb_good (109).jpg': 1, './DRIMDB/Good/drimdb_good (99).jpg': 1, './DRIMDB/Good/drimdb_good (74).jpg': 1, './DRIMDB/Good/drimdb_good (43).jpg': 1, './DRIMDB/Good/drimdb_good (97).jpg': 1, './DRIMDB/Good/drimdb_good (67).jpg': 1, './DRIMDB/Good/drimdb_good (57).jpg': 1}\n",
            "{'train': ['./DRIMDB/Bad/drimdb_bad (1).jpg', './DRIMDB/Good/drimdb_good (58).jpg', './DRIMDB/Good/drimdb_good (28).jpg', './DRIMDB/Good/drimdb_good (59).jpg', './DRIMDB/Bad/drimdb_bad (41).jpg', './DRIMDB/Bad/drimdb_bad (18).jpg', './DRIMDB/Good/drimdb_good (27).jpg', './DRIMDB/Good/drimdb_good (119).jpg', './DRIMDB/Bad/drimdb_bad (17).jpg', './DRIMDB/Bad/drimdb_bad (25).jpg', './DRIMDB/Good/drimdb_good (78).jpg', './DRIMDB/Good/drimdb_good (80).jpg', './DRIMDB/Good/drimdb_good (68).jpg', './DRIMDB/Good/drimdb_good (36).jpg', './DRIMDB/Good/drimdb_good (25).jpg', './DRIMDB/Good/drimdb_good (4).jpg', './DRIMDB/Good/drimdb_good (9).jpg', './DRIMDB/Good/drimdb_good (32).jpg', './DRIMDB/Good/drimdb_good (43).jpg', './DRIMDB/Good/drimdb_good (53).jpg', './DRIMDB/Good/drimdb_good (76).jpg', './DRIMDB/Good/drimdb_good (120).jpg', './DRIMDB/Bad/drimdb_bad (65).jpg', './DRIMDB/Good/drimdb_good (12).jpg', './DRIMDB/Bad/drimdb_bad (42).jpg', './DRIMDB/Bad/drimdb_bad (7).jpg', './DRIMDB/Bad/drimdb_bad (36).jpg', './DRIMDB/Good/drimdb_good (86).jpg', './DRIMDB/Good/drimdb_good (48).jpg', './DRIMDB/Bad/drimdb_bad (40).jpg', './DRIMDB/Bad/drimdb_bad (13).jpg', './DRIMDB/Good/drimdb_good (64).jpg', './DRIMDB/Good/drimdb_good (30).jpg', './DRIMDB/Bad/drimdb_bad (23).jpg', './DRIMDB/Good/drimdb_good (54).jpg', './DRIMDB/Good/drimdb_good (84).jpg', './DRIMDB/Bad/drimdb_bad (59).jpg', './DRIMDB/Good/drimdb_good (100).jpg', './DRIMDB/Good/drimdb_good (111).jpg', './DRIMDB/Bad/drimdb_bad (2).jpg', './DRIMDB/Good/drimdb_good (15).jpg', './DRIMDB/Good/drimdb_good (101).jpg', './DRIMDB/Bad/drimdb_bad (45).jpg', './DRIMDB/Bad/drimdb_bad (57).jpg', './DRIMDB/Good/drimdb_good (89).jpg', './DRIMDB/Bad/drimdb_bad (44).jpg', './DRIMDB/Good/drimdb_good (106).jpg', './DRIMDB/Bad/drimdb_bad (4).jpg', './DRIMDB/Good/drimdb_good (94).jpg', './DRIMDB/Good/drimdb_good (83).jpg', './DRIMDB/Bad/drimdb_bad (26).jpg', './DRIMDB/Bad/drimdb_bad (54).jpg', './DRIMDB/Good/drimdb_good (26).jpg', './DRIMDB/Good/drimdb_good (123).jpg', './DRIMDB/Bad/drimdb_bad (31).jpg', './DRIMDB/Bad/drimdb_bad (56).jpg', './DRIMDB/Good/drimdb_good (56).jpg', './DRIMDB/Bad/drimdb_bad (12).jpg', './DRIMDB/Good/drimdb_good (99).jpg', './DRIMDB/Good/drimdb_good (41).jpg', './DRIMDB/Good/drimdb_good (22).jpg', './DRIMDB/Good/drimdb_good (110).jpg', './DRIMDB/Bad/drimdb_bad (46).jpg', './DRIMDB/Good/drimdb_good (35).jpg', './DRIMDB/Bad/drimdb_bad (39).jpg', './DRIMDB/Good/drimdb_good (115).jpg', './DRIMDB/Good/drimdb_good (65).jpg', './DRIMDB/Bad/drimdb_bad (28).jpg', './DRIMDB/Good/drimdb_good (66).jpg', './DRIMDB/Good/drimdb_good (70).jpg', './DRIMDB/Bad/drimdb_bad (29).jpg', './DRIMDB/Good/drimdb_good (10).jpg', './DRIMDB/Bad/drimdb_bad (67).jpg', './DRIMDB/Bad/drimdb_bad (6).jpg', './DRIMDB/Good/drimdb_good (16).jpg', './DRIMDB/Bad/drimdb_bad (66).jpg', './DRIMDB/Good/drimdb_good (113).jpg', './DRIMDB/Good/drimdb_good (122).jpg', './DRIMDB/Bad/drimdb_bad (43).jpg', './DRIMDB/Bad/drimdb_bad (47).jpg', './DRIMDB/Bad/drimdb_bad (27).jpg', './DRIMDB/Good/drimdb_good (114).jpg', './DRIMDB/Bad/drimdb_bad (48).jpg', './DRIMDB/Good/drimdb_good (74).jpg', './DRIMDB/Good/drimdb_good (96).jpg', './DRIMDB/Good/drimdb_good (20).jpg', './DRIMDB/Good/drimdb_good (82).jpg', './DRIMDB/Good/drimdb_good (92).jpg', './DRIMDB/Bad/drimdb_bad (69).jpg', './DRIMDB/Good/drimdb_good (11).jpg', './DRIMDB/Good/drimdb_good (75).jpg', './DRIMDB/Good/drimdb_good (8).jpg', './DRIMDB/Bad/drimdb_bad (51).jpg', './DRIMDB/Good/drimdb_good (38).jpg', './DRIMDB/Good/drimdb_good (87).jpg', './DRIMDB/Bad/drimdb_bad (38).jpg', './DRIMDB/Good/drimdb_good (33).jpg', './DRIMDB/Good/drimdb_good (62).jpg', './DRIMDB/Good/drimdb_good (50).jpg', './DRIMDB/Bad/drimdb_bad (49).jpg', './DRIMDB/Good/drimdb_good (42).jpg', './DRIMDB/Good/drimdb_good (91).jpg', './DRIMDB/Bad/drimdb_bad (35).jpg', './DRIMDB/Good/drimdb_good (112).jpg', './DRIMDB/Bad/drimdb_bad (19).jpg', './DRIMDB/Good/drimdb_good (17).jpg', './DRIMDB/Good/drimdb_good (31).jpg', './DRIMDB/Good/drimdb_good (47).jpg', './DRIMDB/Good/drimdb_good (55).jpg', './DRIMDB/Bad/drimdb_bad (61).jpg', './DRIMDB/Good/drimdb_good (81).jpg', './DRIMDB/Good/drimdb_good (93).jpg', './DRIMDB/Good/drimdb_good (45).jpg', './DRIMDB/Bad/drimdb_bad (64).jpg', './DRIMDB/Good/drimdb_good (124).jpg', './DRIMDB/Bad/drimdb_bad (15).jpg', './DRIMDB/Good/drimdb_good (63).jpg', './DRIMDB/Good/drimdb_good (44).jpg', './DRIMDB/Good/drimdb_good (37).jpg', './DRIMDB/Good/drimdb_good (23).jpg', './DRIMDB/Good/drimdb_good (77).jpg', './DRIMDB/Good/drimdb_good (39).jpg', './DRIMDB/Bad/drimdb_bad (33).jpg', './DRIMDB/Good/drimdb_good (49).jpg'], 'validation': ['./DRIMDB/Good/drimdb_good (21).jpg', './DRIMDB/Good/drimdb_good (71).jpg', './DRIMDB/Good/drimdb_good (57).jpg', './DRIMDB/Bad/drimdb_bad (16).jpg', './DRIMDB/Bad/drimdb_bad (53).jpg', './DRIMDB/Good/drimdb_good (72).jpg', './DRIMDB/Bad/drimdb_bad (11).jpg', './DRIMDB/Good/drimdb_good (52).jpg', './DRIMDB/Good/drimdb_good (90).jpg', './DRIMDB/Good/drimdb_good (3).jpg', './DRIMDB/Bad/drimdb_bad (62).jpg', './DRIMDB/Good/drimdb_good (98).jpg', './DRIMDB/Good/drimdb_good (5).jpg', './DRIMDB/Good/drimdb_good (24).jpg', './DRIMDB/Good/drimdb_good (105).jpg', './DRIMDB/Bad/drimdb_bad (9).jpg', './DRIMDB/Good/drimdb_good (19).jpg', './DRIMDB/Good/drimdb_good (116).jpg', './DRIMDB/Bad/drimdb_bad (20).jpg', './DRIMDB/Good/drimdb_good (13).jpg', './DRIMDB/Bad/drimdb_bad (10).jpg', './DRIMDB/Good/drimdb_good (69).jpg', './DRIMDB/Good/drimdb_good (104).jpg', './DRIMDB/Good/drimdb_good (107).jpg', './DRIMDB/Bad/drimdb_bad (21).jpg', './DRIMDB/Good/drimdb_good (79).jpg', './DRIMDB/Bad/drimdb_bad (55).jpg', './DRIMDB/Bad/drimdb_bad (32).jpg', './DRIMDB/Good/drimdb_good (29).jpg', './DRIMDB/Good/drimdb_good (67).jpg', './DRIMDB/Good/drimdb_good (46).jpg'], 'test': ['./DRIMDB/Bad/drimdb_bad (3).jpg', './DRIMDB/Good/drimdb_good (34).jpg', './DRIMDB/Good/drimdb_good (1).jpg', './DRIMDB/Good/drimdb_good (2).jpg', './DRIMDB/Good/drimdb_good (7).jpg', './DRIMDB/Good/drimdb_good (85).jpg', './DRIMDB/Good/drimdb_good (60).jpg', './DRIMDB/Bad/drimdb_bad (63).jpg', './DRIMDB/Good/drimdb_good (14).jpg', './DRIMDB/Good/drimdb_good (97).jpg', './DRIMDB/Good/drimdb_good (118).jpg', './DRIMDB/Good/drimdb_good (121).jpg', './DRIMDB/Bad/drimdb_bad (68).jpg', './DRIMDB/Good/drimdb_good (73).jpg', './DRIMDB/Bad/drimdb_bad (24).jpg', './DRIMDB/Bad/drimdb_bad (30).jpg', './DRIMDB/Bad/drimdb_bad (60).jpg', './DRIMDB/Good/drimdb_good (18).jpg', './DRIMDB/Bad/drimdb_bad (34).jpg', './DRIMDB/Good/drimdb_good (51).jpg', './DRIMDB/Good/drimdb_good (95).jpg', './DRIMDB/Bad/drimdb_bad (50).jpg', './DRIMDB/Good/drimdb_good (109).jpg', './DRIMDB/Bad/drimdb_bad (14).jpg', './DRIMDB/Good/drimdb_good (88).jpg', './DRIMDB/Good/drimdb_good (103).jpg', './DRIMDB/Bad/drimdb_bad (5).jpg', './DRIMDB/Bad/drimdb_bad (52).jpg', './DRIMDB/Good/drimdb_good (61).jpg', './DRIMDB/Bad/drimdb_bad (22).jpg', './DRIMDB/Bad/drimdb_bad (37).jpg', './DRIMDB/Good/drimdb_good (108).jpg', './DRIMDB/Good/drimdb_good (6).jpg', './DRIMDB/Good/drimdb_good (117).jpg', './DRIMDB/Bad/drimdb_bad (58).jpg', './DRIMDB/Good/drimdb_good (102).jpg', './DRIMDB/Bad/drimdb_bad (8).jpg', './DRIMDB/Good/drimdb_good (125).jpg', './DRIMDB/Good/drimdb_good (40).jpg']}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fNk4cR0BrXJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class DataGenerator(keras.utils.Sequence):\n",
