UNet_Model1_Cup.ipynb

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    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRnP5yt7UUE5"
      },
      "source": [
        "# We will use RIM-ONE and DRISHTI dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ma4BCv4UcXN"
      },
      "source": [
        "# I have obtained the dataset from the following github page, which is open source by the way\n",
        "# Git Hub URL: https://github.com/seva100/optic-nerve-cnn\n",
        "# Data URL: https://drive.google.com/drive/folders/13g62bhqN1JHJ2fky2Xy5avLbZ2YLMdwB"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "807wnRglVAJu"
      },
      "source": [
        "# we will start coding by mounting the google drive and setting up configurations "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsKfQRodfdzg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88f673c9-e23a-4151-a5c1-5313cd242ab7"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-bdE8d6h0cW"
      },
      "source": [
        "# Initial Setup\n",
        "\n",
        "train=True\n",
        "Epochs=30\n",
        "preload=False\n",
        "hough=False\n",
        "rams=False\n",
        "dynAug=False\n",
        "depth = 6\n",
        "chan = 64\n",
        "ir = 1.1\n",
        "rand_sv = 42\n",
        "ds = 'MIX'\n",
        "zone = 'CUP'\n",
        "cup = True\n",
        "mix = True\n",
        "dri = False\n",
        "rim = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuaCss4d31b1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f2e97405-ea46-4218-bc33-550df09d0b5f"
      },
      "source": [
        "print(depth,chan,ir,rand_sv,ds,zone)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6 64 1.1 42 MIX CUP\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_xV9Nn6JwIkR"
      },
      "source": [
        "# Import all the necessary libraries\n",
        "\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import glob\n",
        "from datetime import datetime\n",
        "import warnings\n",
        "warnings.simplefilter('ignore')\n",
        "import scipy as sp\n",
        "import scipy.ndimage\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import skimage\n",
        "import skimage.exposure\n",
        "import skimage.transform\n",
        "import skimage.measure\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import cv2\n",
        "import h5py\n",
        "from tqdm import tqdm_notebook\n",
        "from IPython.display import display\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras import layers as layers\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization, Conv2D, MaxPooling2D, ZeroPadding2D, Input, Embedding, LSTM, Lambda, UpSampling2D, concatenate, Conv2DTranspose\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from tensorflow.keras.optimizers import SGD, Adam, Adagrad\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau, CSVLogger\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras import backend as K\n",
        "from sklearn.model_selection import train_test_split\n",
        "import math\n",
        "from skimage.transform import hough_ellipse\n",
        "from skimage.draw import ellipse_perimeter, ellipse\n",
        "from skimage.measure import  ransac, EllipseModel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjSzDEK6IMHI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eee63e9d-3db9-444a-c6e3-61161ce357df"
      },
      "source": [
        "# boiler plate code configuration for TPU or GPU selection\n",
        "\n",
        "print(\"Tensorflow version \" + tf.__version__)\n",
        "if 'COLAB_TPU_ADDR' in os.environ:\n",
        "  print(os.environ['COLAB_TPU_ADDR'])\n",
        "else:\n",
        "  print('No TPU')\n",
        "if(tf.test.is_gpu_available()):\n",
        "  gpu=True\n",
        "else:\n",
        "  gpu=False\n",
        "  print('No GPU')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version 2.3.0\n",
            "10.81.14.98:8470\n",
            "WARNING:tensorflow:From <ipython-input-8-03f923cc4082>:8: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n",
            "No GPU\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hb0xsjvWCt0"
      },
      "source": [
        "# Metrics and further helper functions below"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62AopuRZV5-M"
      },
      "source": [
        "# let's set tensorflow backend working type to be float\n",
        "\n",
        "K.set_floatx('float32')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "L3gKgtXdwsuO"
      },
      "source": [
        "# Metrics for evaluation\n",
        "\n",
        "def dice_coef(y_true, y_pred):\n",
        "    y_true_f = K.flatten(y_true)\n",
        "    y_pred_f = K.flatten(y_pred)\n",
        "    intersection = K.sum(y_true_f * y_pred_f)\n",
        "    return (2. * intersection + K.epsilon()) / (K.sum(y_true_f) + K.sum(y_pred_f) + K.epsilon())\n",
        "  \n",
        "def dice_loss(y_true,y_pred):\n",
        "    return -dice_coef(y_true,y_pred)\n",
        "  \n",
        "def log_dice_loss(y_true, y_pred):\n",
        "    return -K.log(dice_coef(y_true, y_pred))\n",
        "\n",
        "# Computes mean Intersection-over-Union (IOU) for two arrays of binary images.\n",
        "\n",
        "def iou_loss_core(y_true, y_pred, smooth=1):\n",
        "    intersection = K.sum(K.abs(y_true * y_pred), axis=-1)\n",
        "    union = K.sum(y_true,-1) + K.sum(y_pred,-1) - intersection\n",
        "    iou = (intersection + smooth) / (union + smooth)\n",
        "    return iou"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "79Jic7wYc6mk"
      },
      "source": [
        "# helper function to increase the brightness\n",
        "# hue, saturation, value\n",
        "\n",
        "def modify_brightness_p(img, p=1.2):\n",
        "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
        "    h, s, v = cv2.split(hsv)\n",
        "\n",
        "    lim = 1.0/p\n",
        "    v[v > lim] = 1.0\n",
        "    v[v <= lim] *= p\n",
        "\n",
        "    final_hsv = cv2.merge((h, s, v))\n",
        "    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR).clip(min=0)\n",
        "    return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82FWBt7h9dGP"
      },
      "source": [
        "# helper code to shuffle\n",
        "\n",
        "def unison_shuffled_copies(a, b):\n",
        "    assert len(a) == len(b)\n",
        "    p = np.random.permutation(len(a))\n",
        "    return a[p], b[p]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFChKFpTU6I0"
      },
      "source": [
        "# helper code to 512 image size indices with original images\n",
        "\n",
        "def index512_resize(index,top):\n",
        "  index=index*top/512\n",
        "  return int(index)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "w7wU3WR3wsuZ"
      },
      "source": [
        "# Load the data\n",
        "h5f1 = h5py.File(os.path.join(os.path.dirname(os.getcwd()), '/content/gdrive/My Drive/Medical Image/cnn_term_paper', 'RIM_ONE_v3.hdf5'), 'r')\n",
        "h5f2 = h5py.File(os.path.join(os.path.dirname(os.getcwd()), '/content/gdrive/My Drive/Medical Image/cnn_term_paper', 'DRISHTI_GS.hdf5'), 'r')\n",
        "h5v1 = h5py.File(os.path.join(os.path.dirname(os.getcwd()), '/content/gdrive/My Drive/Medical Image/cnn_term_paper', 'DRIONS_DB.hdf5'), 'r')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYXcxzc-WNJd"
      },
      "source": [
        "# Let's define the U-Net Model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AKUbHgIhWQ7s"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82nCHO8WiREs"
      },
      "source": [
        "# U-Net: Convolutional Networks for Biomedical Image Segmentation\n",
        "# (https://arxiv.org/abs/1505.04597)\n",
        "\n",
        "def conv_block(m, dim, acti, bn, res, do=0):\n",
        "\tn = Conv2D(dim, 3, activation=acti, padding='same')(m)\n",
        "\tn = BatchNormalization()(n) if bn else n\n",
        "\tn = Dropout(do)(n) if do else n\n",
        "\tn = Conv2D(dim, 3, activation=acti, padding='same')(n)\n",
        "\tn = BatchNormalization()(n) if bn else n\n",
        "\treturn Concatenate()([m, n]) if res else n\n",
        "\n",
        "def level_block(m, dim, depth, inc, acti, do, bn, mp, up, res):\n",
        "\tif depth > 0:\n",
        "\t\tn = conv_block(m, dim, acti, bn, res)\n",
        "\t\tm = MaxPooling2D()(n) if mp else Conv2D(dim, 3, strides=2, padding='same')(n)\n",
        "\t\tm = level_block(m, int(inc*dim), depth-1, inc, acti, do, bn, mp, up, res)\n",
        "\t\tif up:\n",
        "\t\t\tm = UpSampling2D()(m)\n",
        "\t\t\tm = Conv2D(dim, 2, activation=acti, padding='same')(m)\n",
        "\t\telse:\n",
        "\t\t\tm = Conv2DTranspose(dim, 3, strides=2, activation=acti, padding='same')(m)\n",
        "\t\tn = concatenate([n, m],axis=-1)\n",
        "\t\tm = conv_block(n, dim, acti, bn, res)\n",
        "\telse:\n",
        "\t\tm = conv_block(m, dim, acti, bn, res, do)\n",
        "\treturn m\n",
        "\n",
        "def UNet(img_shape, out_ch=1, start_ch=64, depth=4, inc_rate=2., activation='relu', \n",
        "\t\t dropout=0.5, batchnorm=False, maxpool=True, upconv=True, residual=False):\n",
        "\ti = Input(shape=img_shape)\n",
        "\to = level_block(i, start_ch, depth, inc_rate, activation, dropout, batchnorm, maxpool, upconv, residual)\n",
        "\to = Conv2D(out_ch, 1, activation='sigmoid')(o)\n",
        "\treturn Model(inputs=i, outputs=o)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oNfHoZQWYyy"
      },
      "source": [
        "# We can find data preprocessing steps below\n",
        "# step1: prepare the data for our model injection\n",
        "# step2: Create train and test set by combining datasets\n",
        "# Step3: Preprocessing step for Cup Segmentation\n",
        "# step4: Shuffle the dataset\n",
        "# step5: Increase the size of dataset by Image Data Generator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BVAiDcVW-7-K"
      },
      "source": [
        "img_shape=(128,128,3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_4GQ6QZC4_E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b25b998-737e-4c75-97f8-50665af18854"
      },
      "source": [
        "# DRISHTI Dataset\n",
        "# step1: prepare the data for our model injection\n",
        "\n",
        "Xori= np.asarray(h5f2['DRISHTI-GS/orig/images'])\n",
        "disc_locations = np.asarray( h5f2['DRISHTI-GS/512 px/disc_locations'])\n",
        "FC = np.asarray(h5f2['DRISHTI-GS/512 px/file_codes'] ) \n",
        "indDRI = np.arange(0,Xori.shape[0])\n",
        "bsqside=np.maximum((disc_locations[:,3]-disc_locations[:,1]),(disc_locations[:,2]-disc_locations[:,0]))\n",
        "isize=Xori.shape[1]\n",
        "\n",
        "if (cup):\n",
        "  \n",
        "  Yf = np.asarray(h5f2['DRISHTI-GS/512 px/cup'])\n",
        " \n",
        "  Xc = [Xori[i][index512_resize(disc_locations[i][0],isize):index512_resize(disc_locations[i][0]+bsqside[i],isize), index512_resize(disc_locations[i][1],isize):index512_resize(disc_locations[i][1]+bsqside[i],isize)] \n",
        "                   for i in range(len(Xori))]\n",
        "\n",
        "  Yc=[Yf[i][disc_locations[i][0]:disc_locations[i][0]+bsqside[i], disc_locations[i][1]:disc_locations[i][1]+bsqside[i]] \n",
        "                   for i in range(len(Xori))]\n",
        "\n",
        "else:\n",
        "  Yf =np.asarray( h5f2['DRISHTI-GS/512 px/disc'])\n",
        "  \n",
        "  Xc = [Xori[i][index512_resize(50,isize):index512_resize(462,isize), index512_resize(50,isize):index512_resize(462,isize)] \n",
        "                   for i in range(len(Xori))]\n",
        "\n",
        "  Yc=[Yf[i][50:462,50:462] \n",
        "                   for i in range(len(Xori))]\n",
        "\n",
        "X=[cv2.resize(img, (128, 128),interpolation=cv2.INTER_NEAREST) for img in Xc]\n",
        "X=np.asarray(X) \n",
        "am=np.amax(X)\n",
        "X=X.astype(np.float32)/am \n",
        "print(np.amax(X),np.amin(X))\n",
        "\n",
        "Y=[cv2.resize(img, (128, 128),interpolation=cv2.INTER_NEAREST)[..., None] for img in Yc]\n",
        "Y=np.asarray(Y) \n",
        "ym=np.amax(Y) \n",
        "Yf=Y/ym\n",
        "#get binary mask\n",
        "Yb=(Y>0.5).astype(np.float32) \n",
        "\n",
        "print(np.amax(Yf),np.amin(Yf))\n",
        "print(X.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0 0.0\n",
            "1.0 0.0\n",
            "(50, 128, 128, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2e0cSAtE7dpI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6774ce2d-2680-46fc-be9e-73c4a3a22cdb"
      },
      "source": [
        "# RIM ONE V_3 Dataset\n",
        "# step1: prepare the data for our model injection\n",
        "\n",
        "Xvori= h5f1['RIM-ONE v3/orig/images']\n",
        "disc_locationsv = h5f1['RIM-ONE v3/512 px/disc_locations']\n",