        "    def __init__(self, list_IDs, labels, batch_size=39, dim=(512, 512), n_channels=3,\n",
        "                 n_classes=2, shuffle=True):\n",
        "        self.dim = dim\n",
        "        self.batch_size = batch_size\n",
        "        self.labels = labels\n",
        "        self.list_IDs = list_IDs\n",
        "        self.n_channels = n_channels\n",
        "        self.n_classes = n_classes\n",
        "        self.shuffle = shuffle\n",
        "        self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "        return int(np.floor(len(self.list_IDs) / self.batch_size))\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]\n",
        "        list_IDs_temp = [self.list_IDs[k] for k in indexes]\n",
        "        X, y = self.__data_generation(list_IDs_temp)\n",
        "        return X, y\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        self.indexes = np.arange(len(self.list_IDs))\n",
        "        if self.shuffle == True:\n",
        "            np.random.shuffle(self.indexes)\n",
        "\n",
        "    def __data_generation(self, list_IDs_temp):\n",
        "        X = np.empty((self.batch_size, *self.dim, self.n_channels))\n",
        "        y = np.empty((self.batch_size), dtype=int)\n",
        "        for i, ID in enumerate(list_IDs_temp):\n",
        "            temp=cv2.imread(ID,1)\n",
        "            temp=cv2.resize(temp, self.dim)\n",
        "            X[i,]=temp\n",
        "          \n",
        "            y[i] = self.labels[ID]\n",
        "        return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZHEaAxwt9w_C",
        "colab_type": "text"
      },
      "source": [
        "# Task 2 Complete\n",
        "\n",
        "# Task 3 Model Making"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S32_HvRDo-DK",
        "colab_type": "text"
      },
      "source": [
        "I have implemented the SWAP version of the model described in the paper. SWAP stands for Shifted - Weighted Average Pooling. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YVN-tauESdG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.models import Model\n",
        "from keras import backend as K\n",
        "from keras.optimizers import Adam, Nadam, RMSprop\n",
        "from keras.layers import Input, Flatten, Conv2D, Layer, Activation\n",
        "from keras.layers.advanced_activations import LeakyReLU, ReLU, ELU\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.pooling import MaxPooling2D, AveragePooling2D\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import random\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GiPng_SHs2P5",
        "colab_type": "code",
        "outputId": "3ada4ce8-3ffe-48b4-ec64-8b66d308bebc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "keras.backend.backend()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'tensorflow'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bu4of2E3EGFO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_filters_1=64\n",
        "num_filters_2=128\n",
        "num_filters_3=256\n",
        "num_filters_4=512\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dHCRwvqj3EKP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Weighted(Layer):\n",
        "    def __init__(self, **kwargs):\n",
        "        self.output_dim = 1\n",
        "        super(Weighted, self).__init__(**kwargs)\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        # Create a trainable weight variable for this layer.\n",
        "        self.input_dim=input_shape[1]\n",
        "        self.W = self.add_weight(name='W1', \n",
        "                                shape=(input_shape[1],1),\n",
        "                                initializer='uniform',\n",
        "                                trainable=True)\n",
        "        self.B = self.add_weight((1,),\n",
        "                                 initializer='zero',\n",
        "                                 name='B1',\n",
        "                                trainable=True)\n",
        "        W, B = self.get_weights()\n",
        "        W = W / np.sum(np.absolute(W))\n",
        "        self.set_weights([W, B])\n",
        "        super(Weighted, self).build(input_shape)  # Be sure to call this at the end\n",
        "\n",
        "    def call(self, x):\n",
        "        W, B = self.get_weights()\n",
        "        output = K.dot(x, K.abs(self.W/K.sum(K.abs(self.W)))) + self.B\n",
        "        return output\n",
        "\n",
        "    def compute_output_shape(self, input_shape):\n",
        "        return (input_shape[0], self.output_dim)\n",
        "\n",
        "\n",
        "class Shifted(Layer):\n",
        "    def __init__(self, **kwargs):\n",
        "        self.output_dim = 1\n",
        "        super(Shifted, self).__init__(**kwargs)\n",
        "\n",
        "    def build(self, input_shape):\n",
        "        # Create a trainable weight variable for this layer.\n",
        "        self.input_dim=input_shape[1]\n",
        "        super(Shifted, self).build(input_shape)  # Be sure to call this at the end\n",
        "\n",
        "    def call(self, x):\n",
        "        output = x - 0.5\n",
        "        return output\n",
        "\n",
        "    def compute_output_shape(self, input_shape):\n",
        "        return (input_shape[0], self.output_dim)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8tyLk_KsQO3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input=Input((512, 512, 3))\n",
        "conv1=Conv2D(filters = num_filters_1 , kernel_size = (3,3), padding='SAME',strides=(1,1), data_format=\"channels_last\")(input)\n",
        "max1=MaxPooling2D((2, 2))(conv1)\n",
        "\n",
        "max1=ReLU()(max1)\n",
        "max1 = BatchNormalization()(max1)\n",
        "\n",
        "conv2=Conv2D(filters = num_filters_2 , kernel_size = (3,3),padding='SAME' ,strides=(1,1), data_format=\"channels_last\")(max1)\n",
        "max2=MaxPooling2D((2, 2))(conv2)\n",
        "\n",
        "max2=ReLU()(max2)\n",
        "\n",
        "max2 = BatchNormalization()(max2)\n",
        "\n",
        "conv3=Conv2D(filters = num_filters_3 , kernel_size = (3,3), padding='SAME',strides=(1,1), data_format=\"channels_last\")(max2)\n",
        "max3=MaxPooling2D((2, 2))(conv3)\n",
        "\n",
        "max3=ReLU()(max3)\n",
        "\n",
        "max3 = BatchNormalization()(max3)\n",
        "\n",
        "conv4=Conv2D(filters = num_filters_4 , kernel_size = (3,3), padding='SAME',strides=(1,1), data_format=\"channels_last\")(max3)\n",
        "max4=MaxPooling2D((2, 2))(conv4)\n",
        "\n",
        "max4=ReLU()(max4)\n",
        "\n",
        "max4 = BatchNormalization()(max4)\n",
        "\n",
        "conv5=Conv2D(filters = num_filters_4, kernel_size= (1,1), padding='SAME',strides=(1,1), data_format=\"channels_last\")(max4)\n",
        "\n",
        "max5=ReLU()(conv5)\n",
        "\n",
        "max4=max5\n",
        "\n",
        "max4 = BatchNormalization()(max4)\n",
        "\n",
        "patch_pred=Conv2D(filters = 1, kernel_size= (1,1), strides=(1,1), data_format=\"channels_last\", activation='sigmoid')(max4)\n",
        "\n",
        "out = Flatten()(patch_pred)\n",
        "\n",
        "out = Weighted()(out)\n",
        "out=Shifted()(out)\n",
        "out=Activation('sigmoid')(out)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tGOqIR7_3m6x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=Model(input, out)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "riQH-mVB1xHk",
        "colab_type": "code",
        "outputId": "cecafa18-4dbb-4ac7-b151-816bf5322354",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         (None, 512, 512, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 512, 512, 64)      1792      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 256, 256, 64)      0         \n",
            "_________________________________________________________________\n",
            "re_lu_1 (ReLU)               (None, 256, 256, 64)      0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 256, 256, 64)      256       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 256, 256, 128)     73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 128, 128, 128)     0         \n",
            "_________________________________________________________________\n",
            "re_lu_2 (ReLU)               (None, 128, 128, 128)     0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 128, 128, 128)     512       \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 128, 128, 256)     295168    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 64, 64, 256)       0         \n",
            "_________________________________________________________________\n",
            "re_lu_3 (ReLU)               (None, 64, 64, 256)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 64, 64, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 64, 64, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 32, 32, 512)       0         \n",
            "_________________________________________________________________\n",
            "re_lu_4 (ReLU)               (None, 32, 32, 512)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 32, 32, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 32, 32, 512)       262656    \n",
            "_________________________________________________________________\n",
            "re_lu_5 (ReLU)               (None, 32, 32, 512)       0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 32, 32, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv2d_6 (Conv2D)            (None, 32, 32, 1)         513       \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "weighted_1 (Weighted)        (None, 1)                 1025      \n",
            "_________________________________________________________________\n",
            "shifted_1 (Shifted)          (None, 1)                 0         \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 1)                 0         \n",
            "=================================================================\n",
            "Total params: 1,821,058\n",
            "Trainable params: 1,818,114\n",
            "Non-trainable params: 2,944\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1qh2ssCX_vo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "opt = Adam(lr=1e-4)\n",
        "model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1GbWhwPGJP-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "params = {'dim': (512,512),\n",
        "          'batch_size': 10,\n",
        "          'n_classes': 2,\n",
        "          'n_channels': 3,\n",
        "          'shuffle': True}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bw6w2Pxu4cMZ",
        "colab_type": "code",
        "outputId": "24be61e7-dd16-4331-a996-c5db8fe5e820",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "training_generator = DataGenerator(dict_X['train'], dict_Y, **params)\n",
        "validation_generator = DataGenerator(dict_X['validation'], dict_Y, **params)\n",
        "test_generator = DataGenerator(dict_X['test'], dict_Y)\n",
        "print(test_generator.batch_size)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "39\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "bHiGRJ83ei1z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5ce86436-3a18-4639-89ff-8b91b0d4e37d"
      },
      "source": [
        "history=model.fit_generator(generator=training_generator,\n",
        "                    validation_data=validation_generator, epochs=140)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/140\n",
            "12/12 [==============================] - 5s 429ms/step - loss: 0.6410 - acc: 0.7000 - val_loss: 0.5511 - val_acc: 0.9000\n",
            "Epoch 2/140\n",
            "12/12 [==============================] - 3s 253ms/step - loss: 0.5968 - acc: 0.8000 - val_loss: 0.5381 - val_acc: 0.9333\n",
            "Epoch 3/140\n",
            "12/12 [==============================] - 3s 256ms/step - loss: 0.5709 - acc: 0.8417 - val_loss: 0.5331 - val_acc: 0.9333\n",
            "Epoch 4/140\n",
            "12/12 [==============================] - 3s 256ms/step - loss: 0.5507 - acc: 0.9167 - val_loss: 0.5387 - val_acc: 0.9000\n",
            "Epoch 5/140\n",
            "12/12 [==============================] - 3s 255ms/step - loss: 0.5380 - acc: 0.9333 - val_loss: 0.5370 - val_acc: 0.9000\n",
            "Epoch 6/140\n",
            "12/12 [==============================] - 3s 258ms/step - loss: 0.5426 - acc: 0.9250 - val_loss: 0.5224 - val_acc: 0.9333\n",
            "Epoch 7/140\n",
            "12/12 [==============================] - 3s 256ms/step - loss: 0.5297 - acc: 0.9250 - val_loss: 0.5282 - val_acc: 0.9333\n",
            "Epoch 8/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5236 - acc: 0.9333 - val_loss: 0.5377 - val_acc: 0.9000\n",
            "Epoch 9/140\n",
            "12/12 [==============================] - 3s 255ms/step - loss: 0.5242 - acc: 0.9333 - val_loss: 0.5462 - val_acc: 0.9000\n",
            "Epoch 10/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.5321 - acc: 0.9167 - val_loss: 0.5520 - val_acc: 0.8667\n",
            "Epoch 11/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5232 - acc: 0.9417 - val_loss: 0.6056 - val_acc: 0.7333\n",
            "Epoch 12/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.5199 - acc: 0.9417 - val_loss: 0.5393 - val_acc: 0.9000\n",
            "Epoch 13/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.5126 - acc: 0.9417 - val_loss: 0.5953 - val_acc: 0.8000\n",
            "Epoch 14/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.5073 - acc: 0.9750 - val_loss: 0.5341 - val_acc: 0.9000\n",