        "Glauv=np.asarray(h5f1['RIM-ONE v3/512 px/is_ill'] )\n",
        "FCv=np.asarray(h5f1['RIM-ONE v3/512 px/file_codes'])\n",
        "indRIM=np.arange(0,Xvori.shape[0])\n",
        "bsqsidev=np.maximum((disc_locationsv[:,3]-disc_locationsv[:,1]),(disc_locationsv[:,2]-disc_locationsv[:,0]))\n",
        "low_cont_n=0;\n",
        "ivsize=Xvori.shape[1]\n",
        "\n",
        "if (cup):\n",
        "\n",
        "  Yvf = h5f1['RIM-ONE v3/512 px/cup']\n",
        "\n",
        "  Xvc = [Xvori[i][index512_resize(disc_locationsv[i][0],ivsize):index512_resize(disc_locationsv[i][0]+bsqsidev[i],ivsize), index512_resize(disc_locationsv[i][1],ivsize):index512_resize(disc_locationsv[i][1]+bsqsidev[i],ivsize)] \n",
        "                     for i in range(len(Xvori))]\n",
        "\n",
        "  Yvc=[Yvf[i][disc_locationsv[i][0]:disc_locationsv[i][0]+bsqsidev[i], disc_locationsv[i][1]:disc_locationsv[i][1]+bsqsidev[i]] \n",
        "                     for i in range(len(Xvori))]\n",
        "\n",
        "else:\n",
        "\n",
        "  Yvf = h5f1['RIM-ONE v3/512 px/disc']\n",
        "\n",
        "  Xvc = [Xvori[i][index512_resize(50,isize):index512_resize(462,ivsize), index512_resize(50,isize):index512_resize(462,ivsize)] \n",
        "                     for i in range(len(Xvori))]\n",
        "\n",
        "  Yvc=[Yvf[i][50:462,50:462] \n",
        "                     for i in range(len(Xvori))]\n",
        "\n",
        "\n",
        "Xvn=[cv2.resize(img, (128, 128),interpolation=cv2.INTER_NEAREST) for img in Xvc]\n",
        "Xv=np.asarray(Xvn) \n",
        "am=np.amax(Xv)\n",
        "Xv=Xv.astype(np.float32)/am \n",
        "print(np.amax(Xv),np.amin(Xv))\n",
        "\n",
        "Yv=[cv2.resize(img, (128, 128),interpolation=cv2.INTER_NEAREST)[..., None] for img in Yvc]\n",
        "Yv=np.asarray(Yv) \n",
        "ym=np.amax(Yv) \n",
        "Yvf=(Yv/ym).astype(np.float32) \n",
        "\n",
        "print(np.amax(Yvf),np.amin(Yvf))\n",
        "print(Xv.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0 0.0\n",
            "1.0 0.0\n",
            "(159, 128, 128, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bOkf5DyYMIVl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5a4a85a-de26-469d-9200-7860f26a2c04"
      },
      "source": [
        "# step2: Create train and test set by combining datasets\n",
        "\n",
        "rng = np.random.RandomState(rand_sv)\n",
        "\n",
        "#DRISHTI\n",
        "X_train1t, X_test1t, Y_train1t, Y_test1t, F_train1t, F_test1t ,ind_train1, ind_test1= train_test_split( X, Yb, FC, indDRI,test_size=0.25,random_state=rng)\n",
        "\n",
        "#RIM ONE\n",
        "X_train2, X_test2, Y_train2, Y_test2, F_train2, F_test2, ind_train2, ind_test2= train_test_split( Xv, Yvf, FCv, indRIM, test_size=0.25,random_state=rng)\n",
        "\n",
        "X_test_dri=np.copy(X_test1t)\n",
        "Y_test_dri=np.copy(Y_test1t)\n",
        "F_test_dri=np.copy(F_test1t)\n",
        "\n",
        "X_test_rim=np.copy(X_test2)\n",
        "Y_test_rim=np.copy(Y_test2)\n",
        "F_test_rim=np.copy(F_test2)\n",
        "\n",
        "if(dri or mix):\n",
        "  X_train1=np.concatenate([X_train1t,X_train1t])\n",
        "  X_train1=np.concatenate([X_train1,X_train1t])\n",
        "  X_test1=np.concatenate([X_test1t,X_test1t])\n",
        "  X_test1=np.concatenate([X_test1,X_test1t])\n",
        "\n",
        "  Y_train1=np.concatenate([Y_train1t,Y_train1t])\n",
        "  Y_train1=np.concatenate([Y_train1,Y_train1t])\n",
        "  Y_test1=np.concatenate([Y_test1t,Y_test1t])\n",
        "  Y_test1=np.concatenate([Y_test1,Y_test1t])\n",
        "\n",
        "if(mix):\n",
        "  X_train1=np.concatenate([X_train1,X_train2])\n",
        "  Y_train1=np.concatenate([Y_train1,Y_train2])\n",
        "  X_test1=np.concatenate([X_test1,X_test2])\n",
        "  Y_test1=np.concatenate([Y_test1,Y_test2])\n",
        "\n",
        "  X_train1=np.concatenate([X_train1,X_train2])\n",
        "  Y_train1=np.concatenate([Y_train1,Y_train2])\n",
        "  X_test1=np.concatenate([X_test1,X_test2])\n",
        "  Y_test1=np.concatenate([Y_test1,Y_test2])\n",
        "\n",
        "if(rim):\n",
        "  X_train1=np.concatenate([X_train2,X_train2])\n",
        "  X_test1=np.concatenate([X_test2,X_test2])\n",
        "\n",
        "  Y_train1=np.concatenate([Y_train2,Y_train2])\n",
        "  Y_test1=np.concatenate([Y_test2,Y_test2])\n",
        "\n",
        "if(dri):\n",
        "  X_train1=np.concatenate([X_train1,X_train1])\n",
        "  Y_train1=np.concatenate([Y_train1,Y_train1])\n",
        "\n",
        "X_train1=np.asarray(X_train1)\n",
        "Y_train1=np.asarray(Y_train1)\n",
        "X_test=np.asarray(X_test1)\n",
        "Y_test=np.asarray(Y_test1)\n",
        "\n",
        "X_train=np.copy(X_train1)\n",
        "Y_train=np.copy(Y_train1)\n",
        "\n",
        "size=X_train1.shape[0]\n",
        "\n",
        "X_traine=np.empty((20*size, 128, 128, 3),dtype=np.float32)\n",
        "Y_traine=np.empty((20*size, 128, 128, 1),dtype=np.float32)\n",
        "\n",
        "print(X_train1.shape)\n",
        "print(Y_train1.shape)\n",
        "print(X_traine.shape)\n",
        "print(Y_traine.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(349, 128, 128, 3)\n",
            "(349, 128, 128, 1)\n",
            "(6980, 128, 128, 3)\n",
            "(6980, 128, 128, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "1b7b99ad25c64e51bc7ab3247cb04eaf",
            "1b9fd39052594207bcb75f4bd77f5d8c",
            "af04baa7f12046c6a93a38c07ef9ade2",
            "82813a77e8374f08869687fc1088ad3c",
            "88871d25901b40e0b8034e670ffc96a9",
            "8ba00e77e2804c96b7c9d4b23ec5de15",
            "47b74da43bb44eb3bd77245ad741337d",
            "ccb7dc1399624d909429b811773c511d",
            "076b0c21adcf488b94fc2bd413e5fcde",
            "08f32d690f9c4a20a0ea3806382829fb",
            "2353921ebe204dc0891b983103415d29",
            "f5a3d6297ee9488796e1be3160667aa9",
            "fe777358072844cb9e31f149e54b7889",
            "c41cf5ec669d41f192b45e65b0340510",
            "462623180d154ccba577df2935d0c1b1",
            "e48c0bbe00f947d49fd13e5310cfe5f6"
          ]
        },
        "id": "9YdMKLwXMIqV",
        "outputId": "dacf2815-2114-4601-f4ec-66eec534c663"
      },
      "source": [
        "# Step3: Preprocessing step for Cup Segmentation\n",
        "# Perform CLAHE on each image\n",
        "\n",
        "for i in range(16):\n",
        "  X_traine[i*size:(i+1)*size]=X_train1\n",
        "  Y_traine[i*size:(i+1)*size]=Y_train1\n",
        "  \n",
        "base= 16*size\n",
        "\n",
        "for j in tqdm_notebook(range(X_train1.shape[0])):\n",
        "  X_traine[base]=skimage.exposure.equalize_adapthist(X_train1[j], clip_limit=0.04)\n",
        "  Y_traine[base]=Y_train1[j]\n",
        "  base+=1\n",
        "  X_traine[base]=skimage.exposure.equalize_adapthist(X_train1[j], clip_limit=0.02)\n",
        "  Y_traine[base]=Y_train1[j]\n",
        "  base+=1\n",
        "  X_traine[base]=modify_brightness_p(X_train1[j],0.9)\n",
        "  Y_traine[base]=Y_train1[j]\n",
        "  base+=1\n",
        "  X_traine[base]=modify_brightness_p(X_train1[j],1.1)\n",
        "  Y_traine[base]=Y_train1[j]\n",
        "  base+=1\n",
        "  \n",
        "X_testc=np.copy(X_test)\n",
        "Y_testc=np.copy(Y_test)\n",
        "\n",
        "sizev=X_testc.shape[0]\n",
        "\n",
        "X_teste=np.empty((20*sizev, 128, 128, 3),dtype=np.float32)\n",
        "Y_teste=np.empty((20*sizev, 128, 128, 1),dtype=np.float32)\n",
        "\n",
        "for i in range(8):\n",
        "  X_teste[i*sizev:(i+1)*sizev]=X_testc\n",
        "  Y_teste[i*sizev:(i+1)*sizev]=Y_testc\n",
        "  \n",
        "basev= 8*sizev\n",
        "\n",
        "for j in tqdm_notebook(range(X_testc.shape[0])):\n",
        "  X_teste[basev]=skimage.exposure.equalize_adapthist(X_testc[j], clip_limit=0.04)\n",
        "  Y_teste[basev]=Y_testc[j]\n",
        "  basev+=1\n",
        "  X_teste[basev]=skimage.exposure.equalize_adapthist(X_testc[j], clip_limit=0.02)\n",
        "  Y_teste[basev]=Y_testc[j]\n",
        "  basev+=1\n",
        "  X_teste[basev]=modify_brightness_p(X_testc[j],0.9)\n",
        "  Y_teste[basev]=Y_testc[j]\n",
        "  basev+=1\n",
        "  X_teste[basev]=modify_brightness_p(X_testc[j],1.1)\n",
        "  Y_teste[basev]=Y_testc[j]\n",
        "  basev+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b7b99ad25c64e51bc7ab3247cb04eaf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=349.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "076b0c21adcf488b94fc2bd413e5fcde",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=119.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0LN9Tqi-BWg"
      },
      "source": [
        "# step4: Shuffle the dataset\n",
        "\n",
        "X_trains,Y_trains = unison_shuffled_copies(X_traine, Y_traine)\n",
        "X_tests,Y_tests = unison_shuffled_copies(X_teste, Y_teste)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XAMuOnUGFI4o"
      },
      "source": [
        "# Provide the same seed and keyword arguments to the fit and flow methods\n",
        "\n",
        "seed = rand_sv\n",
        "batch_s = 120"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgxWT8xrXMEF"
      },
      "source": [
        "# We can find model training and evaluation steps below"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cG3TALY1_ofB"
      },
      "source": [
        "# helper function for ipython notebook\n",
        "\n",
        "cp_callbacks = tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5'), verbose=1, save_best_only=True,save_weights_only=True)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.8, patience=5, min_lr=2e-4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1e79XCvBAlpd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "42c67ac2-f3c2-483e-bfdc-1c1cac73bb96"
      },
      "source": [
        "img_shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 128, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wp2MYmAvMj4p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e292dc8e-b915-47e0-be22-13264f3b3475"
      },
      "source": [
        "# Boiler plate code to enable TPU or GPU in Google Colab\n",
        "# initialise GPU or TPU\n",
        "\n",
        "if 'COLAB_TPU_ADDR' not in  os.environ:\n",
        "  if (gpu):\n",
        "    strategy = tf.distribute.OneDeviceStrategy(device=\"/gpu:0\")\n",
        "  else:\n",
        "    strategy = tf.distribute.OneDeviceStrategy(device=\"/cpu:0\")\n",
        "else:\n",
        "  resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\n",
        "  tf.config.experimental_connect_to_cluster(resolver)\n",
        "  tf.tpu.experimental.initialize_tpu_system(resolver)\n",
        "  strategy = tf.distribute.experimental.TPUStrategy(resolver)\n",
        "\n",
        "with strategy.scope():\n",
        "  model = UNet(img_shape,start_ch=chan,upconv=False,dropout=0.5, depth=depth,inc_rate=ir, batchnorm=True)\n",
        "  model.compile(\n",
        "      optimizer=tf.keras.optimizers.Adam(learning_rate=.001, ),\n",
        "      loss=log_dice_loss, metrics=[dice_coef]\n",
        "      )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.35.252.178:8470\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.35.252.178:8470\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n",
            "WARNING:absl:`tf.distribute.experimental.TPUStrategy` is deprecated, please use  the non experimental symbol `tf.distribute.TPUStrategy` instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wnpZvh9ljeSG",
        "outputId": "287bac03-828b-4969-e36d-6981c21cbb3a"
      },
      "source": [