            "Epoch 15/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5155 - acc: 0.9583 - val_loss: 0.5288 - val_acc: 0.9000\n",
            "Epoch 16/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5073 - acc: 0.9667 - val_loss: 0.5392 - val_acc: 0.8667\n",
            "Epoch 17/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5036 - acc: 0.9667 - val_loss: 0.5113 - val_acc: 0.9000\n",
            "Epoch 18/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.5243 - acc: 0.9333 - val_loss: 0.5411 - val_acc: 0.9000\n",
            "Epoch 19/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.5203 - acc: 0.9500 - val_loss: 0.5512 - val_acc: 0.8667\n",
            "Epoch 20/140\n",
            "12/12 [==============================] - 3s 264ms/step - loss: 0.5147 - acc: 0.9667 - val_loss: 0.5605 - val_acc: 0.8667\n",
            "Epoch 21/140\n",
            "12/12 [==============================] - 3s 264ms/step - loss: 0.5187 - acc: 0.9417 - val_loss: 0.5140 - val_acc: 0.9000\n",
            "Epoch 22/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5083 - acc: 0.9583 - val_loss: 0.5253 - val_acc: 0.8667\n",
            "Epoch 23/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.5042 - acc: 0.9583 - val_loss: 0.5086 - val_acc: 0.9333\n",
            "Epoch 24/140\n",
            "12/12 [==============================] - 3s 264ms/step - loss: 0.5159 - acc: 0.9167 - val_loss: 0.5268 - val_acc: 0.8667\n",
            "Epoch 25/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.5072 - acc: 0.9417 - val_loss: 0.5192 - val_acc: 0.9000\n",
            "Epoch 26/140\n",
            "12/12 [==============================] - 3s 265ms/step - loss: 0.5051 - acc: 0.9333 - val_loss: 0.5155 - val_acc: 0.9000\n",
            "Epoch 27/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5078 - acc: 0.9500 - val_loss: 0.5255 - val_acc: 0.9000\n",
            "Epoch 28/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.5138 - acc: 0.9583 - val_loss: 0.5612 - val_acc: 0.8000\n",
            "Epoch 29/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5073 - acc: 0.9500 - val_loss: 0.5098 - val_acc: 0.9333\n",
            "Epoch 30/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.5020 - acc: 0.9583 - val_loss: 0.5332 - val_acc: 0.8667\n",
            "Epoch 31/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5106 - acc: 0.9583 - val_loss: 0.5358 - val_acc: 0.9000\n",
            "Epoch 32/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.5038 - acc: 0.9500 - val_loss: 0.5336 - val_acc: 0.8667\n",
            "Epoch 33/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5074 - acc: 0.9583 - val_loss: 0.5195 - val_acc: 0.8667\n",
            "Epoch 34/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4961 - acc: 0.9833 - val_loss: 0.5030 - val_acc: 0.9667\n",
            "Epoch 35/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4944 - acc: 0.9833 - val_loss: 0.5141 - val_acc: 0.9333\n",
            "Epoch 36/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5046 - acc: 0.9417 - val_loss: 0.5041 - val_acc: 0.9333\n",
            "Epoch 37/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4919 - acc: 0.9750 - val_loss: 0.5205 - val_acc: 0.9000\n",
            "Epoch 38/140\n",
            "12/12 [==============================] - 3s 258ms/step - loss: 0.4987 - acc: 0.9667 - val_loss: 0.5020 - val_acc: 0.9333\n",
            "Epoch 39/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.5052 - acc: 0.9500 - val_loss: 0.5203 - val_acc: 0.9333\n",
            "Epoch 40/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.5037 - acc: 0.9417 - val_loss: 0.4998 - val_acc: 0.9667\n",
            "Epoch 41/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4987 - acc: 0.9667 - val_loss: 0.5029 - val_acc: 0.9333\n",
            "Epoch 42/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5016 - acc: 0.9500 - val_loss: 0.4971 - val_acc: 0.9667\n",
            "Epoch 43/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.5072 - acc: 0.9667 - val_loss: 0.4937 - val_acc: 0.9667\n",
            "Epoch 44/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5017 - acc: 0.9583 - val_loss: 0.4924 - val_acc: 0.9667\n",
            "Epoch 45/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5124 - acc: 0.9333 - val_loss: 0.5037 - val_acc: 0.9667\n",
            "Epoch 46/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.5047 - acc: 0.9500 - val_loss: 0.4987 - val_acc: 0.9667\n",
            "Epoch 47/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.5090 - acc: 0.9583 - val_loss: 0.5182 - val_acc: 0.9333\n",
            "Epoch 48/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5092 - acc: 0.9333 - val_loss: 0.5093 - val_acc: 0.9333\n",
            "Epoch 49/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4917 - acc: 0.9750 - val_loss: 0.4973 - val_acc: 0.9667\n",
            "Epoch 50/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4960 - acc: 0.9833 - val_loss: 0.5014 - val_acc: 0.9667\n",
            "Epoch 51/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5015 - acc: 0.9583 - val_loss: 0.5011 - val_acc: 0.9667\n",
            "Epoch 52/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4945 - acc: 0.9750 - val_loss: 0.5130 - val_acc: 0.9333\n",
            "Epoch 53/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5002 - acc: 0.9750 - val_loss: 0.5023 - val_acc: 0.9333\n",
            "Epoch 54/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5036 - acc: 0.9750 - val_loss: 0.5177 - val_acc: 0.9333\n",
            "Epoch 55/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4966 - acc: 0.9917 - val_loss: 0.5160 - val_acc: 0.9333\n",
            "Epoch 56/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4867 - acc: 1.0000 - val_loss: 0.4906 - val_acc: 0.9667\n",
            "Epoch 57/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4973 - acc: 0.9667 - val_loss: 0.5040 - val_acc: 0.9667\n",
            "Epoch 58/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5011 - acc: 0.9500 - val_loss: 0.5027 - val_acc: 0.9333\n",
            "Epoch 59/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5034 - acc: 0.9583 - val_loss: 0.5056 - val_acc: 0.9333\n",
            "Epoch 60/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4920 - acc: 0.9750 - val_loss: 0.5178 - val_acc: 0.9333\n",
            "Epoch 61/140\n",
            "12/12 [==============================] - 3s 258ms/step - loss: 0.4900 - acc: 0.9833 - val_loss: 0.5060 - val_acc: 0.9667\n",
            "Epoch 62/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.5040 - acc: 0.9250 - val_loss: 0.5007 - val_acc: 1.0000\n",
            "Epoch 63/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4910 - acc: 0.9750 - val_loss: 0.4925 - val_acc: 0.9333\n",
            "Epoch 64/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4923 - acc: 0.9833 - val_loss: 0.5162 - val_acc: 0.9333\n",
            "Epoch 65/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4866 - acc: 0.9833 - val_loss: 0.5017 - val_acc: 0.9333\n",
            "Epoch 66/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4970 - acc: 0.9667 - val_loss: 0.4996 - val_acc: 0.9333\n",
            "Epoch 67/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4854 - acc: 0.9833 - val_loss: 0.5033 - val_acc: 0.9333\n",
            "Epoch 68/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4872 - acc: 0.9833 - val_loss: 0.5003 - val_acc: 0.9333\n",
            "Epoch 69/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4946 - acc: 0.9583 - val_loss: 0.5271 - val_acc: 0.9000\n",
            "Epoch 70/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4965 - acc: 0.9833 - val_loss: 0.4998 - val_acc: 0.9333\n",
            "Epoch 71/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4897 - acc: 0.9833 - val_loss: 0.5337 - val_acc: 0.9000\n",
            "Epoch 72/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4917 - acc: 0.9917 - val_loss: 0.4951 - val_acc: 0.9333\n",
            "Epoch 73/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4902 - acc: 0.9833 - val_loss: 0.4879 - val_acc: 0.9667\n",
            "Epoch 74/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4919 - acc: 0.9833 - val_loss: 0.4901 - val_acc: 0.9667\n",
            "Epoch 75/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4972 - acc: 0.9667 - val_loss: 0.5082 - val_acc: 0.9667\n",
            "Epoch 76/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4886 - acc: 0.9917 - val_loss: 0.4984 - val_acc: 0.9667\n",
            "Epoch 77/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4836 - acc: 1.0000 - val_loss: 0.4972 - val_acc: 0.9667\n",
            "Epoch 78/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4928 - acc: 0.9667 - val_loss: 0.5128 - val_acc: 0.9333\n",
            "Epoch 79/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4865 - acc: 0.9917 - val_loss: 0.4867 - val_acc: 0.9667\n",
            "Epoch 80/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4931 - acc: 0.9750 - val_loss: 0.5026 - val_acc: 0.9333\n",
            "Epoch 81/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4803 - acc: 1.0000 - val_loss: 0.4919 - val_acc: 0.9333\n",
            "Epoch 82/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4871 - acc: 0.9917 - val_loss: 0.5080 - val_acc: 0.9667\n",
            "Epoch 83/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4845 - acc: 0.9833 - val_loss: 0.4822 - val_acc: 0.9667\n",
            "Epoch 84/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4880 - acc: 0.9833 - val_loss: 0.5091 - val_acc: 0.9333\n",
            "Epoch 85/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4855 - acc: 1.0000 - val_loss: 0.4787 - val_acc: 1.0000\n",
            "Epoch 86/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4903 - acc: 0.9750 - val_loss: 0.4979 - val_acc: 0.9333\n",
            "Epoch 87/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4905 - acc: 0.9917 - val_loss: 0.5075 - val_acc: 0.9667\n",
            "Epoch 88/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4888 - acc: 0.9917 - val_loss: 0.4974 - val_acc: 0.9333\n",
            "Epoch 89/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4818 - acc: 0.9917 - val_loss: 0.4989 - val_acc: 0.9667\n",
            "Epoch 90/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4945 - acc: 0.9583 - val_loss: 0.5008 - val_acc: 0.9333\n",
            "Epoch 91/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4863 - acc: 0.9750 - val_loss: 0.5035 - val_acc: 0.9667\n",
            "Epoch 92/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4852 - acc: 0.9917 - val_loss: 0.4937 - val_acc: 0.9667\n",
            "Epoch 93/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4859 - acc: 0.9750 - val_loss: 0.4965 - val_acc: 0.9333\n",
            "Epoch 94/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4838 - acc: 0.9833 - val_loss: 0.4936 - val_acc: 1.0000\n",
            "Epoch 95/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4850 - acc: 1.0000 - val_loss: 0.4804 - val_acc: 0.9667\n",
            "Epoch 96/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4839 - acc: 0.9833 - val_loss: 0.4815 - val_acc: 1.0000\n",
            "Epoch 97/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4800 - acc: 1.0000 - val_loss: 0.4881 - val_acc: 0.9667\n",
            "Epoch 98/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4799 - acc: 1.0000 - val_loss: 0.4826 - val_acc: 1.0000\n",
            "Epoch 99/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4789 - acc: 0.9917 - val_loss: 0.4811 - val_acc: 0.9667\n",
            "Epoch 100/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4767 - acc: 0.9917 - val_loss: 0.4833 - val_acc: 0.9667\n",
            "Epoch 101/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4761 - acc: 1.0000 - val_loss: 0.4813 - val_acc: 0.9667\n",
            "Epoch 102/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4890 - acc: 0.9833 - val_loss: 0.4971 - val_acc: 0.9667\n",
            "Epoch 103/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4789 - acc: 0.9833 - val_loss: 0.4953 - val_acc: 0.9333\n",
            "Epoch 104/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4794 - acc: 1.0000 - val_loss: 0.4967 - val_acc: 0.9333\n",
            "Epoch 105/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4834 - acc: 0.9833 - val_loss: 0.4967 - val_acc: 0.9333\n",
            "Epoch 106/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4824 - acc: 0.9833 - val_loss: 0.4937 - val_acc: 0.9667\n",
            "Epoch 107/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4815 - acc: 1.0000 - val_loss: 0.4875 - val_acc: 0.9667\n",
            "Epoch 108/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4824 - acc: 0.9917 - val_loss: 0.4822 - val_acc: 1.0000\n",
            "Epoch 109/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4785 - acc: 0.9833 - val_loss: 0.4847 - val_acc: 0.9667\n",
            "Epoch 110/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4778 - acc: 0.9917 - val_loss: 0.4957 - val_acc: 1.0000\n",
            "Epoch 111/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4917 - acc: 0.9750 - val_loss: 0.4823 - val_acc: 0.9667\n",
            "Epoch 112/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4790 - acc: 0.9833 - val_loss: 0.4896 - val_acc: 0.9667\n",
            "Epoch 113/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4782 - acc: 1.0000 - val_loss: 0.5173 - val_acc: 0.9333\n",
            "Epoch 114/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4833 - acc: 0.9917 - val_loss: 0.4982 - val_acc: 0.9333\n",
            "Epoch 115/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4737 - acc: 1.0000 - val_loss: 0.4955 - val_acc: 0.9333\n",
            "Epoch 116/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4754 - acc: 1.0000 - val_loss: 0.5075 - val_acc: 0.9667\n",
            "Epoch 117/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4770 - acc: 1.0000 - val_loss: 0.4930 - val_acc: 0.9333\n",
            "Epoch 118/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4742 - acc: 1.0000 - val_loss: 0.4939 - val_acc: 0.9333\n",
            "Epoch 119/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4747 - acc: 1.0000 - val_loss: 0.4963 - val_acc: 0.9333\n",