        "# Understand the model\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"functional_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 128, 128, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 128, 128, 64) 1792        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 128, 128, 64) 256         conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 128, 128, 64) 36928       batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 128, 128, 64) 256         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 64, 64, 64)   0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 64, 64, 70)   40390       max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 64, 64, 70)   280         conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 64, 64, 70)   44170       batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 64, 64, 70)   280         conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 32, 32, 70)   0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 32, 32, 77)   48587       max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 32, 32, 77)   308         conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 32, 32, 77)   53438       batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 32, 32, 77)   308         conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 16, 16, 77)   0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 16, 16, 84)   58296       max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 16, 16, 84)   336         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 16, 16, 84)   63588       batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_7 (BatchNor (None, 16, 16, 84)   336         conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 8, 8, 84)     0           batch_normalization_7[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 8, 8, 92)     69644       max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_8 (BatchNor (None, 8, 8, 92)     368         conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 8, 8, 92)     76268       batch_normalization_8[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_9 (BatchNor (None, 8, 8, 92)     368         conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 4, 4, 92)     0           batch_normalization_9[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 4, 4, 101)    83729       max_pooling2d_4[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_10 (BatchNo (None, 4, 4, 101)    404         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 4, 4, 101)    91910       batch_normalization_10[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_11 (BatchNo (None, 4, 4, 101)    404         conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2D)  (None, 2, 2, 101)    0           batch_normalization_11[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 2, 2, 111)    101010      max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_12 (BatchNo (None, 2, 2, 111)    444         conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 2, 2, 111)    0           batch_normalization_12[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 2, 2, 111)    111000      dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_13 (BatchNo (None, 2, 2, 111)    444         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose (Conv2DTranspo (None, 4, 4, 101)    101000      batch_normalization_13[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 4, 4, 202)    0           batch_normalization_11[0][0]     \n",
            "                                                                 conv2d_transpose[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 4, 4, 101)    183719      concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_14 (BatchNo (None, 4, 4, 101)    404         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_15 (Conv2D)              (None, 4, 4, 101)    91910       batch_normalization_14[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_15 (BatchNo (None, 4, 4, 101)    404         conv2d_15[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_1 (Conv2DTrans (None, 8, 8, 92)     83720       batch_normalization_15[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 8, 8, 184)    0           batch_normalization_9[0][0]      \n",
            "                                                                 conv2d_transpose_1[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_16 (Conv2D)              (None, 8, 8, 92)     152444      concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_16 (BatchNo (None, 8, 8, 92)     368         conv2d_16[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_17 (Conv2D)              (None, 8, 8, 92)     76268       batch_normalization_16[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_17 (BatchNo (None, 8, 8, 92)     368         conv2d_17[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_2 (Conv2DTrans (None, 16, 16, 84)   69636       batch_normalization_17[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 16, 16, 168)  0           batch_normalization_7[0][0]      \n",
            "                                                                 conv2d_transpose_2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_18 (Conv2D)              (None, 16, 16, 84)   127092      concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_18 (BatchNo (None, 16, 16, 84)   336         conv2d_18[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_19 (Conv2D)              (None, 16, 16, 84)   63588       batch_normalization_18[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_19 (BatchNo (None, 16, 16, 84)   336         conv2d_19[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_3 (Conv2DTrans (None, 32, 32, 77)   58289       batch_normalization_19[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_3 (Concatenate)     (None, 32, 32, 154)  0           batch_normalization_5[0][0]      \n",
            "                                                                 conv2d_transpose_3[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_20 (Conv2D)              (None, 32, 32, 77)   106799      concatenate_3[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_20 (BatchNo (None, 32, 32, 77)   308         conv2d_20[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_21 (Conv2D)              (None, 32, 32, 77)   53438       batch_normalization_20[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_21 (BatchNo (None, 32, 32, 77)   308         conv2d_21[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_4 (Conv2DTrans (None, 64, 64, 70)   48580       batch_normalization_21[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_4 (Concatenate)     (None, 64, 64, 140)  0           batch_normalization_3[0][0]      \n",
            "                                                                 conv2d_transpose_4[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_22 (Conv2D)              (None, 64, 64, 70)   88270       concatenate_4[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_22 (BatchNo (None, 64, 64, 70)   280         conv2d_22[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_23 (Conv2D)              (None, 64, 64, 70)   44170       batch_normalization_22[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_23 (BatchNo (None, 64, 64, 70)   280         conv2d_23[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_transpose_5 (Conv2DTrans (None, 128, 128, 64) 40384       batch_normalization_23[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_5 (Concatenate)     (None, 128, 128, 128 0           batch_normalization_1[0][0]      \n",
            "                                                                 conv2d_transpose_5[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_24 (Conv2D)              (None, 128, 128, 64) 73792       concatenate_5[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_24 (BatchNo (None, 128, 128, 64) 256         conv2d_24[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_25 (Conv2D)              (None, 128, 128, 64) 36928       batch_normalization_24[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_25 (BatchNo (None, 128, 128, 64) 256         conv2d_25[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_26 (Conv2D)              (None, 128, 128, 1)  65          batch_normalization_25[0][0]     \n",
            "==================================================================================================\n",
            "Total params: 2,389,538\n",
            "Trainable params: 2,385,190\n",
            "Non-trainable params: 4,348\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pghPxpH03FVN"
      },
      "source": [
        "# if(preload):\n",
        "#   model.load_weights(os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper/model1_cup_last_check_point.hdf5'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsNQz_UP8SEM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0377a3c-d1cc-431e-a3e3-0cb30ca47a89"
      },
      "source": [
        "X_tests.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2380, 128, 128, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lc99NWpsgmIr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e74830d8-3e25-4f75-e986-885f8f6a2024"
      },
      "source": [
        "# Model Training Step \n",
        "\n",
        "if(train):\n",
        "  history = model.fit(\n",
        "    X_trains,Y_trains,\n",
        "    epochs=Epochs,\n",
        "    batch_size=128,\n",
        "    steps_per_epoch=35,\n",
        "    validation_data = (X_tests,Y_tests),\n",
        "    callbacks=[cp_callbacks,reduce_lr],\n",
        "    verbose=1\n",
        "  )\n",
        "\n",
        "# save the trained model weights\n",
        "model.save_weights(os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper/model1_cup_last_check_point.hdf5'), overwrite=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Iterator.get_next_as_optional()` instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Iterator.get_next_as_optional()` instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " 2/35 [>.............................] - ETA: 2s - loss: 1.1389 - dice_coef: 0.3409WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_end` time: 0.1129s). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Callbacks method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0062s vs `on_train_batch_end` time: 0.1129s). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "35/35 [==============================] - ETA: 0s - loss: 0.3620 - dice_coef: 0.7162WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0033s vs `on_test_batch_end` time: 0.0356s). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0033s vs `on_test_batch_end` time: 0.0356s). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.71306, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 24s 695ms/step - loss: 0.3620 - dice_coef: 0.7162 - val_loss: 0.7131 - val_dice_coef: 0.4929\n",
            "Epoch 2/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.1314 - dice_coef: 0.8778\n",
            "Epoch 00002: val_loss did not improve from 0.71306\n",
            "35/35 [==============================] - 23s 652ms/step - loss: 0.1314 - dice_coef: 0.8778 - val_loss: 1.0784 - val_dice_coef: 0.3427\n",
            "Epoch 3/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0824 - dice_coef: 0.9212\n",
            "Epoch 00003: val_loss did not improve from 0.71306\n",
            "35/35 [==============================] - 5s 151ms/step - loss: 0.0824 - dice_coef: 0.9212 - val_loss: 0.9341 - val_dice_coef: 0.3959\n",
            "Epoch 4/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0600 - dice_coef: 0.9417\n",
            "Epoch 00004: val_loss did not improve from 0.71306\n",
            "35/35 [==============================] - 5s 148ms/step - loss: 0.0600 - dice_coef: 0.9417 - val_loss: 1.2454 - val_dice_coef: 0.2918\n",
            "Epoch 5/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0440 - dice_coef: 0.9571\n",
            "Epoch 00005: val_loss did not improve from 0.71306\n",
            "35/35 [==============================] - 5s 148ms/step - loss: 0.0440 - dice_coef: 0.9571 - val_loss: 1.0697 - val_dice_coef: 0.3461\n",
            "Epoch 6/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0336 - dice_coef: 0.9671\n",
            "Epoch 00006: val_loss did not improve from 0.71306\n",
            "35/35 [==============================] - 5s 150ms/step - loss: 0.0336 - dice_coef: 0.9671 - val_loss: 0.7364 - val_dice_coef: 0.4826\n",
            "Epoch 7/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0253 - dice_coef: 0.9749\n",
            "Epoch 00007: val_loss improved from 0.71306 to 0.67326, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 179ms/step - loss: 0.0253 - dice_coef: 0.9749 - val_loss: 0.6733 - val_dice_coef: 0.5131\n",
            "Epoch 8/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0228 - dice_coef: 0.9774\n",
            "Epoch 00008: val_loss improved from 0.67326 to 0.56027, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 171ms/step - loss: 0.0228 - dice_coef: 0.9774 - val_loss: 0.5603 - val_dice_coef: 0.5732\n",
            "Epoch 9/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0194 - dice_coef: 0.9808\n",
            "Epoch 00009: val_loss improved from 0.56027 to 0.43748, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 172ms/step - loss: 0.0194 - dice_coef: 0.9808 - val_loss: 0.4375 - val_dice_coef: 0.6479\n",
            "Epoch 10/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0173 - dice_coef: 0.9828\n",
            "Epoch 00010: val_loss did not improve from 0.43748\n",
            "35/35 [==============================] - 5s 148ms/step - loss: 0.0173 - dice_coef: 0.9828 - val_loss: 0.4450 - val_dice_coef: 0.6439\n",
            "Epoch 11/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0159 - dice_coef: 0.9842\n",
            "Epoch 00011: val_loss improved from 0.43748 to 0.31059, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 176ms/step - loss: 0.0159 - dice_coef: 0.9842 - val_loss: 0.3106 - val_dice_coef: 0.7346\n",
            "Epoch 12/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0166 - dice_coef: 0.9835\n",
            "Epoch 00012: val_loss did not improve from 0.31059\n",