            "Epoch 120/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4747 - acc: 0.9917 - val_loss: 0.5020 - val_acc: 0.9667\n",
            "Epoch 121/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4722 - acc: 1.0000 - val_loss: 0.4977 - val_acc: 0.9333\n",
            "Epoch 122/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4742 - acc: 0.9917 - val_loss: 0.4941 - val_acc: 0.9333\n",
            "Epoch 123/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4737 - acc: 1.0000 - val_loss: 0.4934 - val_acc: 0.9333\n",
            "Epoch 124/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4726 - acc: 1.0000 - val_loss: 0.4879 - val_acc: 0.9667\n",
            "Epoch 125/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4730 - acc: 0.9917 - val_loss: 0.4896 - val_acc: 0.9667\n",
            "Epoch 126/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4719 - acc: 1.0000 - val_loss: 0.4859 - val_acc: 0.9667\n",
            "Epoch 127/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4732 - acc: 1.0000 - val_loss: 0.4836 - val_acc: 0.9667\n",
            "Epoch 128/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4727 - acc: 1.0000 - val_loss: 0.4905 - val_acc: 0.9667\n",
            "Epoch 129/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4777 - acc: 0.9750 - val_loss: 0.4927 - val_acc: 0.9333\n",
            "Epoch 130/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4851 - acc: 0.9833 - val_loss: 0.4983 - val_acc: 0.9667\n",
            "Epoch 131/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4713 - acc: 1.0000 - val_loss: 0.4922 - val_acc: 0.9333\n",
            "Epoch 132/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4815 - acc: 0.9833 - val_loss: 0.4917 - val_acc: 0.9667\n",
            "Epoch 133/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4755 - acc: 0.9917 - val_loss: 0.4853 - val_acc: 1.0000\n",
            "Epoch 134/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4696 - acc: 1.0000 - val_loss: 0.4803 - val_acc: 0.9667\n",
            "Epoch 135/140\n",
            "12/12 [==============================] - 3s 263ms/step - loss: 0.4783 - acc: 0.9833 - val_loss: 0.4824 - val_acc: 0.9667\n",
            "Epoch 136/140\n",
            "12/12 [==============================] - 3s 259ms/step - loss: 0.4772 - acc: 0.9833 - val_loss: 0.4865 - val_acc: 0.9333\n",
            "Epoch 137/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4908 - acc: 0.9583 - val_loss: 0.4856 - val_acc: 1.0000\n",
            "Epoch 138/140\n",
            "12/12 [==============================] - 3s 262ms/step - loss: 0.4827 - acc: 0.9917 - val_loss: 0.4886 - val_acc: 0.9667\n",
            "Epoch 139/140\n",
            "12/12 [==============================] - 3s 261ms/step - loss: 0.4761 - acc: 0.9833 - val_loss: 0.4935 - val_acc: 0.9333\n",
            "Epoch 140/140\n",
            "12/12 [==============================] - 3s 260ms/step - loss: 0.4715 - acc: 1.0000 - val_loss: 0.4978 - val_acc: 0.9333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdtZl98ksMsz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "483e76a2-efbe-489c-911c-aeb289fbd594"
      },
      "source": [
        "\n",
        "# Plot training & validation accuracy values\n",
        "plt.plot(history.history['acc'])\n",
        "plt.plot(history.history['val_acc'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p7+4LxiY5wgBtpaZOiEcTD27qIJZMZ8YdyD/utkFJEJ+9eAHDkQRPbs/+TUQTKQKBYQCS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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QsgazNLmsMi4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "502efab7-f828-4d0f-9c9c-d114db03155e"
      },
      "source": [
        "# Plot training & validation loss values\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Test'], loc='upper left')\n",
        "plt.show()\n"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F0CQipznZVB8CHo/jPQBOcHysKseDrioVDkVRJiZxc1UZY3pE5FasCPiB+40xW0TkLmCN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\nIxwGg8Fg6BVGOAwGg8HQK/4/OnAqmuoXKqMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g1EN1J4EIPNL",
        "colab_type": "text"
      },
      "source": [
        "# Task 3 Complete\n",
        "\n",
        "# Task 4 Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "si-eUkXuv54A",
        "colab_type": "text"
      },
      "source": [
        "I was not getting good accuray with the model given in the paper. To further improve the accuracy of the model, I added the layer of Batch Normalization after every Conv Layer. \n",
        "\n",
        "But, still the model was getting overtrained. To improve further, I used data augmentation technique and appended the augmented images to the training data. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMGSWSw9OJ5g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def LoadDataset_array():\n",
        "  data_path='./DRIMDB'\n",
        "  data_path1=['Bad', 'Good']\n",
        "  X=[]\n",
        "  Y=[]\n",
        "  label=0\n",
        "  for sub in data_path1:\n",
        "      path2=os.path.join(data_path,sub)\n",
        "      for img_path in os.listdir(path2):\n",
        "        if '.jpg' in img_path:\n",
        "          img_path1=os.path.join(path2,img_path)\n",
        "          X.append(cv2.resize(cv2.imread(img_path1,1),(512, 512)))\n",
        "          Y.append(label)\n",
        "      label=label+1\n",
        "  z = list(zip(X, Y))\n",
        "  random.shuffle(z)\n",
        "  X,Y = zip(*z)\n",
        "  return X,Y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JOIEVSgFRC4G",
        "colab_type": "code",
        "outputId": "45888ead-4345-488f-dc17-f32b5a8a05c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "X,Y=LoadDataset_array()\n",
        "print(len(X))\n",
        "print(len(Y))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "194\n",
            "194\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nABvk5y3biop",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#splitting of data into train, val and test\n",
        "X_train=X[0:124]\n",
        "Y_train=Y[0:124]\n",
        "\n",
        "X_val=X[124:155]\n",
        "Y_val=Y[124:155]\n",
        "\n",
        "X_test=X[155:194]\n",
        "Y_test=Y[155:194]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GC0U8N_zc72c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train = np.array(X_train)\n",
        "Y_train = np.array(Y_train)\n",
        "\n",
        "X_val = np.array(X_val)\n",
        "Y_val = np.array(Y_val)\n",
        "\n",
        "X_test = np.array(X_test)\n",
        "Y_test = np.array(Y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Y9MSmBKqPa9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_train = ImageDataGenerator(horizontal_flip=True, vertical_flip=True,\n",
        "                                 width_shift_range=0.15, height_shift_range=0.15,\n",
        "                                 rotation_range=45, zoom_range=0.15)\n",
        "\n",
        "data_valid = ImageDataGenerator()\n",
        "\n",
        "data_test = ImageDataGenerator()\n",
        "\n",
        "\n",
        "\n",
        "train_data = data_train.flow(X_train, Y_train, batch_size=10)\n",
        "\n",
        "val_data = data_valid.flow(X_val, Y_val, batch_size=10)\n",
        "\n",
        "test_data= data_test.flow(X_test, Y_test, batch_size=10)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_f-4KPbMoRT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.fit_generator(generator=train_data,\n",
        "                    validation_data=val_data, epochs=140)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5y2vc08ivjud",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save_weights('model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Dw9wh6rf3yp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred = model.predict_generator(test_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QnWmSyvq36Nj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a7888143-c2f4-4ef8-9ea7-b0707852b8a4"
      },
      "source": [
        "pred.shape"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(39, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2v2hOIfNi1Gq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e0b3d092-df3b-4828-8345-e88744002bfd"
      },
      "source": [
        "pred1=[]\n",
        "for i in range(39):\n",
        "  if pred[i]>=0.5:\n",
        "      pred1.append(1)\n",
        "  else:\n",
        "      pred1.append(2)\n",
        "\n",
        "y_true=[]\n",
        "for i in range(len(dict_X['test'])):\n",
        "  y_true.append(dict_Y[dict_X['test'][i]])\n",
        "\n",
        "np.sum(np.abs(np.array(Y_test)-np.array(pred1)))"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "37\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_wD5i1rLqDLV",
        "colab_type": "text"
      },
      "source": [
        "## In the previous section, We have added the batch normalization layer and experimented with data augmentation techniques. Now I will tune the hyperparameters associated with this model. For this I am using talos. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OyyuBLEvqw4I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "! pip install talos"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCcBP8kMrCvK",
        "colab_type": "text"
      },
      "source": [
        "We will making a function eyes_model, in which our main model is defined."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RmmzuR-hx5im",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def eyes_model(x_train, y_train, x_val, y_val, params):\n",
        "    input=Input((512, 512, 3))\n",
        "    conv1=Conv2D(filters = num_filters_1 , kernel_size = (3,3), padding='SAME',strides=(1,1), data_format=\"channels_last\")(input)\n",
        "    max1=MaxPooling2D((2, 2))(conv1)\n",
        "    max1=params['activation'](max1)\n",
        "    max1 = BatchNormalization()(max1)\n",
        "\n",
        "    conv2=Conv2D(filters = num_filters_2 , kernel_size = (3,3),padding='SAME' ,strides=(1,1), data_format=\"channels_last\")(max1)\n",
        "    max2=MaxPooling2D((2, 2))(conv2)\n",
        "    max2=params['activation'](max2)\n",
        "    max2 = BatchNormalization()(max2)\n",
        "\n",
        "    conv3=Conv2D(filters = num_filters_3 , kernel_size = (3,3), padding='SAME',strides=(1,1), data_format=\"channels_last\")(max2)\n",
        "    max3=MaxPooling2D((2, 2))(conv3)\n",
        "    max3=params['activation'](max3)\n",
        "    max3 = BatchNormalization()(max3)\n",
        "\n",
        "    conv4=Conv2D(filters = num_filters_4 , kernel_size = (3,3), padding='SAME',strides=(1,1), data_format=\"channels_last\")(max3)\n",
        "    max4=MaxPooling2D((2, 2))(conv4)\n",
        "    max4=params['activation'](max4)\n",
        "    max4 = BatchNormalization()(max4)\n",
        "\n",
        "    conv5=Conv2D(filters = num_filters_4, kernel_size= (1,1), padding='SAME',strides=(1,1), data_format=\"channels_last\")(max4)\n",
        "\n",
        "    max5=params['activation'](conv5)\n",
        "    max4=max5\n",
        "    max4 = BatchNormalization()(max4)\n",
        "    patch_pred=Conv2D(filters = 1, kernel_size= (1,1), strides=(1,1), data_format=\"channels_last\", activation='sigmoid')(max4)\n",
        "    out = Flatten()(patch_pred)\n",
        "\n",
        "    out = SWAP()(out)\n",
        "    out=SWAP1()(out)\n",
        "    out=Activation('sigmoid')(out)\n",
        "\n",
        "    model = Model(input, out)\n",
        "    model.compile(optimizer=Adam(2e-4), loss=params['losses'], metrics=['accuracy'])\n",
        "    history = model.fit(x_train, y_train, \n",
        "                        validation_data=[x_val, y_val],\n",
        "                        batch_size=params['batch_size'],\n",
        "                        epochs=params['epochs'],\n",
        "                        verbose=0)\n",
        "    return history, model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DO-tHNC82NAY",
        "colab_type": "text"
      },
      "source": [
        "For optimization, I have considered these many pararmeters\n",
        "\n",
        "\n",
        "1.   batch_size\n",
        "2.   epochs\n",
        "3.   optimizer\n",
        "4.   losses\n",
        "5.   activation \n",
        "\n",
        "Other parameters could also have been changed like fliter size, no of conv layers, number of filters, input size of the model. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rK23_gnz429E",
        "colab_type": "text"
      },
      "source": [
        "The results corresponding to the different combinations are attached in a csv file. Please see through it. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IugFSVgy2RNF",
        "colab_type": "code",
        "outputId": "46468272-54a4-45a2-dc51-76e5fe1d5615",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "import talos as ta\n",
        "p = {'batch_size': (10, 20, 40),\n",
        "     'epochs': [40, 80, 100 ,150],\n",
        "     #'optimizer': [Adam(1e-4), Adam(1e-3), Adam(2e-4), Nadam(1e-4),Nadam(1e-3)],\n",
        "     'losses': ['logcosh', 'binary_crossentropy'],\n",
        "     'activation':[ReLU(), ELU(), LeakyReLU()]\n",
        "     }\n",
        "    \n",
        "x=np.array(X[0:155])\n",
        "y=np.array(Y[0:155])\n",
        "\n",
        "t = ta.Scan(x=x,\n",
        "            y=y,\n",
        "            model=eyes_model, \n",
        "            params=p, experiment_name='exp1', val_split=0.2)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5gjgxFKNooMX",
        "colab_type": "text"
      },
      "source": [
        "# Task 5 - Intermediates (explainability)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PAytAyb9pZTy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model1=Model(input,patch_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LF0BG0pssMkD",
        "colab_type": "code",
        "outputId": "c2e3c4ec-040e-4391-effb-f9b55763e08d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "f, axarr = plt.subplots(1,2)\n",
        "axarr[0].imshow(cv2.cvtColor(data_bad[3], cv2.COLOR_BGR2RGB))\n",