            "35/35 [==============================] - 5s 148ms/step - loss: 0.0166 - dice_coef: 0.9835 - val_loss: 0.3405 - val_dice_coef: 0.7148\n",
            "Epoch 13/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0167 - dice_coef: 0.9834\n",
            "Epoch 00013: val_loss improved from 0.31059 to 0.28180, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 179ms/step - loss: 0.0167 - dice_coef: 0.9834 - val_loss: 0.2818 - val_dice_coef: 0.7565\n",
            "Epoch 14/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0141 - dice_coef: 0.9860\n",
            "Epoch 00014: val_loss improved from 0.28180 to 0.26500, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 171ms/step - loss: 0.0141 - dice_coef: 0.9860 - val_loss: 0.2650 - val_dice_coef: 0.7691\n",
            "Epoch 15/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0131 - dice_coef: 0.9870\n",
            "Epoch 00015: val_loss improved from 0.26500 to 0.22329, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 174ms/step - loss: 0.0131 - dice_coef: 0.9870 - val_loss: 0.2233 - val_dice_coef: 0.8009\n",
            "Epoch 16/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0128 - dice_coef: 0.9873\n",
            "Epoch 00016: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 149ms/step - loss: 0.0128 - dice_coef: 0.9873 - val_loss: 0.2868 - val_dice_coef: 0.7535\n",
            "Epoch 17/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0142 - dice_coef: 0.9859\n",
            "Epoch 00017: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 149ms/step - loss: 0.0142 - dice_coef: 0.9859 - val_loss: 0.3316 - val_dice_coef: 0.7221\n",
            "Epoch 18/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0129 - dice_coef: 0.9872\n",
            "Epoch 00018: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 152ms/step - loss: 0.0129 - dice_coef: 0.9872 - val_loss: 0.3240 - val_dice_coef: 0.7273\n",
            "Epoch 19/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0114 - dice_coef: 0.9887\n",
            "Epoch 00019: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 150ms/step - loss: 0.0114 - dice_coef: 0.9887 - val_loss: 0.2977 - val_dice_coef: 0.7456\n",
            "Epoch 20/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0113 - dice_coef: 0.9888\n",
            "Epoch 00020: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 154ms/step - loss: 0.0113 - dice_coef: 0.9888 - val_loss: 0.2970 - val_dice_coef: 0.7463\n",
            "Epoch 21/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0104 - dice_coef: 0.9896\n",
            "Epoch 00021: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 148ms/step - loss: 0.0104 - dice_coef: 0.9896 - val_loss: 0.3587 - val_dice_coef: 0.7040\n",
            "Epoch 22/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0103 - dice_coef: 0.9896\n",
            "Epoch 00022: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 149ms/step - loss: 0.0103 - dice_coef: 0.9896 - val_loss: 0.3380 - val_dice_coef: 0.7177\n",
            "Epoch 23/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0106 - dice_coef: 0.9895\n",
            "Epoch 00023: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 155ms/step - loss: 0.0106 - dice_coef: 0.9895 - val_loss: 0.3304 - val_dice_coef: 0.7229\n",
            "Epoch 24/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0102 - dice_coef: 0.9898\n",
            "Epoch 00024: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 155ms/step - loss: 0.0102 - dice_coef: 0.9898 - val_loss: 0.3702 - val_dice_coef: 0.6962\n",
            "Epoch 25/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0097 - dice_coef: 0.9903\n",
            "Epoch 00025: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 150ms/step - loss: 0.0097 - dice_coef: 0.9903 - val_loss: 0.3524 - val_dice_coef: 0.7082\n",
            "Epoch 26/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0092 - dice_coef: 0.9908\n",
            "Epoch 00026: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 150ms/step - loss: 0.0092 - dice_coef: 0.9908 - val_loss: 0.3265 - val_dice_coef: 0.7258\n",
            "Epoch 27/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0094 - dice_coef: 0.9906\n",
            "Epoch 00027: val_loss did not improve from 0.22329\n",
            "35/35 [==============================] - 5s 151ms/step - loss: 0.0094 - dice_coef: 0.9906 - val_loss: 0.2817 - val_dice_coef: 0.7572\n",
            "Epoch 28/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0093 - dice_coef: 0.9907\n",
            "Epoch 00028: val_loss improved from 0.22329 to 0.21483, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 180ms/step - loss: 0.0093 - dice_coef: 0.9907 - val_loss: 0.2148 - val_dice_coef: 0.8078\n",
            "Epoch 29/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0090 - dice_coef: 0.9911\n",
            "Epoch 00029: val_loss did not improve from 0.21483\n",
            "35/35 [==============================] - 5s 149ms/step - loss: 0.0090 - dice_coef: 0.9911 - val_loss: 0.2632 - val_dice_coef: 0.7710\n",
            "Epoch 30/30\n",
            "35/35 [==============================] - ETA: 0s - loss: 0.0091 - dice_coef: 0.9909\n",
            "Epoch 00030: val_loss improved from 0.21483 to 0.21017, saving model to /content/gdrive/My Drive/Medical Image/cnn_term_paper/last_check_point.hdf5\n",
            "35/35 [==============================] - 6s 179ms/step - loss: 0.0091 - dice_coef: 0.9909 - val_loss: 0.2102 - val_dice_coef: 0.8113\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vj6D5YnGJ-nq"
      },
      "source": [
        "# Load the pre trained model's weights\n",
        "\n",
        "if(train):\n",
        "  model.load_weights(os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper/model1_cup_last_check_point.hdf5'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z12tlBDr9E8O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f31e492d-c1c7-4242-ae76-ea91109a6ff6"
      },
      "source": [
        "# evaluate the model with the test data\n",
        "\n",
        "result = model.evaluate(X_tests[0:700,:,:,:],Y_tests[0:700,:,:,:])\n",
        "print(\"log dice loss for test set = \", result[0], ' ||| ',\"dice coeffecient (accuracy) for test set = \", result[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\r 1/22 [>.............................] - ETA: 1:12 - loss: 3.2481 - dice_coef: 0.6483WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0038s vs `on_test_batch_end` time: 0.0225s). Check your callbacks.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0038s vs `on_test_batch_end` time: 0.0225s). Check your callbacks.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "22/22 [==============================] - 4s 183ms/step - loss: 0.7736 - dice_coef: 0.7790\n",
            "log dice loss for test set =  0.7735503911972046  |||  dice coeffecient (accuracy) for test set =  0.7789580821990967\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNxqqB4RXVNF"
      },
      "source": [
        "# Visualise the change in accuracies and losses during training "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ta8svhOnN0s2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "31f7d714-bae8-4a10-caf3-8d091e5c5973"
      },
      "source": [
        "# plot train and test log dice loss\n",
        "\n",
        "if(train):\n",
        "  loss = history.history['loss']\n",
        "  val_loss=history.history['val_loss']\n",
        "  acc=history.history['dice_coef']\n",
        "  epochs = range(1, len(loss) + 1)\n",
        "  plt.figure(figsize=(8, 8))\n",
        "  plt.plot(epochs, loss, 'r', label='Training loss')\n",
        "  plt.plot(epochs, val_loss, 'b', label='Test loss')\n",
        "  plt.title('Training and test loss')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.legend()\n",
        "  plt.savefig(os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper','loss'+'model1_cup'+'.png'),dpi=600)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7Edl_4DOET0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "abd716d1-845b-4d88-fba8-22756319230b"
      },
      "source": [
        "# plot train and test dice coeffecient (accuracy)\n",
        "if(train):\n",
        "  loss = history.history['loss']\n",
        "  acc=history.history['dice_coef']\n",
        "  val_acc=history.history['val_dice_coef']\n",
        "  epochs = range(1, len(acc) + 1)\n",
        "  plt.figure(figsize=(8, 8))\n",
        "  plt.plot(epochs, acc, 'r', label='Dice')\n",
        "  plt.plot(epochs, val_acc, 'b', label='Test dice')\n",
        "  plt.title('Training and test Dice')\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel('Acc')\n",
        "  plt.legend()\n",
        "  plt.savefig(os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper','hist'+'model1_cup'+'.png'),dpi=600)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KqzTU_uoOICD"
      },
      "source": [
        "### We will Visualise how our algorithm performs on our Combined test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hglC8YtD0Qd3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "565fe48e-b3db-40d3-bbd0-be27524bab95"
      },
      "source": [
        "test_s=int(X_testc.shape[0])\n",
        "test_s=test_s-test_s%8\n",
        "test_s"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "112"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MTsLU6aHfH6r"
      },
      "source": [
        "# Defining metrics again\n",
        "\n",
        "smooth=1e-07\n",
        "def np_dice_coef(y_true, y_pred):\n",
        "    y_true_f = y_true.flatten()\n",
        "    y_pred_f = y_pred.flatten()\n",
        "    intersection = np.sum(y_true_f * y_pred_f)\n",
        "    return ( (2. * intersection + smooth) / \n",
        "             (np.sum(y_true_f) + np.sum(y_pred_f) + smooth) )\n",
        "    \n",
        "def np_iou(y_true, y_pred):\n",
        "    y_true_f = y_true.flatten()\n",
        "    y_pred_f = y_pred.flatten()\n",
        "    intersection = np.sum(y_true_f * y_pred_f)\n",
        "    return ( ( intersection + smooth) / \n",
        "             (np.sum(y_true_f) + np.sum(y_pred_f) - intersection + smooth) )   "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jITi6M5if30f"
      },
      "source": [
        "# importing helper libraries for visualisation part\n",
        "\n",
        "from skimage.morphology import dilation\n",
        "from skimage.feature import canny\n",
        "from skimage import color"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-MAss7tr_-h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621,
          "referenced_widgets": [
            "8b8bd1556f214f8a91ff82cbe494afce",
            "35f18b2299af4abc9945219e8acef5e7",
            "476b3436b1c24e82903005898cd9e2ee",
            "040d9b2a35e2484fb5d8c0eba7ab95db",
            "820354a946fc42909af1396c3f7d12b6",
            "c81766a827fa459bb4e1daa85d5f2076",
            "382cdb65f5ab4613921b1b760724e734",
            "fec1bb894a8f4480aba3cd6358980d82"
          ]
        },
        "outputId": "728ca60c-bf52-4192-e800-50e43220b729"
      },
      "source": [
        "%%time\n",
        "pred_dice=np.empty((test_s), dtype=np.float32)\n",
        "pred_iou=np.empty((test_s), dtype=np.float32)\n",
        "\n",
        "batch_X = X_testc[0:test_s]\n",
        "batch_y = Y_testc[0:test_s]\n",
        "print (batch_X.shape)\n",
        "    \n",
        "predv = (model.predict(batch_X,batch_size=test_s)[...,0]>0.5).astype(np.float32)\n",
        "corrv= (batch_y)[ ..., 0].astype(np.float32)\n",
        "predv1=predv.copy()\n",
        "\n",
        "\n",
        "for i in tqdm_notebook(range(predv.shape[0])):\n",
        "    img = X_test[i]\n",
        "    pred = predv[i]\n",
        "    corr = corrv[i]\n",
        "    label_pc = skimage.measure.label(pred, connectivity=pred.ndim)\n",
        "    props_pc = skimage.measure.regionprops(label_pc)\n",
        "    sz=len(props_pc)\n",
        "    if(sz)!=0:\n",
        "      areap=[props_pc[i].area for i in range(sz)]\n",
        "      k=np.argmax(areap)\n",
        "      predv1[i]=np.equal(label_pc,k+1).astype(int).astype(np.float32)\n",
        "    pred1= predv1[i]\n",
        "    cur_dice = np_dice_coef(pred1, corr)\n",
        "    cur_iou = np_iou(pred1,corr)\n",
        "    pred_dice[i]=(cur_dice)\n",
        "    pred_iou[i]=cur_iou\n",
        "    \n",
        "    \n",
        "d_worst=np.amin(pred_dice)\n",
        "d_best=np.amax(pred_dice)\n",
        "d_mean=np.mean(pred_dice)\n",
        "d_std=np.std(pred_dice)\n",
        "print(\"Dice mean=\",d_mean, \" Std=\",d_std, \" best=\",d_best,\" worst=\",d_worst)\n",
        "\n",
        "i_worst=np.amin(pred_iou)\n",
        "i_best=np.amax(pred_iou)\n",
        "i_mean=np.mean(pred_iou)\n",
        "i_std=np.std(pred_iou)\n",
        "print(\"IOU mean=\",i_mean, \" Std=\",i_std, \" best=\",i_best,\" worst=\",i_worst)\n",
        "\n",