        "axarr[1].imshow(cv2.cvtColor(data_bad[6], cv2.COLOR_BGR2RGB))"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe324a16710>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IxJ+XjRpFWOg7iCjFRqhfgy0HrTlST2gH6WekVrRHyrB1Iv07wysB3OMHqWFF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XkcW/94mAEflM3E0swFthpQg3rB3wtfwGUzvk0IaoJhh2ziygScd/pnHEbWO2KluZ4Yed9XkRYH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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJpoaHmWsrsU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "81cdb68c-b3e2-487c-bb0f-4d38b2ebac54"
      },
      "source": [
        "test1=np.reshape(data_bad[3], (1,512, 512, 3))\n",
        "test2=np.reshape(data_bad[6], (1,512, 512, 3))\n",
        "heat_map1=model1.predict(test1)\n",
        "heat_map2=model1.predict(test2)\n",
        "heat_map1=np.reshape(heat_map1, (32,32,1))\n",
        "heat_map2=np.reshape(heat_map2, (32,32,1))\n",
        "heat_map1=heat_map1 * 255\n",
        "heat_map2=heat_map2 * 255\n",
        "heat1 = cv2.resize( heat_map1,(200,200))\n",
        "heat2 = cv2.resize( heat_map2,(200,200))\n",
        "f, axarr = plt.subplots(1,2)\n",
        "axarr[0].imshow(heat1, cmap='gray')\n",
        "axarr[1].imshow(heat2, cmap='gray')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe32493a898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5fH5oHcAwDOWPT6VSSKfTKktVL0B2W6j08MnJCZ4+fYrvfOc7OD8/R6PRQL1e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\nzjAMs4KwuDMMw6wgLO4MwzAryP8HYhzpgrZahDwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OU5QIKP5ygYw",
        "colab_type": "text"
      },
      "source": [
        "These 2 masks images are the corresponding intermediates of 2 bad quality images. In the first image, you can easily see that the left side is quite brighter and on the right side, it is quite darker. In both these regions, there is not much information we can extract because of luminous nature. This is the reason, why everything appears black in the intermediate, except in the middle portion.\n",
        "\n",
        "Second image also has the same case."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVA1_ZOWvsZn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "45b1c4be-e614-4296-e1a0-57af0b8c7174"
      },
      "source": [
        "f, axarr = plt.subplots(1,2)\n",
        "axarr[0].imshow(cv2.cvtColor(data_good[4], cv2.COLOR_BGR2RGB))\n",
        "axarr[1].imshow(cv2.cvtColor(data_good[6], cv2.COLOR_BGR2RGB))"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ERROR! Session/line number was not unique in database. History logging moved to new session 59\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe324794fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5iPV9Q+2NifGmtrk7pdTIqrRpPB7axyTA0QJ8FtjZUc5OC8MgzOdBU0zPGQc4PjFOTpWk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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRL00m4bu3Pa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "3a1f31ff-f676-4357-b1c2-d3af771379a0"
      },
      "source": [
        "test1=np.reshape(data_good[4], (1,512, 512, 3))\n",
        "test2=np.reshape(data_good[6], (1,512, 512, 3))\n",
        "heat_map1=model1.predict(test1)\n",
        "heat_map2=model1.predict(test2)\n",
        "heat_map1=np.reshape(heat_map1, (32,32,1))\n",
        "heat_map2=np.reshape(heat_map2, (32,32,1))\n",
        "heat_map1=heat_map1 * 255\n",
        "heat_map2=heat_map2 * 255\n",
        "heat1 = cv2.resize( heat_map1,(200,200))\n",
        "heat2 = cv2.resize( heat_map2,(200,200))\n",
        "f, axarr = plt.subplots(1,2)\n",
        "axarr[0].imshow(heat1, cmap='gray')\n",
        "axarr[1].imshow(heat2, cmap='gray')"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fe3246c20f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xd+DVMI/gUA85Ji8/l652itTvB4LysW2DEqq2bXNrAfLM2+32xoIUsllqV9Dr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\nCEXuCgoKCncQitwVFBQU7iD+f8fmH3ob4RsEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54x_WkHNtomd",
        "colab_type": "text"
      },
      "source": [
        "In this case, both the intermediates are majorly white because they corresponds to good quality images.\n",
        "In both cases, the brightness in the image is appropriate and nerves are also very clearly visible."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5mdgtcA1xG6",
        "colab_type": "text"
      },
      "source": [
        "### This shows that our model is capable of pinpointing low quality regions in the image."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEExCtseAJ0Y",
        "colab_type": "text"
      },
      "source": [
        "# Task 6 - Criticize the paper explaining its drawbacks\n",
        "In the paper, authors are using the notion of Multiple Instance Learning (MIL). In this approach, they have considered an image equivalent to a number of instances. They are dividing the whole image into different regions and then each region is treated as an instance. But they have used CNN for this task. In CNN, all the filters present in any layer are convolved with all the patches of an image. So, the weights associated with a single filter are not unique to any particular patch.\n",
        "\n",
        "\n",
        "They have used the concept of ‘weighted average’. In this case, weights are assigned to different patches and the convex combination of the outputs of different patches is the final output/label for that image. This implies that the model has gained bias towards a few patches. So, for any new image, this model assumes that the relevant information is stored only in those patches. But this might not be the case. \n",
        "\n",
        "\n",
        "Patches in the resultant map are also overlapping because a patch’s information is shared with its neighbouring patches, while convolving with the filters in the intermediate layer. So, the resultant feature map(before flattening) contains instances which are not independent of each other. \n",
        "# Task 7 - Suggest alternatives / improvements to the model\n",
        "I haven’t tried these alternatives.\n",
        "\n",
        "\n",
        "Initially, divide the whole image into different segments and for each segment, make a separate neural network. Obtain individual outputs from these segments and using ‘weighted average’, assign a single label to an image. In this case, all the segments will be trained simultaneously, because the loss function will only contain the true label and the final predicted label, not the outputs of the segments.\n",
        "\n",
        "\n",
        "In this model, the image is directly given as an input to the model. We can also append a filtered image with this source image and, then this combination can be given to the model. This filtered image can be the output of the Canny Edge detection Algorithm.\n",
        "\n",
        "\n",
        "Sometimes on these small datasets, Statistical Pattern Models(SPR) are also worth trying like Gaussian Mixture Model(GMM), etc.\n",
        "After obtaining the outputs of different patches, train a FCL(Fully Connected Layer) with the ‘binary_crossentropy’ loss. \n"
      ]
    }
  ]
}