        "ix_best=np.argmax(pred_dice)\n",
        "ix_worst=np.argmin(pred_dice)\n",
        "\n",
        "j=0\n",
        "for i in [ix_best,ix_worst]:\n",
        "    print('image #{}'.format(i))\n",
        "    img = X_test[i]\n",
        "    pred1 = predv1[i]\n",
        "    corr = corrv[i]\n",
        "    print(pred_dice[i])\n",
        "    fig = plt.figure(figsize=(9, 4))\n",
        "    ax = fig.add_subplot(1, 3, 1)\n",
        "    edgesp = canny(pred1)\n",
        "    edgesp=1.0-dilation(edgesp)\n",
        "    edgesp = color.gray2rgb(1.0*edgesp)\n",
        "    ax.imshow(img*edgesp)\n",
        "    ax.set_title('Predicted')\n",
        "    ax = fig.add_subplot(1, 3, 2)\n",
        "    edgesc = canny(corr)\n",
        "    edgesc=1.0-dilation(edgesc)\n",
        "    edgesc = color.gray2rgb(1.0*edgesc)\n",
        "    ax.imshow(img*edgesc)\n",
        "    ax.set_title('Correct')\n",
        "    ax = fig.add_subplot(1, 3, 3)\n",
        "    ax.imshow(img)\n",
        "    ax.set_title('Image')\n",
        "    fig.savefig(os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper','img_test_set'+str(j)+'model1_cup'+'.png'),dpi=600)\n",
        "    j+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(112, 128, 128, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8b8bd1556f214f8a91ff82cbe494afce",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=112.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Dice mean= 0.84788764  Std= 0.09471012  best= 0.97775126  worst= 0.48906642\n",
            "IOU mean= 0.74676996  Std= 0.13264243  best= 0.95647097  worst= 0.32368493\n",
            "image #76\n",
            "0.97775126\n",
            "image #70\n",
            "0.48906642\n",
            "CPU times: user 6.51 s, sys: 96.3 ms, total: 6.6 s\n",
            "Wall time: 12.1 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ntk_NOsz8ST5"
      },
      "source": [
        "\n",
        "\n",
        "### Visualise our segmentation algorithm performs on RIM ONE dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WJGAme4KlzH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ed52cf5-1733-4c45-dfe8-cfb1e81a9480"
      },
      "source": [
        "test_s=int(X_test_rim.shape[0])\n",
        "test_s"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpCoo2imkL5O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585,
          "referenced_widgets": [
            "3f24c3ae52af42b9bfc73d398c3fab9e",
            "33c8d2206f4b43dda6aa7ac2df6bb1ef",
            "b64ba000c9134289b18465010fb07be6",
            "34337b52f8e644839c5d5fbe05604fd9",
            "bc9343e820cf431280952573407ed656",
            "b462108f716b445f8256baa9dd7fbb9a",
            "4d0de85bc171494abfaa8f57508a3d3c",
            "02882af38af740e38b91cfd72864eb96"
          ]
        },
        "outputId": "d487c7eb-1193-4724-c2af-0ce09fc5cce6"
      },
      "source": [
        "\n",
        "pred_dice=np.empty((test_s), dtype=np.float32)\n",
        "pred_iou=np.empty((test_s), dtype=np.float32)\n",
        "\n",
        "batch_X = X_test_rim[0:test_s]\n",
        "batch_y = Y_test_rim[0:test_s]\n",
        "print (batch_X.shape)\n",
        "    \n",
        "predv = (model.predict(batch_X,batch_size=test_s)[...,0]>0.5).astype(np.float32)\n",
        "corrv= (batch_y)[ ..., 0].astype(np.float32)\n",
        "predv1=predv.copy()\n",
        "\n",
        "for i in tqdm_notebook(range(predv.shape[0])):\n",
        "    img = X_test_rim[i]\n",
        "    pred = predv[i]\n",
        "    corr = corrv[i]\n",
        "    label_pc = skimage.measure.label(pred, connectivity=pred.ndim)\n",
        "    props_pc = skimage.measure.regionprops(label_pc)\n",
        "    sz=len(props_pc)\n",
        "    if(sz)!=0:\n",
        "      areap=[props_pc[i].area for i in range(sz)]\n",
        "      k=np.argmax(areap)\n",
        "      predv1[i]=np.equal(label_pc,k+1).astype(int).astype(np.float32)\n",
        "    pred1= predv1[i]\n",
        "    cur_dice = np_dice_coef(pred1, corr)\n",
        "    cur_iou = np_iou(pred1,corr)\n",
        "    pred_dice[i]=(cur_dice)\n",
        "    pred_iou[i]=cur_iou\n",
        "    \n",
        "    \n",
        "d_worst=np.amin(pred_dice)\n",
        "d_best=np.amax(pred_dice)\n",
        "d_mean=np.mean(pred_dice)\n",
        "d_std=np.std(pred_dice)\n",
        "print(\"Dice mean=\",d_mean, \" Std=\",d_std, \" best=\",d_best,\" worst=\",d_worst)\n",
        "\n",
        "i_worst=np.amin(pred_iou)\n",
        "i_best=np.amax(pred_iou)\n",
        "i_mean=np.mean(pred_iou)\n",
        "i_std=np.std(pred_iou)\n",
        "print(\"IOU mean=\",i_mean, \" Std=\",i_std, \" best=\",i_best,\" worst=\",i_worst)\n",
        "\n",
        "ix_best=np.argmax(pred_dice)\n",
        "ix_worst=np.argmin(pred_dice)\n",
        "\n",
        "j=0\n",
        "for i in [ix_best,ix_worst]:\n",
        "    print('image #{}'.format(i))\n",
        "    img = X_test_rim[i]\n",
        "    pred = predv1[i]\n",
        "    corr = corrv[i]\n",
        "    print(pred_dice[i])\n",
        "    fig = plt.figure(figsize=(9, 4))\n",
        "    ax = fig.add_subplot(1, 3, 1)\n",
        "    edgesp = canny(pred)\n",
        "    edgesp=1.0-dilation(edgesp)\n",
        "    edgesp = color.gray2rgb(1.0*edgesp)\n",
        "    ax.imshow(img*edgesp)\n",
        "    ax.set_title('Predicted')\n",
        "    ax = fig.add_subplot(1, 3, 2)\n",
        "    edgesc = canny(corr)\n",
        "    edgesc=1.0-dilation(edgesc)\n",
        "    edgesc = color.gray2rgb(1.0*edgesc)\n",
        "    ax.imshow(img*edgesc)\n",
        "    ax.set_title('Correct')\n",
        "    ax = fig.add_subplot(1, 3, 3)\n",
        "    ax.imshow(img)\n",
        "    ax.set_title('Image')\n",
        "    fig.savefig(os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper','img_rim_one'+str(j)+'model1_cup'+'.png'),dpi=600)\n",
        "    j+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(40, 128, 128, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3f24c3ae52af42b9bfc73d398c3fab9e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=40.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Dice mean= 0.8241146  Std= 0.100919105  best= 0.977521  worst= 0.48906642\n",
            "IOU mean= 0.71260643  Std= 0.13796121  best= 0.9560304  worst= 0.32368493\n",
            "image #37\n",
            "0.977521\n",
            "image #31\n",
            "0.48906642\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DlwUJRvYSQPXBuTX2URa/mTEEfYCNxK0mMVbxVbMywyMH1hveGp6dMOp+RtLSIAfn1blQ5CVzuveCMRI7OVi5GpsEATmSYeXWtGiUN9Vqsgman0gMIsrVh6HaTFRMU1Qs2JNlORFjZ0RN8JDjEp3AF6tFI2r2mZVbdIwdEE3XnQAIKxGbYERgdxYEqRp+yJPqt3dw+ODwweGPhsNw8Pjg8VPkscZb8uy5iUvvomgo0jQVtNO9B4DY9w2zBWQEQY5RVnYTp9Xpo9xsGo5hWA5Yb8oiiyBiuocMhoLso2Jr1tj3jfvnhsunu8gYI9E+KlY5krY21uH0fktGZzi0HbLV4AfQM2GHNcVDu3A+n5BDZLBnoj54cNNoIR4meCT0evjL451B0Hwhw+sBZtLmI+590BKUhhmYsixcGkNX16ezritjDCIHXYPlShhqYmQE00hlyIiorZC7MzJroo0swiNGGmsZ9UVuiaLXwrbvZMJisDbIGGwjCMGpGVoNp4RMNsp+HvB4IWsDaGXFi+nVJUvk1YwBtSWirOjrOIdqV6EYNU7myIzBWxP9WePg8MHhl53DcPD44PE7w+MXwvCwhLZ3OgI5GfCw7zQXYgc3osG+j3o4VgMYtxfMxNoENJoZjjGyRmN1YyeIUdabMum9s0iA4U00vzsEybbdsrYTQ45skFETjNZZzjCy0UYwJIKy9hT1gLZJuHvdYdtxc1prWKjckJeBm7hZF3oXY4Bl8qAZww21usbIgdSYzjvUqs3hUGDDWL1VKlgGys5pWegj2S8XOl7ioQweG5zeILdK2cqAFBEiI4lTWce3lw1rjpXRy8gSI5kZ294p728tMmVr77SsOCBRbrdwwyVWE8up9mypIEeveK2JoWRXWfv11Jxgx+fOaR+DVV7xY9U5ZTNdbnRSFUNNQdjV7oYewVDS9/3ZE/ZNcHD44PDLzmE4eHzw+OnxOB7rPv5uvBCGh7uwBdQHhtHkeDsR6rSlXEglcKpcYyV4iPVeo28DtXooOaaV6GV1jtFY1zP75ULZwobJ6Zl4q0HKZuxbJzORDJ1u2C2JMXBbkTrewHylWTJiZ9wKI9gDciakm0SLSgcbXSwJdhNocRglMhqXpJ2C01Q924yN2Z7o5HQ6w40Htj4O3yFq9wGQYEspk8desp+2lAtwjw6WjBx4lMVqMwVMthGdmdddMT4hyEbfE6felzvNnCSIvpG9QnZjzFhkXMOJCVu5Gh3Rx2DrTjsJXyEJuiiL3FYiOul1Do+g76UiX+SIxGyp2GqC28I+YNCn69Cuqfzgy9wedLYMYthjy7wEWtBaA7Z3jLtXHBw+OPyycxgOHh88fno81uMv/914IQyPzMSb0FT5miXNRVjj0i+s3sCCe60RCX0kRHKjhQ/6xmLCZOxQ8cVhnFzIRO8dX1ZidMgxXVkLIzrulVYli5n+I07msIB7sI9bHidSx0wtemCMluwPHRvBTgmSlEEzYw9j9EA43o2mAB+MTG4v4vwITj25aUXWnjAy8T2Qw7rUpB1TnOMu3MDc6CNRJkPO4pU2VmrsMQlhnHDIYFB+O3PhOIbQiKnJSiKCnoOTjJEdwohLJ+dEMDM0Ekuv95ZErZNjlIArgaznNeZ5im1BM0czTzxjr5S5yOl2TCRnaY023boWM146/40FrnITmhsZWYtRORix1hh9J1qJ1UavcSSTZm9N9meJg8MHh192DsPB44PHT4/HT8ILYnhAH5V2ZFJZSll50eU9Ss5qZAR7JORUDCec1MhRZqUyp+q4V7qVLyzNyVG/mT4xeu+g+t4Mt1VpF0FEwB60+fDchGcVe4nparPF8NNAlynxETCJaPKpcDZyutBctYsxLXC7MfbBCZX6R/PaxsCULCGGC8sECffKqz6pIXUyBs0NqVFlXmKqpeu1RqW0mdU4Rgw078OiQob7SPoYjB5kCHllupcIqvLeBeyj3JESEKVoFkziQUZizViazQkpcMOUc7wKSj1Wi9uMGUKJtPaZ8C2Y4eQSTHlbSoQWVydnjZPMGTlo1th6MoLJmynW5smEf1Y4OMzBYXipOQwHjw8ePz0ePwkvhOEBMw3r6mJjxon6AJsDNYwRoxS01ID1TFwL236LvFK/TFainZFkDKYjC7N8nNccGZBGnzErR7gZUKlXThBRMbLmVW2u0pwqdWtvop2uMawgUwyr/G+bghyAHCXCccSyOLYYsRsZHYbjM9/drIgQAT6yqspRrqqGISWLDKxSsDAwGcjZGcSM0c1U6yoLnEmmYUG5O8k6z0hElEE8DdvmzuTUtGiZeeBXhXmQI/Bp7Y5MuKZsKXEHWeXEV9nkWeBG1/SyNxYon4sLJsYoEosah+vvci54zRxFTZo5ojwupcy1bHAd0FSuxjfqDr/zODh8cPhl5zAcPD54DE+Dx0/y270Qhsf1xsoirVEfWfrqmgPiMsqiIkv5Wy6sxGSMYcgCrAq4lIAoyorMnXX1MsFCkMIcRsBO4sprYlANmkNzGDs0NxqqB9uyLN5ItATdyuq3TCLENjXVHkGTVdGcXufMVKmfW8dXY+8JPVGIxY21VUW7kR0fyTAwM1xXxxyE1SbBaJX7bqAKFEIOYgzkMEYtHAk8rslrYmQiF4rKYy/lcRAmGuIyU9KokUCpIidRKvFglsat8riJaIsYuVc++jWVKg1ZxQJdzMI1qkk9L6fMZafPWK8kZEnKZv2AKpZzrR9QKurK048YXOsBkDl3B3XRlrW7eR44OHxw+GXnMBw8Pnj8NHn81nghDA8QniWkIWuwV2vsNh7nLHeirMCsB9d64K3U32nQe5al5waLqtBKJvWVgft0G5rINFDg4SwxEFHV+Mxo7oxeB+0DxMBUx10WJzc4LQMI2mmBFDuD3KryXbrhWTnljYFGWX5jH6zu2FopRznEvndWc86nlbEP3BoLJ4he4h9zaEaQNIftknhrQC83GMZihpOMBsZgHyKGTd9aTYawueOIRnngqhLfhc4uiJFkDtrSkHlZ6AQNh0jWqGt+xGDM3QBeBLcsZfWIJHqvssK2ABW7RMnS3rC2E+aOJkkXuXWaysHXqfOe5OUijFECLCjrOyAkRgz2oBYCXXeIgbme42bx4PDB4Zedw3Dw+ODxU+PxE1j2YhgemfTobANQ0tRpgK0LrcGY+cOST6s7WTzZt+Q0xbXmRlpZdSBO6wkp2G93JJ+FcWAQeB+zdKzjZo/dQibIacWtVu7CGICVNX7ag7Ykl9FZ3CAG476xLoYug6134mGUS3AkMptV5KyKznRYTnBanIuCvAA96K9t3NwY1sTlcsGG2PadiM7qDV/FYjfYvRVl56y1LM/sRCa+LGzpEI3FkkFMqze5xMZNu0frt2zb4GZdS3k9OlqdJUrFfr/dY8ydyd6DMQJvECFOtpAMXA2zUYlcWW7FRUZFJa3inZ6M6Ayq4l+zRnNn6x2wUryrSv6eDC7qyBtGVj49TozaQclWtr7NBdCqBHEMLKofwHrdZJTDki2v24uDwweHDw7/oHDw+ODxU+Lxk+znF8LwGJm8+qijLMtSbbrgxsAGNITfnLgdO7EFPqD3gdaVEX0Weimry5u4HTu3l9e4bMmD08J26cjrs9N0i+0EJ0sUgbUSUpkb1hrc7lxVuddJgIKe4tTuweiYEhbQAE3LWY9A94y+b3jMngUqQZAH3O4Xln3hfD7TzoNcRO4BW6e/vnHzyg1LM6IPzJzE0DBO1rhkxzHMfQq7A1M9wB7ViCgMxn4hRuJylmYsYezjQnZYl/JbZq/CP5bUvXulpmU1TiBlyJLWVctAXuv515hYKZxQrkSMsrQjyJGPY4IPmnMZwWrGloNxjQxm1j0jBuDeqGZIda4kCQOXgzqeYgx7HMN1OUnnlaUs9wSGGQPRt6o38DxwcPjg8MvOYTh4fPD46fH4SS6PF8LwIOHsK9EDxiAT9jD6pTOGOJ8XGBsjB5bVO8DkINGyUqYei12GpuBphV5uoKXN8rKhKvvrwf2lYSNwayhjCqkqx/tsgWej5+wsSKl+XaBt5xU/s1mWUtqECGwJ5OLy2s5iKxmPsMqHmqE9Y2DkJtIDLVRPBitXZFNj753ldGYjqsocIrdkt0HeBKd2Zjk5j3rAUHV7pNTWGVex1Q3ZEqJESIsa0W950Bq3fQdrmHv1TlBiHgxENGdTxUB9UM2cSPYI6GV9m5JFiYVINUbv2OKMMVjPJWaKaeluKTaK3IrZtMiSjKDPPPZ69IbNzo1MF+DeO/IqW1zoNYihKd5KyEpfMzcy67PV1hJ8PY8aCAeHDw6/7Byumzl4fPD4qfCYJ1QvfTEMDyDGXkrnhGujmRGAjNvbnWZWPZmtBtBao4/A1oXIAaMsz1IEC5pYvNxOQUmD3RyXYx6lZjbDEjwMRXIdKMmmbEeTcsLMWVTV+Bpi6xvL6VRiqFEPzxfhCXkpF1XsTNHNtW9BxW+zQ2vCF2MoGZvwMCwTH4OTL0Wa4ixxOzjdnDmtC5cRKIPFKvVqMEm4riVq0l7uLlX1uJYrZwUquXORV1UauJP0mEroGCwusldcMUz0SDKDEaPEVCoXKyrBmClqnPyqiFala6UYPRkm+r5z0jK5mnSuVrqwnNcVORdbyKJ/PftgVtEr07nU40laYlY58mblgvWo7pah57dbPDh8cBhebg7DweODx0+Hx0+i8QtheEiUytfLyrsKYqsmncrCa4ai8sW7gBx4M/apvg6Vy6gynaZCei0rtE+xkk0rcM1J5gja2jDPqbKus44MgmA1B0FzsZhjKivPl5XzdJ+ZOZaGKCXyqTm3rwYjbFr9Zf2XYlvlWnMqxriWGln3GuNhHds6tBVojcigj0oRa1uynhNrTlTrxhJ7JTSMpUGoccmq2W9mU3EuFlo1Ppr3XGlQlZrVVB0dFzkeELMJUMUlYW3OJbImolVvgsq3E83aY8V7j2TJ2lJkJF058+kr/c1mUSQHFq9yvEmlXgGVOla3U0WAAkaoVOzkzFuv9LjLHrQGpKOwSjVL5oL1fBbtg8MHh192DsPB44PHT5HHT9AqvRCGBxLLukDZw8wQEsLoATlTlSz1ODd6z8r7jlFNhhLKTeSOKXCSnJlbbfA4Z3kAnkZSwhjz+SC6qixtjRvNnZOMULDOwixJqY7lyb22lBjKqu692dwh3BhiJ/pUD2+z0ZGoqFkm0SH3JLegnZ12s/Iwg/02IQyNYFkW0gz3TgyI2x1Wce/BDblUG+MdaGE0HG+wY/Roc+zK9TjmBG4yBombMxR0kqbEJC4JS2XM0zOpxtRFTHljLNBykD3wXm4/BM0qT78q4TFzyKnOh5Qrr5mXW28y2QG1qgdgVgtCBI8LC6UgR6DuZBpDlS9vc4JKYu+1C8us2ORAJbK6lhN+Hjg4fHD4ZecwHDw+ePzUePxMDA9J7wX+e+CT6jb46sz8LZI+HvgDwKcC7wN+bmb+7SceCzitjRFTGUyJZzyFRzDc8TC8WhwyeuAYj7bBaXG2rLzp1arICxjExp6BwkvJTMUjVzP61okMfHH6iFmnnumKC5TJfV+xLPdVQywqC97PJ/bLhbZWJT7LqEpxqyNVZ0N/sKIRbNkfxyavDXPcjbEHG0FmxQ/PryzkfcMssUeCSGwHPzvtbGxb8Ohyob++sVjD7lPdBpvXJEpYloXXto17S6VPjSzLVyMZUbFHz7KOzQKzQSRYJqs7ow9iLhZ7DGLuXmRVKrhJxNy9NNVqVL0cBFQp3RiDTKPPAjcRg1wFAZF1rC7Y9qRncE6n78G+j8e56gmwz1QvzTK8UXnjosRvQeN2zAlAqeQzQariPM+DxweHDw6/7Bw+eHzw+GnyOJ9gQb8dj0cH/oPM/FZJrwDfIul/Ab4I+F8z88slfQnwJcCvftKBJNC+sbgYNsuiBFVzf13YvHOWQQRjTLdfHyiM7bLR2sIqGJcg971U2GvAKIvOZ05x752kREwNVQnaTLZ9x4dYafi6YrmzbxfOq7MureKJIdyd82rkCU44aUb0xCYBWnNII25g9ROv7sZtdLCAnceV36w58lIRi4WxG6cbsbaV17ZHCNijFM+tNc73FqLXmPTbzvl0wq79kc1QlE753nllcIGAPcrFt5ych49ukQatV9neKrYjDCnx/68AACAASURBVMesUs10di5jq89a0FZBNnLsuIJ7boRgqAre7LFX/K+2PYxZA7ipUtRGDBa3ij2WUY7LwBqXHXIzHuVGRRidzNo1MHc2ZBCxU7UMg7QqsDN6KecfDbhxVVGcAJuxVD76jICnwuODwweHX3YOHzw+ePxUefwsNB6Z+QHgA/P1q5K+HfgU4POAz55f+++A//3vRXaAh5FY2CyqUk16JBH7wAdogbY02iL2qAp142FHudATvCdp1aBnCUe3xnJaaJ40SphTVdvaVO1e87uFDy9RVxOKzop418edZ/W4QcuaGH5K2iJgYfGyHtXWcgvugXlOyxXOJ+f1BwNtYtmgvuwYzh6duHQ0AkIMnPO5fvjgPQuv/52H5HCiG/1hB9s4L606E+5iPLzmpYP7TGUD3AZmxtirm2DRubEujYxOnOC21y5iEeUUy5VUEEBrVv0P0uZupfHodocQF5Ub1SnR0ckW9ozK8Tfhy4KUUzUeNQE9cRojZ3nehOydtjkWxpZZz1K9dlAYfebLZw6slVisR7nwDGFeLapvWjU0ahjhkCYsFxaHalz9zvP44PDB4ZedwwePDx4/LR5L/S059lQ0HpI+FfgM4P8GPmlOBIDvodx/T/49sCipHGUv69Nh3wbhnR6gvkHMAjAZyIMH91cudGyIHOXWM0Eg9uj014w8GbYkzatObGanjeTm3oktkt4DF6weLO60ZWFZGg/Wlc6O2glnlIuv3bB4sE9lMXTOa0PubJeBGtxz4/bRLWHBu9/VuNHCrXcevnrh4XahjSrOIhdppWyOy4X2d5JP+GE/hIftIW39OB69dmFsRbSFxummcdvh0nfydnYxHMLuLVgLFjP6bG3sS2NpVhUDIzn7yiMFp95omXQPhiCHGNqwJpb7Z3IfRDRaJJ1q2rTLWW7hJhvoEUkpsXskLYvoRqXeDa+xJ0SfgqqqzBdsodkbQeQs3XvJC6Yab/fELWgWdG0oFyySTb1S9rIaTEUMwjpLNHYL0gNPwwcknUXLc+HxweGDwy87hw8eHzx+mjy2J/DsbRsekh4A/wPw72fmD1xrzANkZkp600CPpC8GvhjgwSKW1qrJDJW3nCMAcJxMETnYI2dMTFgXLMGqcpvtPgUumeQYjy3QftkZuZSyyRIUuIz9EuBVKW9dnPNiLFOFvaqUus2KyK4ZvxO4Ei2N5dSIvnBtqtMaRRY5aMEpQi/3KqY4uoixMrI8qZGBsurpW5aYanv9gt+DtGB90IiHRtxWsZx2qfimlkZsQe4U6QXnV0TKiBg4VQQmVGlR5omaEXsV71m14D7YszPmvfvaCBPZlpk7PvA+6Dm4p8ZYg9f3nWVpnHehqFbZlzHoOSvmuWitlOp9B7VGEoyN2t2EPc79Tgva4tzkUu47qxS4ZtUgSim2iEqxKz04MC14q3z8xR2ZlXBqNnMyeGIK19Pm8cHhg8MvO4cPHh88flY8fhLeluEhaaGI/nsz8+vn298r6ZMz8wOSPhn4G2/228z8auCrAX7ojWdmktdUMtXNpYwRVVTlyracte01JVgyn+lblX5ElDq4qSaNJ8QQpNCoXPJ0kQ2IYG3iZM4iWC1Z18ayGMsqbPHH9fKVc7C9VMquRDOmFgPIxNxm4x+bD0flLrtnsFVFv/6ws8d4nDJWEh6xdXj4+sa9dcFWw1ugk8EU/Yxe6uqlwSUqrSt3GOr0h0aupTK/KrbFrMNvATMFzL2ccyOFZIyWtPOCLcaWgUylVs4aV0hyr4Ma4poH73P9WszmeEeljJmR+IwJlno6p/K9cs0NuQgrX9/Z5uIkYfMRI6tuikxR0zwvlMo6qEmRqbmolcp+zNSyHh99SsAPlscHhw8Ov+wcPnh88PhZ8TifIJJ+O1ktAn4H8O2Z+V/c+egPA18IfPn87//0kRzv2mgnJ0nMqjFQJNUtb6b3QCl6J39LmGOipegzg8et4lidLPGTgCzRy7WwiqVjBAvGqqo1f2rGeVnwVlakrw7XrKAowuM1+BkVn8t448+aPSZEZuWKmyXWRD6YRXZ2Z+wxj1vxzkiRI9m3IDawxcCFTtfvDPbLwLzhUfG/MSoNSj3ZHg4MwxcHZh41VS4XGfs2sG3QvLGPEv1485r0pzlmU3WUTOvcoOHIBhG1QWEUe63mMpKm2roWKGdONjO2vjOz5avCnVlVB/QqkQyBWT7eWUmQ0mziaDS9MQmYE+ZK5MrUr4XtukW8ls2JHs+NxweHDw6/7Bw+eHzw+Gnx+Elp4W/H4/FTgF8I/HlJf26+9x9RJP9aSf8m8H7g5/69D1UDaDNlB4OlNcbYqx2wYB9UJ74pmgkrMVKOeNwyOVql+zASLeDW8F6pPpXnQ1W9M2MxZ5FjUcVWbqxxbk6Tw+hV0jcmsYlySwVlZucA2oyRUQ8+IPdESxVtCSrdKbOYspyc5UGQj0qVnXMGJrUbEDBS9C1Z1qCd26wO2NlG0PfA+o5kLOtKeimHBYzbxJYB3sBrB1IblSB20R/ttJ6k1URwm8Vy1sZuO2NPzEXPa5ndKEER5bbb95pUFldC195gUG61Ji+iRb27phgR0MpidqkWAtesQsiM/xaxrxUJyYobrksjrT5PZixxptNdF8QRUdY2zMVmVkp8EtufKY8PDh8cftk5fPD44PFT5PET8HayWr6R6Yh7E3zOR3c0MQT4tEwpF9IrZ+f1y0birDK6giTxEIucjMS9cq7bUnnMmcE2hNJxF+dVVIviwEKc3TjhGLC645mcaawsuFqlddHwIXi1FMtMY85MeG9VlncpS69cfBADFGXHW3aW1qq1sGZFOIf3fNx9fuDysKzqrGpwStUkarCN4PVHt9xvwU0DnZzT2WjLDa/rdeyyEIPKdz9Vatl2u3F5lIyx8+BBQw8aYVV2JgeM18WyNUQn9sHqhs7AOaqU8W64O4OOYWyXHXqW+3Hb6RHE3vEBS9TuIShLd1MVnfFpmUdGqb0DWorbvVozL+eFnJ0alXAmKo+8G24BKguZufvJsXFeVx5F0KPPcRaLFjI7W4Kidkw9xrV4H8ETGPnMeXxw+ODwy87hg8cHj58ij5+AF6NyKXBiwbNcVqmkq+MRXG3/bFkNgqabLgyWJjSLp8QIhoESLBJ0i8bCmiublboYjEt2Tt75uHe/i9EHN3mCYeQAPdrJ3bB7STvfp2vDTcQG6tWZkUvnYezcHytawZuhG2Ocxf7a4HK54A36SJbTCajGPdmh7xv3372Wkc6gb/W9kaOs10j6BuPWyRW8DcwTW53Tu+7T/3Zyuz3kXj9VqtICfnLW24Rc2S9Bt422qorVXAKNgbNw4VKuxpOjs5FrIgV9T5qqSl5mr91OSyw33E5Y1C7n9VcvZAwyc/YKCBZrJEaz6obYZUW4mtucvYOLPQaKa+Ef0VaxE6wNNBIw9ilGwyARr28DmkNUwRxTkux0ayiCZTG6yvVY5YABZ2YEPJ8GWweHDw6/7ByGg8cHj58Oj++Kmz8cL4bhIQgbNF/wMPYRjJ6EGotNz1yvVs1qjhYnGVV2VuUKdJWgKTNmA52VxVWE7I3Rg3UJHjw4cf/eSru/8Mr5TAuRr3Z0qYY4kpEPO/vDVzmdV4ZdSBnuJ5ZlYbeNG0EfnaU7PqiCNCa0GvvrJbjZH21wO8hmaDF8MVICg9N7yl0pOd6TrY9y95U2h30EexerFpZWHQLX5qwfb7DcMB4O9r2EU8uycDobW4f+2sa6G2OtGKHSMGtIwXq7wP0iO1YOQBlcrCa0bUF20D5o3RBnhoqQrz8Cz4V7bqx0tqhYqE3iO29cu5mgVY+Fh1kldhcrgZKVE5BHW5XoPblIL1ekAnpUdDArKYzWp2DJ/LE70UZiLFNfXel3adUV0anyygeHDw4fHD54fPD4+fJYesG70wo4eatUHILmlV89LNj3gJwlaQlMO5ZVia01A0vcaiBHJrvK0jrJqkWxi5Mb7gunRTy4Me69srCexeKzDv/NAhfI2yS3mnR52YguuEaytLHddnDD0ko1ncbWB0RHVK8CurFfEnRiXFXJSuTQFogdzveduIhdSb9A5sJt77gC1gUk+iW4PNxIA7m4d7Py6OGF9STGWGBPopdl3E4rUtLaQo6B7W/E8cYs0Wer0MmLkZGwB9Hhnp+JeFRNlxiP06kYiVRliDMHzXJWpqtFoToRBsmOpTidGunJUMVKL7sq4z5KZR2qtLWRyYpjCM9ByiueGUGMiiQ2b7M3ZcdVlfdGJsMMl7FtO21ZaPXUSYfUVNZ/lG7qg8MHhw8Ov4GDxwePnxaPn4QXwvCAaXWOQFYinUB0jHSj9fL0mHnlF6vSuJp8KrATRbn9msrFV/HB+k5zOJ8a9+83Hrzi3LtZwI1mVSs/3aAldk40DOsLq1ZyH/QtyRTTzEMx2HuyyImpFE4qTcplpOo3RmImdA3aCnyd5Xnl3LwrcB/c/kBnv3TcZtOgKTza1bFLTdjl5GTrrEuDrOI6+SiIy4CEvgfu4K2xj0FQymyNsrbX1clTXQM5ptip0rXSOiaj5cDbwhZBzzquuSAqv105Snlu4Nh0w4IM1htjWQQ+2zsPEeqVrjUglFjebZJUVnmmVRMiqvJfugioOCKiyctdaNXiWlPB3kyYAqQqkjP7P+xj/GCEeU8NB4cPDr/sHIaDxwePnxaP35pjL4ThkQmXHjSiYoJcs4UmIZWPJ+O1VfN1Z6Cs/HKLq0K3Bq7GQKxunFbj5rxwc+OsN85yduQNT8jspEqV7c0wDEZZ0bkl3sv1lFlWpQ/IrVKLkio8Q1bpWLP5m736BqhTKpudx02AbDGS6rDISYxz0i8JNiv3mdPHAIPexb5V/wFrVXzGlmqrPAYlHhqlHF9csx2zM0YQUeSQV/pUWuWWp2ryXnO6TSUWyijruomy7EOEjJjEf2xdF/9nzje4OetaaXQ1i2q1GV67gx5zjjFT8wD51EzNZyqV2lqqEsxkdV10lWraqaI8Sug5cEsgkIwY1+9D9pJaPQ8cHD44/LJzGA4eHzx+ijx+ggH9YhgewN531Oyx9VepzDnb0gQjrmVMBikhT8INqKeTJMycZ7PqTGiLsyzGvXsLN/ec043hC+CVf20ZxDBSgSJLCS0roU+88T3yWnjFaJFYrzGtBKK6ZkPQAraFBWe/BPFoFNFHEr1SwJZ0mPG3JuO0NvK+0S7i4cML1pzLXs18xoB9B3cq1uezEI6ABrlCbHNSZZYbz2yqmSs/WwrSK3/brdobJyUaiqRU0NdUtAhcVc1ueC2AA0EEGomlVexQWSuAnMXBvaz5ZMYPldP1KnKIx6E+QZjopsd56GpeAilKGT2sUu7Kfi7l9axmAxIqBRPUsoRGEqM6QBKVGvY8cHD44PDLzmE4eHzw+Onx+El4IQwPgMwL0k01DQowiWxZ7i6yFMUIp2JLMTrbJTEbrDMWhyo1yCQYg9WdtjRO95zTK2Vht4Ua6AiwgdkbFfgqNzmKVLkwRsURr+19XU7EANsJa6ytrEcGWAzSguX+QkSwrCLO1ckwtwGvlzWcW7D6SrcBAYuLPENLo9mZSx9IjaAmSd8gmxdhtoF5B1vJte7T5MQW9H3Dhhg3VR73WjlOaZjEbnuJg6ImEmYsvgAXLAO6sMacvpU/3gjSIbbgRo0LFxKVwGjuam7WsuLDptUcFRuUfMZ8jYdjq0I0VnHNkYZ7koyqdkjMxUS4vJ7jqF2fBCPr8wzDaxirWdQw9vAq6ZyVf+7jrQVNzxoHhw8Ov+wchoPHB4+fDo/fvEB/4YUwPMzg49/1gH1EiYJUJtvogclRiJZZwiSrQXAal4BdiUVwarNbnonmyel0DxzOq2Mnx+4tLPcaiyiV9kgYQtVfeMYkG66FPh4hBu4G7jPTvNxUckPZ6LOsrVRuLGO6HW9vaedzxd1sx07i/MpCvvtMvjbQo0FGZ/QB5qynhfVm5Xbp9B+4cGqO9mBkVQvskWxjZ91P3G6d880JNbF4TfqeQbREw1FkxVxtlq1NprWarPMeWmu1u8giiFOqdLOa1Hk1YndhDmsLbCzsY+Dps0hP0mZvxNNpAas+BVjSgW3vaEnOEn11llFbOFPlnaeJM06uyW2CDRDlgmQuup5ikCzU9Y7pYkVGGyeyJz2qHoCxgFUuf9J4HqmIB4cPDr/sHIaDxwePnyaP39p192IYHsASxl49d6sITBi5DG4E53vGclqJCLZeBWjcHHPj/mlhz4Fauc2iEn043TzA2+DjPunM+V0Ly+LViZBAC6R2NE6gHdcCBDHzq90Nz0HsTqRPC7Cci8pWx1DiVEwzp+ORCHReS3ndYV0bQkRAtOD08Y1tAzqcHy1VXW8FfyD+f/bePdiy+6rv/Ky1fr99zr3dLcmybMsP8APbOIOJeYRXZrBdpgaqIAFSUCSEgAnMuCAVSIYJj0ASJjMkBamQDFAwhAkmJEPKwRmSDAWhKsNjQiAYcCA2Br8wlp+SbCxZ6u57zt6/tdb8sfa9rbZxC+th3W7fX9WV1Opzz91n7+/+3bXX+j42fcvBrFy+K5GLzhigx206D5a9I9aY94n4vpjlazaCS2ABSw76WFnLUmQhwlCdQAWxYn3bCgkPx5nInGmSKzmoodboCcjg3nv3jMszOHQUFEICxeiHQjsE2Ri5OBlK02o7ztUVJEZwbjrAcZYMSDjMwTzqdRrOJK1kehFYCevJNXvAxyASYkDOoAaeBdt5niGEgZMKWzWWx6hPfYbhMwxf7xiGMxyf4fiRw/E16o7TUXhkCu6JScUKByAIB23D1JPWq++USNnsUul/9IZosNGSFKkKpo0+KTbNnL9pw4WbD8odTksmxMiq6rSh6UQIqWs1Tys72ChGds26ViBnkXGyFfhUpJzwEERKo60oMWakTbTzxT7OcIpODO6D7bYBFWW8jAWWwObGZjJ0uyVuSprDbj9YFq9vTVjWWaslaCo5ap63tU5Og9wniy9sshcpicQ9q0W5d9iWm51KSd1Ase2WsbtMk8biR9V2y2J/O07OQl7OamsmZNbnEAHB6dkwq2ulU2eMYHhisToXxmDqMMZA1ErKFYEE9BZVUEtDUFwFR2EIxOBoGWUDvABRT1qaiWURvjyWaufZGpCkpYVXX84wfIbhMwyf4fgMx48xjmtY9EevU1F4IGuIkBSJBViZs4GGkTEYQ1YffUFUEVXONUOk6mpWJza1pBtsz00cXOjoJilqNfWa1tAosDZTZh8VboSsjGKDXHCXcu/L8qRXWf3vfZCUyU+SZBawMioF8PhcL/NAtapziWJeN+snnBs9Z/StVnzzSMZFR1rQt0acS0QGJmCujChCklqRq1R6naugJFTbibg8yIhiYx80bFrDpobDnMghiAu5tv0gyeGAUZJtq9ZpJJF1o+x3wdHlwTyqvSpZt74ItK5stx1pyZAiWpkpZJIZeCxsTJk9EKotKJKYCWGCuSAZNJGTc5Kx8pxQ2lKUpWO2eGQlOcYYOJR9shzfeQla80d9rLyXzjB8FYa/6zV/wJ337atlnHChG9/8/I85w/BpxjCc4fhsL65r/gjg+Fp9u9NReABoMWs1syZgUtrr2Z1mpVUW0RO9crO1XbV+OuswdWGalMODzuZA6dtWJjOiFEuKE7YxImTqCkaAJLWQ2qyVbIhYgRGkr2BXQZqgrRExyBqorUSfapsJjqw3YFV/9f8TKbIVEFbVcnS4654jXvGb7yp2uGrZ9y6j7Hyjwpoy66b9iuc9hemYtSNJ4GhXwhQd1VNrdZgMqZtv2e1pS8UkZ+bajqwsBYO64THEg3BYFmd3tHB0XzBmIEa5463H3xpst6v5zgRkteCcRAwqTtlW0x5B4wrRbCT1xJSsevIgqJtYRpYZT5SbXq4SuMjEAZe6YQJno4Y0ZWTNKhGqvXpNuD/K66MQw7/4hnt5zdvvJ5eSMx5j+NfuvJf791dIkhsVfvz37+RYX6lmfMbtN/Enbzt3huHThGH4qMTx8V6cS8CobkAnyI0BHe9JjpKMLosTJqDFlWA9R2c4vhrH11qnovAQVhlQSDnRCZWIl7BfBkgv6ZIWeJCaL6XWSTlO+Otb4eDQODxotK3SJiu2FMctKYCSaAXUScVqwySqFUfQZIuZ4OJIOhI1RxQRUjutHUdGG6YFbwRUq+orGROI2QnQa44n/Pzr7wFK981alL/z3iN+4nfv/mOdq6ffcsC51kiSJx52PuG284gp0aRaYZ7oiPWmKKCMCNqcZF9PQgJBaeZLGF9gS2HMwf7I2V0a7C4PciiaC2CoCN2ETYftQdIOFO91g41IhFwrYCFzIpaZpoo0B1b//wTJmiGXVK/ajpmKeMnRMta47Sggh5ZEywXUBEPoAtoUjfq7gCKUfeju3qO6PlownFlPo7/4hnvYzcG/fc17eNUd9z3o+dlH8v+8/Q+v+n/vn/fcO8+IJGqNP3140xmGH0MMw0cfjgXK4AvQbuS2IW7ETtARdBShE5r1Napz4mtxk1EFl2i979lefAXH11qnovBIsqrP1QJXRAhN9kc7wpPcO22arnwYXad9orTWoCvWoG0a7aBjW6MfTLSpnVxMSRC0EBJORjGMVSckDcKRDIgsgOtUbb2CWLWTuiLSaWos4zLNJqRRP0Oz5lqqkFbvJaW1PpqDu9+/ZxnCt/ybNz+sc/UDv/32k//+9CfdxNdPE7aFJ5gSIisJyImmIEamI9qRkUQPVG1NE4TMIDwxOr7sEd2w3+05un+w7MGjzltdk5JgTZZspmRz2KEV+YtV9CWiSCTLcDRAxeiaNIHFK55aBUSEOZyctdrKAX4sLZO66XZzeeatpv/11EJtcIc60dd8iK6KRbJ4Mujr7faRXx8tGM4oI6O//dNv4eL+Q5/rp96ypZtAwm4J7rx//0Gv+cW338Mvvr0K8a7CD37Wf8MTzzD8mGEYbnwcl0NFYrEqSETLEEzLyEykMRDYd3IecL8jBOWTlVesxKURy0JEjSxKbaNFMjU/24vpwPiQOHvYhYeIGPCbwDsz88+IyDOBVwCPB14NfGVmXlMblgHzEhjJQp6waGcXRDsewdaUNMDKxIUso5vejK12bJNsDye2FzbYRthsJsRq3qRIdQ8dfPjqt1KzrkwhckEJLBOy2nr7/RGb1onhCHUDelYWQS5B71PJuYSqfHUgqpj2k4p6twzC4T///n18079644Oey23XP/r8POAJaD+utLB+/a77+PW7Xsekwk++6AVr29PwGIxwcmq0/SG53xWL+aBajGBlmmPGvBfSyib54v0zl+5f2F12fPEKX9JEW0eoWOXWk76F7XllUa+ZJNCa4ZEgiSmMuVIeixWfLNRmYyLs9gsyFAY0UwbV9vOARWDnC64D0gndYFIGPyoCbXCgDfdBSCfmrA1Ka0OKh+CBcIZhHjKGu8n6pHn1+sGveAFPvWmCJfkvb30/3/hvX/eAk1X/GOsTEsASyct+5XX88894Lo/rq920nmH4w1lnOOZD4jhipcketybCODjcEJH03moEqEVq3Ttoc+a2o00dWQYsCXOQu4V2EdiDLVYYcUVCGcMJP4IoxclH+158rcbdI9Hx+GvA7wE3rX/+HuAfZ+YrROSHga8F/o8He5MYVeebgoTjuSA0oDF05v79Ea0p02R0K5/9zdTZHijTJLg5aNAkaWY1Z+tFmsmx7nUi2EYLGJmwksdFtcg6KUBDmnDu/CH7/R7RLXhVrsRAtgbmYBMVhSiIKWqbyiWQVpU8yf/0L17LL7/hnj/WSTy/afz63/sclLoHjvMDI+DoaHDpvTtyCb74n72K9x1dzXqfI/niX/xtXvHZz8EWQxAmNcyUi21fM1EMWbJaueI1qxsQrXHp/mB378Kl3WDsnRhRJDdq01F3po0xTcl0IPQDwxtrvLKRsuBiRfOWAC8tuelgTmMgmCaxwLxLdBZ6QqRz/+VkyWPNdxHIksSliFpdDdKLwCWl43cZuCnmjhgseEnpLJGHhugzDD9EDP9vX/6JfMWLPpbhyRjGstt/EIZf+KSb+M9/8sknGB6XB3lp4Yd/++288s1Xjxi/6lVV3PzPz3kKn/fUW88w/OGtMxx/CBw3SvIra6MlRRAxNg1QwawMtYavfhrmHB5uVhxvT/ZiW/dilmC5PLBL9Ys7HDKcLk67bcO9d19mOYqP7r34GuOWh1V4iMjTgC8A/h7wTSIiwEuAv7i+5MeB/4UHA7scA01rviQ1P2taF3JOq1baOg8cqhxst3QLZgKdku25Ldtznc22sZ16SbqyYbkni2VTbb4xSkp0wrpVRJISrSfS2zp6O8J60loj9zUDk75dWdzJNPV6GpB1niWJNcOmRuyDL/ieV/HWuy//kR/3P/6vL+bmA6WZYK2DdVQ6kzqYY7KFFEY6HgPbGJum+Bj867/8aVx+10WWewe//K77+EdvfMfJ+371r74ZSeEbP+52PucptyAiTIeNS+/fIUtV/Xa+KvsxhGVZ2O/37HaDo32wLJBz2RAbfT0nznarbLfK4U0T/Zwh2yKFBTOa1QANnN6EhrCflYbXU9I6K2ROdEmmgP3JHDvXQKKGaxGwQpKWStdOptMnwcMYy6inIZtYEroo3gGPYoCvJj+HGwM+uK1/huFHB8PntiWv7G1i6p2lyYNi+NIf7rl8ac9LP/YJfPntT+Ad+4Vv/I2ruynf/+Z380NvuZPn3XLI3//UZ5xh+EHWGY4/NI57K9lsXS1B1DAp5YunwHAkq5vwUHC8XJzJIUhTZNOIBttbz8O9M7FbIOOjci/Wa1QeD7fj8b8D3wJcWP/8eODezDwe7rwDeOqDvoskuhF8plpiVq2nZWU4z/MOnSYOpk5vRtPV078ZbVPs6q7K1pTJDJWKSS4ys4KtgTgrQUbTcE823crVbmVpK4ZJI1H2yyW6auml1xujAdohm5GtJEQqRfQRTe7ZOS/9h68iM7njvUcnbeRPf/bj+Ntf+ryyO7Bc+QAAIABJREFU1VXlabeerwRDE6xP6LSC2/eMBMVIEVooOoylO7IZdO2cvzDRDifmy8GfvvWQw49/Gt/1hio+dl405ZffcTevfNcf8uxbDvnWT34G++3CfN+ePEpcFG8wezDvBu7Jbj8Yo7ISShVV9seGMPUtB1s4vKnTDhLdrEFHHvQ2MbtjYvTWGAwWH8WET8ghxJJI1VOwEphICALaSnCSWDlhiZa7L5gyZz2ZqELvVq3XSFKcJeVEUhZCzSAbjAdjNZ1h+CFhGOBJN294+V/51GtimJYPimHbDvpkZBNsdj7WlO97/jM4fOLEX/3/3sTekyVrVvy791zir/zqm8kIvvs5T0adMwyf4fhBcexSaazajo3FWqk5SDIrY8ZMq5Mj7eHhWBeWMYhZWC4nh9siu0YX1MvtUzKZNv2jai9+VEYtIvJngLsz89Ui8uKH8P0vA14GcKEL6cWcFZEVrFWhRRYJR0VXCZfgIhzH6xS715iaYVbttNZqZqiMAlHkib0skXgsa9oeJekKWZMNITXISJp01IWWJa1CotIMTWi9Ic1JNQRDVHnbey/xD3/6Tbz+nRev+pyf98m381Uvfjp/4um30LVBltGKqqEK0hs6Tav//aCFXZlvaqUSFknKcXWmbWc0YSCcV+MF5w/4pmc9GdT5gd9/D0sm75kH75kH71uc7/7tOxjL4Ouf8kSGL+icjAxmkhGCe/n1u9e8VbXYyZbJZmNst41ztzT6AUhfSVuUsU3FTxfgdstSGu8By+yMy8nYBxKOqpXuuxKIVmWbYGloBOt2QFDXHxEcB9UyAxZZEx21ZsFST2ISiVCvTxHIOMlyOMPwI4vhz33BkzjYGs9/xuMfUQxLQE941rkNLYVv/qSPZfbkZ+74Q153zyX2kfzBxR0AP/i29/KXnvw4bm5nGD7D8YfGcSl0y9I9fJABIoVVNKrouGovbo8IjsPLOXX/vh3TLQegQjTBR/l4qCx0E4Y0lnHj78XXWg+n4/HfAl8oIp8PbKm54vcBt4hIWyvtpwHv/KO+OTN/BPgRgCcdWuaoCyyy6sdTSlvsWXM7seMOHQlVRa8pfT2VdVRHteusWnYxIA1xQVYAsc7iRCCWAr+mUGnPsVrQUgE3WfNHEYVuyMag7PAr0MgaiPDGd13kp171Ln7ut67Mq//cZz6F89uJl3ziE/ms5z2+Wnxma5VIZQqYoa2BHl+nQKXVbCwVXedxoo6wYWGhT2Ve08zAhAsNXnLbzUByx1HiXXj1e+/j7Rd33DsP/sM73gfAM/uWz7ppwzkL0lZnOWXd5IQKWoamQlejS7I9MA4PjM35BuYlYauzX1HVxQgjMxizk4vgczLvBrkoukSx3rN+TiqV31DsLjxWnwDiJFAoKRLalaM61oYXMUwk0cj15qj3O07RPJ5LnmH4kcfwi55/26OG4dkXMsEvJS++7XFIr192Crz2nksnx/PL91ziCb3xYjEed2E6w/AZjj8IxyKlLDGt61tXppAhVm/DRwDHOSXWFYxSAy1J+sB6eV7EMmDc2Hvxo9LxyMy/CfzNOg55MfA3MvMrROSVwJdSbOqXAv/uwd8LMqu6NmKN2y3QegxCk0EWXyZBU7DU8pN3oYeW9e4oZ7WymE9y1IVoWdrzDIcRaNbkkEGdMj1WkyRkYE2JfZ08Z6C9odsOU0e7QO4QaSDGHe+9xE+96p28/OfvOPk8n/rsx/NtX/oJPOnmQ5A6BkFL452jLIEF1BpqhrMmAi6DEEXNqt0oVq1PEvVGa0Lr1dbsmwlbgvDAlyBDedkznkK/pfEvfv/dvOrOe7k0D+64XCT2l7/jbs5/zG08H+WcldbeJVDNE+fAJrVrbZvSJ2U6hIMDQ6cko1qdJdWyOu4Y69NBWR8vMyxHge+i2M+AaytTJMlKQVQgi6iFxFVgLuvjWAHf0BjHkC5GmBSwJZKIsUrC6lrmWnHHh7Fnn2H49GHYLyb9FuFzn/p4JoRlFPnujReL8/BTd7+frRmf2S5wy2H/qMfwGY6v4Lh0uvXUrVoGWsYGbJRx2Wri9ZHEsa2unr46npp2dBLkSMiRqMUNuxdfq/J4NHw8vhV4hYh8F/BbwI8+2DdEwhiGaM2sBrCEo2LlEJfK0UhCqeRDQAgMp8fEtk2FKRdyOHNcRp2qvpYd9EClA8VkljSsKzYZLmUAowayuuVlKNNBIxaHdKSDbikTHFPgHKHBxZ3zj376zfzsq98NgACPu7Dhp77zJSgN0bG2TutzNumgibUtKlFPds1QaYTPgJExo+0QTChHO1mDeZKxJG0zMZ3bMN+vxG4g1mjpjMUZOyeOkq/4uNv5C09/PK+9817+7uveyf2rBPf73/5evuFpxqfalh4wKUijNOkEm1QOGmy3Rjts6IEjEwWkpvhwMkux4Ks9skljn45LPcF0V9QaSwQLgopjG5h6pTd6BBFKpNCbMaLmkJNUpoCjzJokhqgw8vgpSOopSwLMasOIrJwIwHS1cT7ZuM4w/OFi2FS46bAhoo85hvtB47+7/QKffqGz2w++9rfexn2jrvW/fPf7uD8GX/Tkm5manmH4DMeoKJkBDFIGqNHaAWwM9e1jiuNsyRCnS8JINhYsbcKXAXMFwNyIezHXoCo9IoVHZv4S8Evrf78F+PQP5/vLPGYgJiDJnMGSwYak9QrF6b3RNkrbgm0qgvlw22k3T8S5YDrfmA47bdNp1mE/k1qMYEsFP24BQUhHTYrzALTjWbVVu2nv0IGpN0ImpIP1Mk6RBUKrPfXl3/trvOFdV+bhj7sw8Ts/9IVIn0AD0w1qHWy1x83BxAWq1TewLtAaEQ1UGFHJhmgjAjy9NP3LgCVgWSBKEqZauuuDzZZZFsjLjMXRuaHbIJryvFsv8COf9Cy+/DevmJb9wDvu4suWm/nzT308vW+4HFk3at/QPJmasJkUm5RlynK+i4TFMepmP/YaKBtjgaGwD2QeqFf7cfbgsHnZEZ874MgHI1YwysA0WQbYdnPitgdaLVHKn/fyMmjJSYKjiGCtvEyaKC5OW7MfcmVR7x+i+dIZhuEFz7yZn/r2z2Kazp8aDEvvnBvKKz7lOXzhb7y+bJ6Bn77rPu6ag+943sdwdIbhj2ocC4n4AHFoHXRDMhdGtdHa6cBxHDnijjWjSRA6EJINjXYD7sXXanmcCudSFaE3ZRTNmdYaJuvFCTg3KXlesWZsm3HQBXRgh1vYJtIEU1AJSMfD0JZYarW+KD114kgkUy+ijOhEMBgkLYWWRlD+9+KCa83odFMM/BGgUhOtz/zWX+L9l684s33Ks2/l//5bn02qgJUFtMiEtlZEKCgG9UiQDWYbrGlVkhLoOejbjg0hosyV8EGOmSUWaCVtcoTp/IQeLixHpfPWcFQ6rVXry5ZEe5JNuW2z5Wc+43l84a+//kSh8Mq73s/bdoPveM7t3Pz4hlOBTW10cr9gCn3biAimyWA0hs0Eg/AFXyjQU61ZP0rysqORxcqPgeK0cxtowSIDaUFLAW9E+MqaDoavLXyCIFgowlWMLLviBvsxGKtUS0M4MGVmoaWxeKz2v8kgquX6GKwbAcOiik3bU4dhNSVw/s0LP56v+bXf5z37OubfuOciX/9f38yPfvazzjD8CK3rEcfWG7bZYlaE05DEVE8njsMJRp0Tp0ikiyM633h78TXWqSg8RGBjhpqfzI/mGMxR/v9zJFPWfC3DWULYdqNNynYStEP59Nbcz3AipYxmdNBkW4yhEEwNdGYRY0JrZifTSYUmCLokuVG01+xPtGREmDAvwud953/kvgds2F/6wmfyHX/pk9kcblFtJ3kLIxYiFnRQralQNAzRi2RXPMq0RlJBrVwAJ6mchKUqSdMJMyMaxDxjI9i3GdqMTY6NhCGMo2Ihd2200BrFHSS+HzRXfuLTn81f/a238t65Eh2DRBUed+6QvS1kShGRPCGkQr8662ax4D5WlnurIDApx8p57+hlJ4evZC5FErbnDb25k+6Q5ZhXw8TAXWiexyR3ligW/cgkqbmo42Qs1TK1YtFHgiIsmVjr1X6lCHAkNO0fefCu60bBsG1OJ4bThUOE7/+05/Ldr3sb//WeiyRw59HC1/7yW/iXn//xwBmGH+66nnAsNMQEa4JacTvEjNYMFU4tjqdM7NwB+xRiHgjckHuxXKNzdyoKjwSG+HpCG6pGVxgSTFMDAdOKWO5W3ISD1mjNGBpMHXQqUGYWBWYZMxaKSWf4jKoUwziCjXU6DciVwTuQtPLBzyQ3jWkLLlItRwApC140edc9u5Nj/7oveC5/4SXP5fZbb0IsEDohe3xkZQQgEBW2NAIsoIkwRhLLAhGowLQ5QLUTaghRN5is7TQzQiEEjmLHyd2USbeGTpDSiNkJCSIHgmHNyO7YPrg1kr/ziU/lh950F6+/b8cbLu34vrfezXfedkB7HMxzeTIUiznwlrTW66nFEpXSwwsre1nKetcv7kunbspgnTVuhH6h4c1YcIKabRr1mXJryH5hXtYkRkmalsxuF8XVLoKYFllKimEf64xxjsFWp3UDgaAY2BGOyTUGi4/iul4wfNe9C9/+z19z3WFY94FfXnjKzVu+7plP4JWm/MJ77yMS7t4N2qYIdWcYfnjresGxaSdFQQYRIF1pupJBbTr1ON7cXMqcJbKs4B18t9xQe/GpH7UIRUiJWJnNGZiCbaaSKkl9YEXKBa8p1ib61LE22EytWmUCx3G8EgUIY9UdW/nLi5WGW5Ky0I2SVeErm9sE2bQ1cKgYvEJVsO+7tOfl/+GtJ8f90s/7eL7kRR/Hc556M+Gjqj0zrLViUEcxs8kE6WhXLCuJJ0UILymZPtB1L6/op2W9GUVKm+7TRB7NEOW9b6qEgqiVYYvYGtdTuus0Q7qAJrIEn3LzAbf0uuTvH8Gvvu8SL3/DXbz0Tz2xpGVr8mLlHRToWlN8RMVUr9r+chcsnXgeBSrFyobie9lWYFOOhGgR0FTr/NbbF7BbK3Mej6wUxCgpnUfQVmLb8fmom6xu+CYGIWUMtM6KU0puFx+uJOARWtcLhi8vg//0e+85Oe7rDcPmzvPOTzz14EpnIDL50dfezZc96zb6GYYf1rpecJyWKw9k7Si0CaQYCHmd4Lhp4lIqGg0YlwdywW6YvfhaKD4VhQeUtXyo1gemTmCfDFBO3ONz/VorT2vGtgtTs5JPUXM/DaFJ2bwqiVgrM5bjL6l7wqwaeurFWsYUnQyaEsIqoTrWWSfvO9rzw//+irXzV/73z+V5T7+l5nZjwYQ1QGdC2srs9aXAIXUjNe0gXlV0VKZhFyXFKtEwr0iR0mtOSlZSpKui0hGSJgJm61RUUAJpYDQyjeJwCb5pxQSflVySP3F+wzsu73nH0cLFEfz4W97DV37ibagpPmelJfaqosERXVMlKXJTEYsoB9+55q/0MvZRBZ0UPVDcBLzmuSLVlvNMIqi5oiRTr/bfGBCj2NIiEO506zWnzbroCcflNF0b7nVT1MNh7RQK+DXh/uiu047hu99/mVe98b0nx/viT34KX/9Fn8DTnnDuusKwaPIxB53nntvyxks7IuHHXnM3f+4Zt9JmzjD8MNdpxzGaFOjAspXxl04gct3hWBX0eBy0c2wvN8xefK11agqPeRklzalLXe5nHkhrRDotV7xloumI1oyqa0My6wbRCk8uBrUxUhEMkZJbqSo0CK9KO+Y9MrJaeybIRmjbiZELUCdatdztjkZy531XZuJPu+0ck4EWTapuDryMbyIQtVUnHlVdMpBUuk1Y35BTyZgkQSIYxyFJxAoIIUdAzJC6ErIavTeGNUKFIE/kYWSsP29Cs9wHyaQfNub7FWuNWIKvecatnO/K/3XH+7hnqS3urvuOePKt54GsG7IbtCDNSK8nIAkjrZ4YMgrksSsr3xShYq4d6Yl2IzGaJcNzJRyVMY2SIE6fFFqRqSyS0ET9Sr8uNZjMcHci47hJCAnuMA+qgj/udEa1GPMx3LNPM4bffzTzc791J9/1E1dSYv/J3/hszvfaPK8nDNsWPveJN3GA8bffeMUTq61KnTMMP7x1mnEsdlzstDLvokFv4FyXOJ6mRkYy5sHYBX0B7RuWG2AvvtZ6xATjD2dlAtbr40iCKiHG4skyz3StTcVQLBWJNQiHtmqJy/wFUZz6CmbMqh0Isbalam4oEYjPyJHAaMhU4M2wqvKLU0WYIJZYb/zSa9/LV3/Pr5wc8y987+fzzKdegFTMOjZtCDt+IvC1JbeeXqkQrbY5wKYt1htQLGr02GK25pCmZW6TWufB6JWWuFrI5eyQQriSXrpzKPa5RxLiKxlIaNloG+jnV7tflBYTX/bk2/imj7sdAE/487/wVi7tnBQlG2RbSB1kBGMUGfX4CQWKnBRDWO7fV8ttlcvottMOt8WE9wRPzvVOm8oRsFINA8kBK+hxpyP11CAASV/JYWMMRIImFcWMCGnGHM6mCxKBZjKJsG1WhCvkI4LZD1ynHcP/5GfedFXR0U2RUez16xHDtlpg2wMut8+lmTjD8ENfpx3H1hs2TSuBdItjcJ3j2FToqkgTlvsHsnBD7MXXWqem49EyCNNKxSOQgIGwnRohYGIFvEk53G453Gzw+RJTXqBRcybRRDj2V7OqCNtYjZBA0uEIZDegdewmqRmgVhsxByzzEdPhhFgFF6UZoq1afQ9Y7iWbsmZo39BNSTmHoDQ1UrLIqJl1bAhdCnQesdryJu6OLwPz6rllq6q0SRSruvdqXXmFEI154PsF04RJ2S3HsiWlaSfHWu3mqLlqM+TwHG27wNGe3W6H6wfb2dpQNtv1HFvNFUckbYJlBHisMclCeBL3DTZpZIcxnGlr2KZOZUFuoXVwScZuLha6NmSpWe9IL5JTJoTj60fdihAo87KU7XFU61VIWtZTQDbDFQ7NSHeWdOYoN0TR7aML1GusU41hu4Lf7WS88Z99CVBt3usVw5928zl+7BOeyVf9zh8A8Gd/9vX80AufzfPPH5xh+GGsU41jbev4IxB2gBEB2uy6xXGuZiDLfITaZt2L9brfi49/+h+JsUcVwX/cJYJrVrsLxZQiHq0EspyA5nRTejOkK+Og5onj4hG5Wzi86Tx0VkKP1IyPhu06Y8ws4ZjCtFHkJquZ3KaIUh6JCvRNQ6LhJhxK3Xhqyj9+5e/wT3+muB2byXjVD38Rt9x8K2l70gTMEUukdcSFEYOuVlymcp9BaRUdHAWaJZz0QQ5f/x5UOxAoRl02xUzr6WFAuQzXE0ORgJxmyiwb0GDKhm+puaZCdieAw3Mb7t/skf0auKSJydWlx5f8/Ov53hc9i+cebMgR6Lakccvlmm22pgyEdEHCy/BHhRgwTYodGrQkPfBURCdog+VoxjQIracEAoaX7MqXwBDMJiRj3XSDyx4cmAENzyjbbF8zAVwQcaZQFoUh5SgIMKOc18foafEUY/h//N5f5Vd+5+6rDjebsmkXCN1dtxgOArOr/QKkQ4w8w/BDXacYx8d+LkhxMY7D26YbAMeeAQmLB0cXZ+RxvWzmr2McyzW8PE5F4ZGZ7B1QQVTRVZt93K6xdYaUquSqTbZFkFA0Vj3x/XPpu6VmvNgEBimDadsJaSshqUNraBPQmlWpgUTiYy72MI3Rp+rjpnC0dy6vpkUC3H7rBWQSkMOVIGU1R1yB2MxKfgQVQqSg1M/0SOZlX9rsWFnSRs09m61zzyL4EEEML5tcB/VKLx5ZbKxswlgWxGrGKpNUS9MUMSmL47HQDjsHNx2yuzQTS8AIPvFww999zpP5zjeV3fv9S0no3Hu1LIeRMtCprba6Wba4A3xZiUfrrLS0+ysDm4QctNaYZ8GsV40sMARSEt2DZ0M1kHQiHRVhWq9tAnPWzZyjbky0yFY+fJWQVdfGvTIQMgRz2PvyEcXu8TrNGL60c/br09jHPOEc/+c3v4jNtEEmsOsYw7Wu/iX9Pb/xDl76rCfwwifffIbhh7BOM44rgRVYOQZT3yBmNwSOBcVkS6YTS2I+cJfrei++VsryqSg8oAhGqxy6vqTmiD7KP9685oa4okPRy44edNphza0q8KZOSIZXCuC0XoBWccmljKqwovRjjXZDV0CyglZbQ61OzY/+7Ov5T6+966qj1daLvEuRkgBwJ3ysSYcFNtVKShQKwO6VFaBSlbqurzGrGOk0qZCi4cRYK/BVS23CyRNIbjq6SWIMIozafANylJxLKZc/K4Y2Edg5g15abEI5j/Bxh9PVl2EO5DDrPK2mMrYShjyyjmUkufiVetYE2bYTi+X14uGe5SyoAtIwKlb7mCSmkjWPlHIHjKB2hvUJSbwyJT38JDTrOLVTPHGtiG5bw59C6jMfV9wf+XV6MfzAX87bTeOTnvuk2livcwxnJDc15Ruefjs/cMedALz1vh3vH6Nst88w/BDW6cWxohWkpvV3Yv2GwTEkZh0PZ+wd3VH5MdfxXnytdSoKDwHkOE53VS35KDtcGYGrMqKILWqloxagbRTOFThDo4TFsb6Hgk6ypi+XMUzNBhMQMm2VhMnqhieodqx1pFsBBeX//S/v5vfedi8AN52b+OIXPruqfXci5rpY6/tKONkNpEFWsI5aVaRIsZ5NhNank5aiiqJrlHMI4AsIJ/pxpARaaIIYqkbbTuRhtUMxwfuquz8GDxSByATUUHFkC3LQC1RLkac+YNpC7Es6J61upFzg2AxmiUA8YZ8PkGYJ7dCwg0blH2QdO1QbvJV3na6R1qwkJzGQUU82IZC1c0DWE5NGoMmJNlxWWZsIdXNFrnI3MFFSklhlXhUU9ZFfpxnDVxO9BFojVW4IDB8qfP4TbjkpPACsC9rPMPxQ1mnGsYqiljQTVBsuesPgOFfPk/pX4DuhTXZ978XXwNmpKDwSWDzJKpiqmpuT2Re6CiOFOcsVTzaCbAXdNvoFIacgVmlRaXQUzY4PmERWBnJURLPKiQwLUVTaynwuso21Rts0dGPrdKrDAzbtJ916yD/4hhfi6aQv+FIXXdfqvZjaxz8vCF1131qVtU5KMytzmahqn4QMWTec+kczxbYT4UUkyqy5NVZpg7bp5DlwMWiGTMEyg8REZSBQ7S8Dmp4cXzu3wecA99J8f8CT1djVDJRJGFKv8XX+50uSc2UA2HqsojBd6NCS4QF5rP/Oqs6toXGluq4nqHUOGEDUBlfa8uOx4xWwE0GT1UhHBFVjspKtDxeWcRzFDJFRlr+P1aiF04nhN7ztIpf2ZV1887mJ53zsLaB2A2GYk1TM46UroM4w/OGv04rj2oup7oZKuWPlDYbjdMIBhWXntHPtut6LT/2oJTOZpWxiRaQqSg0WE8IaByJsVWlajnWugk0TmFcnS63ipPE6OdLIXoxqjwoMUqF04KqAo9pLToRUu8uMUCOlZmipQsbCA/3XMpNl7Cv8RwSPes/jWZ71Tp8m0DqtHk6mIDTK/MbradODyGAsMzEcRau9GYJJteeyFXhiGOJJjAUh0G5In8hJSVc0IdPpIrg3WPZIW+dwTZCpMbLc9XS7IFslRtS8Lq/e4O6/vOfixSMObUOfGkMdXMjZYL8g+1FWyKZIBJvzbZ1trkBXSHE8oiyNA5AgJCq50Bopiu8cz4W6K62SFkkGzvC68yKDaep4DCzXah+vcKPWWGKgTRgO5LGVrxDagf1HALVXr9OK4b/4d/4977m37NFf9ClP4Qe/5UU3Hobz6nyTeT/j8zjD8ENYpxXHGcuawlrXbkmF1BsOx2ZldrbsBnIpmK7jvfhaktqHVXiIyC3APwWeTxVJXwO8AfhXwDOAtwJflpn3XPuNEm1O9kZfq0JPLdlWLzV4m5KmAwtF00hZUOlUit4o05goW9suzpDEhhwfJ6nGkCIZbeg1gpJe1rqt9q40mBlICFsaaVc1PADIHMBA23m2m0CtrTPHmpWpwI5BkwYYmcEyV15AqHPgWTNEVVrfEhZ4jKrYc4HcoOosweryW7PUXA1dPINOkDJIC6Q3LMBj0DFiGIxqW6oK+8ySa5kiF4zmE/MSxKy0zdWyvb/1lvfy16cNX3TuJrIF0SowaFmgu+AWLCnEApo7Wm7hsrDkgmG4z6Qkbdoy1OsBRQKkZF7zsjCG1yaVsACJoyrkqLksUYmIgsGSTBhzDiJGyfomY79zVBQC3Acj61kpE7o0PtxN+xHB8SnF8FWHqIa1iTHvbigML2Nc/Tl9QsbmDMM32F7MwUEVRCmlKrnBcDzc0WxE7FFpaBwgo1+3OBYepcID+D7g5zLzS0VkAg6Bbwd+PjO/W0S+Dfg24FuvDfYCY1eIHOyWYCzJuamzSZg2E922bDad3hORqipzVvrhRIsK8gUlPdkTZXyykntU1nZSCoJhKKpWJjUSaAp4ErKQbvQNoAd85v/wk7zzPRcfcJjKZrqAas3fkJJ2qRiSRkagrXMwCcOT8EEsubre1XGMWEgJmm5B18MKBYqZrJKErpX4cGLMuCcusFHFVFkAkQnVwOWIZf30ETPChKhVd0YScmDakRb4JhiH0I+SnIXbJPnpT30Of/bVbzr5jPPRjqN7L9G2DTs0fO9sQ0kJhAmVwBV0Osc+HTkaEMZ+qepYBPayp02BHk7YlnJBXLX9qNDT2BM0OkkQkZgoXRtjeG1yzThag5vI+rzSlL3PeJaUbV7KqRDaOrf1Gko/Fjg+pRjmATd/fWfHpn7DYHheZiz1qkvxA294J2+89yJ//Xm3n2H4BtmLTQRyvQare+iNhmN1ZQbQMiDbX94xxnzd7sXXSmt5yIWHiNwMvBD4aoDMnIFZRL4IePH6sh8HfokHAbsAUzOsQ7qzUWW7NaamtBZVuTVFGJClNdauMEFYVZJlK6MkguaChRAY3UfJizJRhBa9SDQjgSNs2sDK8iUEtcC9s5eZ/Tw4HlO95E89lb//dZ9V7mzNynoWgZDKVsgBqbg60wzWG5ICTWF1mROHYKklAAAgAElEQVRRkrLt9ahoYwkwF8jBoBHR8TEYUfIkRiCuNIoctGC4g4yKTR4hpcgahvuoM5CJh+PDsSak7tE2kduEeSE34BMQ5TL3wFX2vY4vSbu4J7STEqR6AbI3+lbwyVE2zPNMj0ZD66Ycjo9g3gkyJ8vkZHPmcEaurVI1fICOKLe9KIGcR5QJzwDvgXq1HUuaR0nkaKQmy7IwL4FKW9uKQoxWs9jHAMenFcM84OaPcIbvmHRzw2A4Q+hm/OgnP5NvfM0dXPLAE/ZLsL9/OcPwDbIXb0TJCELK1zU1bzgc2xBEozxKxlLx9HNet3tx5ofmKj2cjsczgfcAPyYiLwBeDfw14EmZ+e71NXcCT/rjvFlvxV52rwmSqQBZzGYVrK0zwFQylVxnekmgkRzjTaJIP5FreE+2kkKl1uyvUemIquVH7wHHbnisEqMQZHWSPV4XDjY840mPQxUyjBELkb6GB9VIprVG04nURDxqljd10JIiEY3M3Up60rrIWTPGdAhJFt8To/TQ1d/TNVRRiD2Mfc0imQfsS3rlu1Gabg+UBckoBrYlGYlJVa+yZDkLTp3YQs6BczU4UotwlVFRy5oLag3pwrRVZFK8xqRlmjMFTSbca6OiKRqdZU5kDsa+XP6WSEbUhpHNURfG4uS6QaUkBCXb09LKSwYjpQhfIoRHzReH4jO1uWRZIiNVNH0gYfYjiePTjmFBsLQbEMODp7QK+boC5NpEzzB8Y+zFqasCo3Ra1f14THGcjP3yiOJ4ULJdUUV7K2mrcx3vxR8aYw+n8GjApwDfkJmvEpHvo1p5JyszU+QDRZu1RORlwMsALnRBciC0MpxB6mKpYAjdBDVISRxnhFfFlYJErNIlPZFKFVACop1onk0VtJICsTz5ORWhvIJdBJPKSjg+gQ84XqwV6SPXllNmneDMWGVla52fghxrpE3BykbYHSSsWMyslrcBsUYRA0QseGixyY9Z1sPJxfEdjMt7fDfDbpB7x/dO7Fc3uSzST1KtWknBNJn3g0hfg5RAmiJdSVvjjq+6aKukK6I08yK0SeBA0QNbfQaqlQpxosknamMwg2PReViSS4E1fJWcZVXTZLX1CuNRx5egIYwMYgBUSFN9omKUiwg5KvCI1YEvM4ppTan4PlI4PsPw6cCwk8dd4AdeszWl8wzD18LwdYNjrJQqWt0K1RsXxw6Eaf1iX8/f9Yjja3BLH1bh8Q7gHZn5qvXP/5oC+10i8uTMfLeIPBm4+4/65sz8EeBHAG4/11JiKd1wE1yEyCxDlwy6tHKUMwhNBmM1MwksHuBK14AI0sfqOBcIFdcrTaAJblLtIpNVVrTOVFOBRpMyxxH9gHtUAC1gR47yqU+Bqa0/I4v41OpghKxqOqgbVwRkVOaLRMU357FhiyNRFy5W4tTJjTSS2Dm+m4nLsFzeE7uZ2A9yH+RSrOiMXFtl5VhXOnxBW7JEtTgz26rVzgo+anxQBHesLToIWodp27HzRm4g2wrwAMXqwSy1Np+scyBrJLaYMjeh+YR4ouqoB2gZ8zAoXX1URsKxb4BSTz4Ra08v84o8i5KHrZ+Apusvm/UhMVUeytPiQ8bxGYZPC4YLG1fheN1UzzB8Y+zFgq4S0ARN9AbGcUSpgGLE9b0XX2M95MIjM+8UkbeLyMdn5huAzwF+d/16KfDd67//3YO9l4iwPehIUyLWeR1Cz9IgmwooTG0iNHBd8FxYYkKmRKcJySSjJEY2TbXxDGFjhnRDmpAaBIZmJ9XWjVmJ4QgNkw4YaY4Ov/oRKko7jQohTno54rXesaZ1EYRid897IsoJLwel/9ai2sgK8rGMqiAjiRhkKjKSZCLmuQxbloB94EcL8+WZ2A1iXxV3zEvdzG4rYMqIZvbVGEfqycRNaAcHjD6Qy1S1H4kniBnxAQ9B7sESjnZo5zrThQ15UAmUmV4WyVqa8toUgv0yqNCmas+ZGZrBooFsOuHQm2KjYqrHsiYgsrAsQdMii41IugdLJNIaI4M2ElFlkWT2ZB5BTs5WDNUgtDT7gXIUg6YPYpn3KOH4DMOPLYY/0HQrSdzyDMM3yF685GXEO2hgJphtblgcR9QD4BjVfbhe9+JHq+MB8A3AT6ws6rcAf5nq+/ykiHwtcAfwZQ/2JkniTVGvdlEzxSLordOmIi6xSohEpVjV/ZDpAEw3leInhngrAtKqDW8bYYykZ82kJAIdMLMQOdFmIS4NWgSig5CLBIm16f9n782jLc+u+r7P3uf8fve+V9WThm5rQoAlQIxmEBY2jpnsgGRsDARsExbYYCcYUGyvJNgOSQxm4SQEA8FecSDGGLDNYEwQhuVAjIhAYhIYkBgkotm0RKvVU7169/5+5+y988c+71VXqylodbfqVemdpaFXVb/77v39vufc/dv7O+B1y6k/LJBGNI3VJ4ovBDPTNGVLiYAalDJ02pss/5plRSiaTOxJFaPRvNOziUi409dC9Mqy7ql9B5f39NXx1Yil0deOXz5GTLOyb4r0iliSlsRA0rGFuQhlK0wXJ8rhRB+SuL4TIhYUSbBOgW0ijW8efi8kN8G8KUyHNUmoUjONFyHCCdLPn5P5LTUtckWJcJZ1TYc+ATT/v05C05wlel9T+x2VWhUbLdXqOVM+vDBjFJYOi3YEqFLQKswbZ/aJHR1BqFLols5/t5T5lAz83sbxOYavJ4YV44oSRIBSSiowzjF8c5zF04RuV+bDDXVbblocI04P6GuqQnS6cc9iediefOR6XIVHRPwK8HGP8lef+lheRxDmRTElb5w4UkEnoQGbCQptkHQqm1qodRoVVcmnOhFCHCcIE2oJTDpWTuZjikhFZEKK4zuj7YxNLehGYSoEQluNUgprO+YqOVAT5HKhXFCmskGrUCelTCBTwYqyb9kss9aBgrdjihamaUZmcCYg7XwlBGuNvnRs79A6YXv6UcWP9iy7hb4YsRrWFooV1DoyzwQt55YxSF21YP2YzQWQ6QCfCrJRZM4nD0t7ucwTCAeFMlc8lJCr/QJkuNN5c9bjxsF2gt7RSTMWORxCUBQzB9FMPIwAxqDdlUaHusENWjcahkUgWthst+xjl6rBCIqCRRCSLcocMGZk8yyFNa7MHYWKBkwq1BA8hChCVMlsiEcfY19zPRE4Psfw9cNwY+Ev/PIbOOl5/OVn3MGfftot5xjOdVOcxVs/QFYhdgGbGTmcbjocIwsgeECpZYyebtyzmCex4/GErCBYPY1Iwsd8iollv3J4sEVNmS7ObLYzdVuoh8p0EFQOsVhgFbwUKkK1kkSzomg4G51RyZtoRJKWzPHeqNsNbAsxk9HPFKrMSHEOYjOc9XK5ddrlY4rPyIUD0MibF0DPtpi19XSWKGFUrTlr1Mi5ZTi9QTOhLR1fO746vQdtd5nloQV74Jj1oT2xjjTcELSR7OwpLX8jSkZXD799b4YqLPuJeRuUWaAUuglmjb5abhQECR9ZFhkVXeZHdDwi2d09wNZKOzam27c4C13TbEYc9n2HSs3PCkgJnJwHI8GsB6yaGz/ilP2VluyuuAlVU7sP2TYUFKmF4o5b5kGs7rjZICsV1mhQpjwsJAlcMjafVxkz0ff+Osfw9cXwIwWoEWl+dI7hx7bOKo6RYIop7dv7gl5uNx2OdVPpa0O0pSleZPfhRj2Lr9W6OxOFBwESJYlAeTfpq0NRrCvWITxJRtOsbDYbJIRuTpVKSAqwTmKcYxjeh6bBSi25ibBAzDIxUSaYAq9K0TLsdhWtQinb4aZ35S3KpMhhyTlf21Nvn/ESNPEk8UhQMKSkiY2HjUhozRAdkjxlFngDbx1vTl+N9fLK8sAx+weOiMsdP27YeiVPxZpRY7Q9axAhhJ2wkw11H4SuTm8drY3poFIvVkyDvqyUBtYdi8AHz8pHIuNVt0KDGIFGSyQJaR5PYJIEaMIDRcECpSSZKU4SJ3Nc4+rJziaNZXIG71DyIJiHsU6E4FEGaSmwEDqOS9oco+ma6CTZq7jmxoq0Ck6WdR48dN5dpfPeWucYPsfwjY5hOLM4DrKDIltFvaRU/CbEsZSKuKeU1S1Jozcojq/Vtzs7hYcnE11FIPKiS3V6MWg1W3FzQbogjZxeYjBPp9ItSmqMiaEpD0Gs5hOEOeKR4UdV0TIhOpL9ADkJHtJk68ojuA+oINuCIPgumcApm3K0ZEZAkRF6VK5QfFOeBPTBCO5CmCHuEJFz0L4Sa8PbkqzgSP02PVBzvK00K6hWpOYd9biSxGgn8q0lpWSlSsq71k7fFiJSW+6tj9y7ZIh3W3lkfLGUgs7T0Ncns9olXQZH7y67qUja/5KMcvcst4OAopngOwh/J2mF7glK9/FUFcLgXeMhGIZ5SrosBBNFJOOnJcYmE6V5J7QO+nRuMBm/R692c3jvrXMMn2P4RscwnFkcp9gnk5YVSSvymw3HOooFZMhhC6jesDi+Frv0bBQeBL2NijaRS3i2oqwYpoW2M/ocScQRo0yFoOOSkixHsKHJ9kGMiu5I0qUg8rW1BlQdfv6aBi8hQ6olwyDG8Ucyy1XQuSIi9L5mYJHJKD0LQRnRymkNLKFEGE5cMaWxTtjJRpDcWJphOkUErRkPnda+ybWIcKRkFauqyb/IRCS0FAil9yAsQYQOSaw5pRssBZk6IVmty8lmBHYt+Jl7Hjz9iB912yHPuniAThVRoSqUkk6B2VdUIFuagYJkgBBCvlcYyMt46Rh/llYzqZNvljr3bkEq6fIOBTF4kCfpicnLJuJ0E6VfQCEkspJnxE5HmgqdmAVdn3WO4euB4XAZs+grq9R6juH3eJ1NHNciiRctp+nDNx2OS/JEpMgotDJH5kbF8ZkftUTAfmkUlVHDKbXMiBRihUUb22nKVtjqlCkolfTQb3uk1qE1zvrP6czThr7vuDqbOfMpylQoVVO+RNrOnoAVS1JXBBhK6NVSRFFF65TRxAWiN9SzChxy86x0PUY7yrMKBvT0hur4fFPeGEaw0LTBN85sQUyGsUvzl+ZID+gTU69UJmxNsKik8Y6HImrQhVoK7pHzQYNiQVkbdVZiytTIUsHFMQqXmvCNv/qW08/4n3/A03neLYc4KUmTKZi3cwZFSeq+hYKEj8o6BsO6oKVcBcAiKahwSY0+J4D2JJxNUeksI4QqnxIk8gAi+phZDgMbh+4Z91xUqbUQ3WihaUoUjuajEnadIsXPMXx9MBxU7r58hSB953bils1EKfUcw+/BOqs4LpoW55QZ1Qncbyocq04UFnwqVGkoFR/S1BsVx9daZ6LwQKDMUEWzWnboOBsVSm1Yqdg00WfFNkEcgBzOTFMQpaTlbqIUMWHWfKqbb7kwyFFjvpdXhOoVqYlx0ZItLYGwfqWBNU1XzcfDgrY2LDphK2IVTl3pDHXBVbHoqDl9vFbRMuaLkRsmPAFSwH20t+aCHlaKXEDagkwQt2nOHpdGiUqph8iuY/cfQ1GkCaxBRMuCfSp4b6goauW0rRjhRAPF8SmrUD/IfIBVrn6y0jplFPRo1RVN3X26/KTkjEg2dgwyUa2VsMj23yAzhQRL7+lECGznDct+jyDMdWIJaA4lidhUP3kfQu/GLBOLCFEiY7Z7T7thzQAiXz3zIEIwF1yUERhJKRNwHQ7uM4rhzZxPfx7Qm3N0eU+tcdNg+O5l4a//zOtOb8NXffhzef7hNh0ZzzH82NcZxXGUAJnwFrTIDKKb6SzuZLfHPF1FyzxRjBv6LJaHf4E+Yp2NwgPJOVEtzJrf9yrCdiq4zNSNMB1A3WRryotSDydkDmopScgaMz6PJNKUeT51qJtkopRCrQm81EDnfK15mrGoKqWWTDesOmaTV5ab0fZreuADq3amcojUGSYhNC1mEWhilEmBggxpk0sSm+pUqTVYlk4US6CLYHWC2aFvqNZZW4duFA9UC30Vahib22bWWLCwbAOemPzMSQDrrePF0vSmO7GHTjrL6SLMEcjcmCQ4mKer7oJZEqO0ClICPRDKLZlOqIvhYdioekUkbY8dLNLtziVfw3onaozqWlj2uwSiFqrAPBUuHy/QNxlR7bB6p7mnDa8AZOUsUvFaWcUGmSqd+gSBKsO1MBAPJlVW+73be0/uOpsY/qlvfQl/6Wtezi/85jv5t696E7/55nfx77/hJcA5hs8x/GjrbOJ42Z+4bawnvYub6iyu88QD7IBMh3VzRPSGxvG11nVkMV1ZAcRo7XV3WjidYOnG5WUPpLX3amvO9JqxOzommmCmNIxQJ0pgGqwhLG2P+UopU/r6D4CHkzaxZKhQDWUqlXkq5JiuJ3/ag//nWz6Tj/3gpwPwYz//Vv7M3/kxisBUK1U2YD2JSSa4KeaSlX7kZjkBhMjoJJy0uTzfa5rZBO4N6UaVnIE2y7mlAForZZqQeUJloi2SeQgGMhzyaJ0Ip8zKdFDRGbQEWpSowzVPgi7Omv86vQVtuZo/X6og6hQNNpuJzcEMIhkHHR3xBBUEOCzrQm9ttPnA+piHkrlJLYJm2ZZLgVeAO+H5fiMaZiNIWsaMtZScH2rFQlnWRvSe7n9VaZIpDFIF5hjWAcMyWcDr9ZmPn1UMZzzWlScPgZsGw69687v44u/7+avuwzmGH986qzg2snMQp6MAu2lw3Fsmgk81i7JScgx1o+OYa/jRnJGOB6BZ4SUrVlEKUYRalapAaYROCVMHpEIZxLJQNHRMrjoiTjKIJpSUOuGkD38JNnWmTAX1lHzpydkcAp5VeCnBxa3mfI+0092tRtls6ftgUyVJPRGMPhoQeDTCBS2brOQVkKz+NfJ1QCiRM83QGN2zILQj0tCqoJkvoKMPWQBrgXQF0/x8o3WpmwmzlnPTi4VYJnQv2JIucl6gRUe0srozmfAbdz/IN/3cG6++BYMxLuR7Nh2dPc/6tA9QltHKm2RCMNxP8gVO3lNhEti3HUCCfMQw5Og3UAM3JVpPAhQwYpGIgLYzrAjNST8Gj7RWdliiU6zkC0bmSASZU3ANPtOTv84ghjtx9QEgctNg2Dvs+5Uv6f/5Y5/HB95ygIafY/jxrDOKY9F8bR1fjEq9KXA8+RVipgBSapJ0LW5sHF8DYmek8BgXeJB1hRgVHdS5EORMSQTMnW5Gi2Brho7IZoYkKuX7isSEas6bRMC9p2FLCC6eUi81Qmu2k07+6z506sKs87tJ26zHkELNybAuAnqF/TsM5BBrhI2Z3NgTYR1cB1vcKZLgCa3ILGO/rbAqmKNRUJKI1Lshy0L0BRk+/UTeZCMIKZS5IIcVvTjT9ivL0cq0qwhKLPu8FlMeEkdr520P7k4/15d90DN45sEWDZIgZCmn692hQy01iUykqVAwCF/RgEqRLFksHBtPAQXNlh0KzaAH4glptfG0gOZmgSHjcsyE8ELgyQoPTu+R5ktnGzCRQnr3xEDR9Tq1zzH83sawP+Ib+jmHE1stRI9zDL/H6+ziGJHxO8AhQ9tuAhxbWPqjdIPu6fxJRcc848bF8e+9zkjhMYg3kmE/Quq3FXIeNle0Jl05DPq60vYLvs2Ew+EJg4yKMUSyOtXUo0um7mS7zE8kQIJroOKDljQqaSmECCElWb0Pu4APHC1814//Jn/xE56LTHO+zulREZhnhRmD1JNhSZatPtLWVohxruTdU1FUHBXHVShas4j2/PwSTnjDzajmWN8n69vIltZQhOmklIMZDgtSwTdCrYIcCNGcWjsaEKWc/szD1yfeeRsXlNN45N4c9jbCiwypdXzKwIYBTV733NAxkgrFB6kpIrX7ndzpDRgSOxuactFsdSLjMefk4Yigj2p7uPOmMZHkdQnRQQZTelzRowd+ejBdj3VWMfzpL3oul3aNX3/TfTcNhn/rvkv8/O/c/4jrH5j1fHI8x/B7vM4qjuXEJIL8HdE73tcbGscomBltdbCGjjHXzYDja6H4zBQebk5UYLiuqQZVQMWo03ZUwCmp8rUR+wVfCr2SLbhRvYoMGxoVYti7hfWcdJ/omd0hwELQcHoECBRRCspg0uAYH/R+t/OmdzzE7953zL0P7vn73/Vq/uKLno1G0MLGHLHkjYkh3xomLflOHIlAtRKl0L2P2dyUMqjhKYd3NDQrXdpovTkRRkRHelapKVtTwhM4GpFOf9No821yE4sWqm6QQ+i7PWVbmLqy9uCeoz1ve2B/eu1fcPshpSg9LGd2KG11/KHGfFDRraZ5zYhcjoFCkYJGHioWiVSJ3KTmjhppMRwBPjampJYeRutQAhEopAOgheA4zRoiddz3rMJF4pT13SOldh4dixTGoUH5fUhNT+Y6qxj+ks98AXe/6zK//qb7uPfBPV/zz3+R5z99w6d82AemXv8GxPAv3P0AP/r6Kynv5xh+4tZZxXE61Gb3JXEmdOyGPovDg+hCXwMNo85pM9/txsdxVkOPvs5Q4VGJltUfRZhqXhy60y83SsyUySlzME+FGoK1ju8ELYoro2KtyVUQH9WxpfWuKCIlW13RaD2tea0HHYFoBA2KUMjWHSJ83X/xIrabyrf98GtP3+ssuflKKG4Qw9pWZcQQjxlms44K1FoRYC4bIhSnE+ZE+OgZVhyhFrCoKCsl4nQDCRDLSj/aI62MpwlNYxx3qDmbFHWgJBtaSZKXd3QrHGy3tB7s7t/zf73+7fzgb9wNQBXhH37CHybMkTJjzbHVwIVohdU71ZSQTgySlCjp/Efa5IYNQx3J9rf1dO7DoZBVus8+JHEwU3A3zIW5bGjmaXaoox3agBLJBq8ZmtRN8lqjuDWUwFWSeGZBBzZV0975Oq2ziuGgcLiZuLCtXN531u583v/0Cl7zv93FnRduzSeZGwzDi9vpdT/H8BO7zjKOU2uYnQDRwoYb9yyWo44tjnVBW9qkS4VqgcybGx7HPDwZ+xHrTBQe+YadXpyigkbBTCia4UJ9XdER7mMIqwfaF7YHFWiE1owcLoFKyzYdlWBCN2Rb7sSZLgphQdAQn7CFJA+NxB1rOcNaQ5m0YN3whxv6CMRWWXfGtK0UdaxcITdZXxGdRlswiDC85Y13yfagDAKZR+AtYM2Kv5nT3ZgoFFFMhs58CfzoMmoL0kltuAPiFE35Wj5BNMQBzY1epoJWaD0rc9z4B694HT//1vuufByB7WamE7gUXPuQnY2wIBeWox1luNAEpLyrOFGVWiqtdyIMSLdBacMdMEBmKDU3oEVmABTIHAKM1rKN55Bx0JKVeCmKFGfdL8loF8UkjX9ES0oOLStqRamkBv5aVfaTuc46hv/Wf/ahfMhzbuG//MafPn3PH/HSH+CV3/K5vOADbrthMBxufP0rXscvnGP4SVlnHce1pmlVxsFOhNsNexbXKrAC5lk0U9luyk2D42vxPB5X4SEifxP40oQTrwH+MvAM4HuBpwK/BHxhRKzXfiGoasylME+agEeggFlQasUkyO6fYLPCds524DRTpaYuqTlRBmEpyLyABiGaQT4ROAvFQbXmJvegzNm2c5OByD1rFOLAqLLhv/vCj+Ejn/cUXvrNP8t+NT7gi76HX//Wz+XCgwt1u0XmCZMOOKGO14m5bihSECkQztr3OfsKIfaNsBFyMEKGpG3p7rAau+PLiI1DoHXWowW5nMl/HmDWUwYFhAt1noDCwfaAo9LpPu5IGK4nktms6h9eg951MPF//icvYG0d3SjRDVs8542erb5a0rdfTRnlNdGMhcZm3rJGBxqO4OZZqLmnD8C2sKrj0qnThLqwX1cMoWihR7ZUjZwrO0GJoJcKtgJKnTbDjAaKBuawE4OWEc+qShVFBRY6Uzx2SD8hOD7jGAZ48R97Nj/5nBfzKX/jx07f9vGDD7EeXbhhMPwF3/Nz3P0wUvQ5hp9ADN8gOLZs7BNSkG2hLwvtBjyLe1j6dVgwKYTGTYXja633uPAQkWcBLwU+NCJ2IvL9wF8AXgx8U0R8r4j8E+BLgP/993ktpKQvvWtBhnudiiIbQWdoBdKVQDmYKjIpHWHSlZh0RAunpKmEIkTKuhbD8QyOqllpWw600DEHkyapnVZN+dQslChIyRndpIVJrlyqbsEnf/WP8i9e+if4wDtvTWnXpqLbStNg1qC3TtZ/WbG6G90N23Vs6ZQG3gJb01ym+BHFHfNO3Tg6zzlna0JbnPZgh12jMGWgj5P0HXWwFYnCurSshpURUywEFSnQdgtf8W/+A69/5xEAf+Spt/Bff8T7UbWwYsgCa8swI8ypRdGt4KUz6Ybiyro7xrohVig6sawdrSCyoVBQNXSybPF5YxkQa+YZGkQ+5SQjO9jMCfQyaNIRjjeoAVZnKk4XR12QSOmeubHRQszBNEGPyNf33PjrNbTjTyaOzzqGC8IkEx/07Lt4+bf+WT75K18GwBd84yv42s/7aF78sc854xgOPvefvIK3P7Q7LZ7PMfzEYvhGwXGJmnLUWBGp+BTIsbPsj2+Ys7hEoewa7DvRDSc5MTcTjiN+bxw/XhZTBQ5EpAKHwNuBTwH+9fj7fw581h/khXoM7XiWjjjG4kv+uYNER5PAC2Yj5KfjzbDeU6I1yENBsFrDI07ZzmaNdb9jvXQZ9oGsiq9pXGN9SeMYlYyCLhV0mMd0o/WVj/6gO/i6L33h6fu9+75j/tvvfjWvfN3b831cbvQHF/R4AHgV6DmvbUunL4rtAi4F8VCnX9phlxZityJ9oUhn2ijzrRvmg4tQJjxS5uR7R1aDfbCuK6t1FoyG4QbuAr6CGKYGSiY8ShKgojf+9o/8Gq9/5xHrCKDaqvL0eSaiox18cWLtuDvdg9aD7kKEZpDRNtCLhXpxohwonhRponfCG912tL7Qe8fdgIIs4MdO2xltt2JLOr/2boDST8pncSyM1QzDaN6TWB0wqVIlEHG0QJ10+PsO0x8d2v+SFvBX93Teuzg+6xjet4XunWc85fD0Pd/z4J5v/tHf4Lt+8rfPLIaPdit/4/t+ibsf3J2OjT/1rjPv5foAACAASURBVNv4q3/4rnMMP8EYvlFwvF9XwpwWe2pVZCtQ7cyfxfSGE6yXOrYDVkeaoc5Nh+PfD6zv0YqI3xGR/xV4K7ADfpxs5z0QESd9lv8IPOv3fzWBUNwCLzkZKiIZASwFLTVtZaum3MiN8DI2RUEtJV8xgJ71OKi1jEcmZ4bJiBZ8t9CLo1tgyrZVeCYNaikpE/MOSDKHUZ5624b/9OOfxdvfdcw//qFfB+AX3/AuvvOn3sDSgk99wTPxfcNa4OtKrR204pEzN/GKtWN0BRmAUFUGXRxU6BpEHYY23nFzfDFstw5vAiG6EZr66RBlkgIYLkZoztWS7JTXYd+M737VG/jZN7/r1Jjoo+64wCc/43YCITpYTwtcdU8yFtA80DXwCCqBzpEJjJtIsNURr26k7E5ItrWl7a4b+c8WEE5fHS06ZownXLNkgwP5Mz5MfTyDnE6SLce+zcRHTzJh15xDBjkTLiRR6rFW0k8cjs8+hj1sSCOFv/nZH8o/etlv0brzW3c/yPf/7JuZRPj8T3j/M4Xh333omO/9+Tfzs2+49/RKf9ozn85nPPN2nnfrwTmGn1AM32A4FoXe6ExQk3wKkV+yZ/AsBmgWEAWWjjSGRDVuShynfvfR1+MZtdwB/DngA4AHgB8APv0x/PxfA/4awMUpb7D7uLiiGU2smn79pVIYMjPJ2RIx2jl+olsGZ8iTJCVMZoaMMBvRBKpqwb0n2YY0rJHBiO7S0VrzNYAqhVN+TMDtFya+4rM+jN948/284lffjnnw47/6dsyE2w43fMxzb6P1lViCqAuM8UzqxVfEV3SqSaBiQiRAwKJgYy4XfcwPrWP7Rj9eiaURJ8Dw3NROEn9cAQ28FIyHAYXgoeOVV73hXr7jZ998et0/+NZDXvycp/HJz7gjJWRNiO55PUf7NCTd73rrydbOkWnaURdgUuo042FgQ++vgXaw1onmrO6ADllY0JOcjUzAJOntz4jTdkEiPRJP2eVkVoCT1yhOiHeeDoZC5H0NTg/HUnRU+O8dHN+IGI5wihRe+lkfym/9x4d4xa+9g8v7zq+97X4uv7zxjNu3fOKH3HndMfy6332Id1za8cZ3XuJf/sJbTq/5Rz/1Fj77uX+I59+6IehwjuH3ybM4vUYE7dCKZRFSFJny90KcqbMYD8KhdyF2ID25Hck81ZsSx9eiSD8ecumnAW+KiHcO8P4b4I8Dt4tIHZX2s4HfebQfjohvA74N4K6DEuo9gU0WxKIk4zk6ZgMgbtmKCsUnsCXndjH1TDuU4XwXHfUNLkGTlYKgIZx49tdb5nQ+JG+wOqgEbsCqTEUpOiHDQEzCk14dyqYI3/63PpHP+Nv/jje/4xLNgn//2rv53Us7vu2lf5ynX6jUJjTrFJNhwetEgc28IaaK96CUCTfDWse6o+G4Kyklq7S1s15esIcWZPHTCtTNcYUITVfh4rhUZLNJxzgL3nW85/K68rq7H+J/fNlrTq/50zcTX/r8Z/HRT7sFEejmWDOmqhmCF04phSl0sNuTVm6qmMFUlTIJZRZ0SoWBTqD1inJKACTwLlgEXU/aiNC70ZshE2x0PKV4yt1y/ynFhX3bo5MyTxt21ochTQzwZzUfIkxFseFq45KHkV5DwvVE4/hGxbBoYKJ861e+iL/yv/w0r379vRwvxhvuOeIrvvsX+eG/+0mULtx5S2WW9z6G73lw4dte8du8/PXvuOp633Uw81Uf9YE8pRQER0XPMfw4MXwz4BhPBw5xR8SJDemjsazX/SzOfBnLf+6K7wK7d4+sDW+WZqo3KY6vtR5P4fFW4EUicki29z4VeDXwcuBzSTb1FwE//Ad5MY+gmlF6smwT9iWrNu9QnI4ilkYzx3tjnhq9VpApTWU0kDIsXn2BMoErVhmWsIaqE6pIlGRW20hVxFFLL/2Ig2RGi1C3cyYLehJ1egGa8iNf88l89tf+FL/5tgeJgNe+5X5e/D/8OD/9jS9hW3qyuKWnBW+ZQJ1FFOuDQd2GTNc6KgGddL8zZ/WFtjba8Uq7tNIuL7RmtFWZVGgt23mlVIJCuCNV6HT2xzu+8Sdez4+/7p1X32gR/uELP5i7DqYkHEXQvTPPAM7FOw5QN9bLK7a0zAoIsN4pmwssrbOuMBWYZmE6hPlgonun1nQCDNckewFWjUaHkxClTrrnaR46D927g0NFZyVKwXRY7Uaw3W6HXnzMlD3rcS/grph2IrJyR2UEI/WUsT121tIThuMbDcOiwrf/V5/I3/1nv8QP/exbcA8eOm588lf/BAA/8fc+hfe/U5HyXsCwOV2CZitf/n2/xBvfdfmqazup8M/+5IdTDSASm+cYfsIxfKPiWKjjCTsgwHCkZPFwvc7ivt+jZRojI6EdGcf37pjWFWlx0+P4WuvxcDx+XkT+NfDLQAf+A1k1/yjwvSLydePP/ukf5PWUrK4nTRlXkLpptLDHmKPkRVOYa2U7KUwntrw922OjTRRacE/jF+pEXYVMISwwbzF3igTFC0Ud1WRRi6amuyNIrHSgHa1oqWidUBFYbGA7+MGv/hS+6jtezct+7m0APHB55SO/7Id47bd8BrUUPEAxCj1bhtHpLXXeQhCR3vnhQ3bmgAf7y87+vgW7f0/sO4XCfk1duOuGbj0Dj4rjfaGXmUqlh/Ml3/sr/H/3Xn1oC/DDn/YxzOR16D0TCauSA8pboG+FSadkrR83bOfMMXNpv2ddGgqsnpwRXYx5B/OmcHjrYOFrsFqwtHS0q7OwdKAGUjLmubsjIWykcrDdsltaRk5PQq8yDHCCJulbEwRlDBXNQVyYSx54e4lsv3qSoqIUVleCx9amfiJxfCNiOMT5B1/8sXz4c2/na/7lr1z1ef7U3/tJvv2v/1E+6SPufNIxfBSFl3zbKx71ut46Fb7vUz5suElyjuEnEcM3Mo4LCp4S1+wQZMIrItflLFY9xL2zrCt9Z+iRU3bHEBPyPnAWX2vJtSQv761150GJz/uALVOpbDaVzUGhboRd66zhXLx4kc1BuuIVYFOC7QXl4i2H1K0gRZliAoRQoZaK7VekCXOZsQimqUANTBuHmwMmnegsFMkIZi3ptCeAi1A9CDXUhSgCpVBlyiRBNdRm9rFjXTrf/9Nv4xtf9vrTz3Pb4QTAN/+Vj+NFL7iDEEdiGjcmmEJHVT688s2ILkQzVg+We45p9+7xhzp+7PS10yznantroJuch2pwz2L8nde+cTCn4Wjpp622D779Al//8c+HZtxS59TjD7c5iwYaHNwyIU+ZsKa0OEai0Bdn99BldDfRVuN46eg000erjciNOwvMm4nDp88YC8e90S2oZAbBZTNkdZb9LvlkPsKIHLwluI2AovRJaAXcnUbHPCV5iLH6SEb0TJUMDdbI14O0+Q0RGoE14V+88fiXIuLjzjH8B8NwBazB8erct1v5M1/7/55+rgubQi3Kn/iwO/mGL/6YJw3Df/s1b+SoX/2F+xkfcCd/9cPfDxbjYk76M8TwHMNP2rrRcZz0VaELOYLxhW2dUSZc/Uk9i6tWRGp2ILYz3ALlYIIu+OUOeyPW9j6D43/7poV3LfaoVI8z4VwqkNVxkJIjEwqVuQqbaUv3Tm8zE2k/i4EdB3pQiWNDq6bvPoKKYDIc37yiU1bDoTmXKipZDbsjZZCUyIrYw5BwVITQCWRIuobpTfeGlMgUQVEqMG8qn/nCZ3G4mfn7P5C26g8ep9Pp137/a7jlsPJxz7uDr/rzH45Iuu81a4zYoWFM48jqrG7YQ2D3dPoDRt8ZtjrWSR14QGXi/3jz23nD8R4EmsOl9u5PSJ941x184fOewaH7cA8E74anKTFVK7p19JZKG5uwoqmzn5z5woZoCczZBSQteRVyo7qweFbUdu8x02EltGBh9HB8PxjNAbUWqqSuu3UnepLRvEfOA93oDbqDiKAysXonJ6SSgVWkWWGI4VKgZyvWI5+sxMGl58z3OqwbGcMKSBUuSGUzC9/90j/KF/+jX8A8uLwYYLzitffw+d/w01QVvucr/xjNHx+GW3d+7J3388oHHqI5VxUd/83HPY+7DmaeMlUudIZyArREsvXPMfykrRsdxxaBGYTkF2NhZLdoPuFLZLLuE3UWt+50UdCCqYB2+nGmuZZaaPuOWFBWx9uaHJb3ERyfEI0fbZ2JwgOgSEEl54u9G7oGUTWrT0nTLpWODRlQiNJbR2vqzaUkkEUEM2OeFHfH1AavWBEK4iVtbaf81nYMSkUwIsbNNKGHMVXJiyeFFGQHXQTVgs3J4nWCp1yY+KQPfRrxuS8Agq//wd/CA950T5p13fvQwm7vgIwq1fjiFz2XZ962BQ+8B9aC5WiP3Rcs7zrGdmng4iYcNeM7335PVrcCv/DAEfe3R5cqff7738Ud08RzL2551mYaZB9BXXIzqyCToFuhXpywqYD3sdGBMJS0yfUKU6msfkxEMFWlW5wGAXV3mq+sRx1dLOVwU2Tb0ZOxHW4UGXwwSTCbBjGBW0eK0j2SzOV53aNkOJWRPnx4HoYqEDqIZTV5K82SfX9iUmS/D6npyVw3MoYzbEqoAh/1/rfy1Z/zfMyVf/XKt/GGd1zmoV3j1978ACrwNT/wGhhfEoJABO7J0jeCL//AZ2P3J4Z/6V1HvPK+SyQnUE4x7AGvO9rx1v1y1TX84g96Bh996wUulGzdtqWdyjnPMfzeWTcDjoW8nuC4JQE1GwRjvNIlXT3N8GYM3/Tf9yx2D1yGkmUoekQST+6GFsGnQosOxzFkp8Oe/H0Mx1xjmHImCo+UOQ+dMUrrQvZvspWDJokpIqGEgCvsW2dCiQJTKUgZ2nIVYsoZVScQyYuR+zkr21AhyDhhM0+Qe2CR/vstGmoV1E814yJgntUt1hDvpxZsT90UPveFz0QQXvumB/m/X3MPuzWf4u6+b8e/+pm3XPWZb9XCM2/bDoBB79Au7/GjoO8WwoBQIoSj3vl377w6AvzhS4E/+YynEMCn3fVUnjpPCQwLrMjYHPlUUaaCHAh6QWAj9HCKnNKyThCJVEUmUFe0pFudTgrmGGktbGR40NoDln6FZT0HifAYGvYxhyU15khACaLm3zl+KsczU6IGJnnfXbKdqJEzZcaXl2pGPduQsUUI+PgivA7rZsAwIYzoUD7nhc8korDfdV72y2/n9e9I3pAHfN+r3nrNa/F++wrLShi8+oFjfub+S9f89w+L8vFPvYiUyp961tPYGlizxLCcY/i9uW4mHLtkIq1HOqa6J6leQtCWX8QSWWzgQ9XRgvXIWO9v9AeN5WhNq3fX4cbpuCpSkvuSw+tR9LgjVdEpMck+uxTvqzi+1joThQeAmWERVFdUUqIVzWGbVeOEpoFJFShBt2BnwXjgolTJSk2FUkfqoGq2f3R8u4dTIm+cWwc1VOds8XkSqsLz50TASlbUopA1ruBm6YvSh8++jgscglQBKfz3f/aDeeeDC/deWrn3aOVdl989HuHbX/mWd/uzx7KeMldumzOqeBLlyz/0OXRP8xyzSEMclZRMARTn4LBQtwUOC74hK9owYkjbAExyRipViBmWoyXJXjhlAiHTJ1sXsI4j+HiEjXC8C9KUekhe7zK6PDJ+h6TBUNGAQcAND4xMSMQL3jqLGfM8UWrBxFN3Pi51H5WakwY4Kvl8k3kD5XFd18ezbiYMj0/EX3rhs5kRfvDVb09TqAh++97ja16Hb/r1t/2BrtcJhu/cTnzZ859J2W7SqbHl0+45hq/PuhlxnK+XXZwMZYNYs+sQIkDBOrTjTn/ngl8y2q7jayaunljAh+SYaJ6EVuEkHK1IKlTKlNgw530exyef49HWmSk8TqpYIfCwtIgViL5SykRbG6U4FvOIW/ac5yHIxJjVOeolq+LiKc0am8ElUPzklxHhWOtIVHDDLVARShG6WMZCdyWq46PSzhyDAWCNEdecnQVOquYI2tr45s/5CCTgO171Fr7z5/IJ8eG3Ydedx9pQPaxXZr+f/X5P48XPfgpRFfNK7ytugVjBPJnltVZKyTZkqYHMhdg6fYKMHUoZWl8XtDIqbBK4FoQWLi2NW+rMbrcyqzBvFUqhLcba0yo5tCS7u6UGH5y2czbbcblz96Xu2zoujmjBi+FR6OT+tQh6b2DBNFfW7pQyowKIo3FiYlNZolHJ+5rjq0xn3PeF67VuNgxPtSIBn/Nhz+TPf8gzaEvDuvPp3/HqHJmcfPAY/5P/YfcwH4oqMJ0edFdfr3MMnz0Mw82LYwnAgrZ0xAOx4WbqTm9B3xn9aEGOnGiw7gIsuwOUzI6pswLKtA2macO+G2qRvIlzHF+F44j974mxM1F4COB0KIVSlFISJB5BWKOW1GqHV9bVWWxhqsFt2w09OqU5Klmpt1rYTJthhFJHa0vy4kqwykpYxRZjUiGWtIKbVak6Yaa4LtQirH7MHDpkfAWJgh2vdBp1P7P2Y2wxJJQilb4a0TulgrlTo/IFf+hpfP5Lbgdv1GlCtIAbX/ry1/Mb91/7yfHhSwV+5CUfmfHMmpIv98CaYjvQ2ODrnjV2VBl6ddEhEyv0rTFtJ2KqyezGMBEUz/Cnk+eI4T7oxYlJ2Gy2HETQJlh6oy6FMhe2d8Aiij605ej4mB6BR88K2ib6USNW4fCgsk4rRRQRHR4iKUwPj2RNi2CSbdkyOcuuoUwclomj3vIpR4KFlgQzVohCSEruzFMiJqVTyvV5WryZMexNMmhrYPgn/twfATd6d3oz2r7Tjjux77QQPv81b6GNtu5LnnYLX/Tsp3Jw8YD54ozMQj/H8JnE8Psajk/OYutp3tUfgeNwhQ49Ws4YtTDXA+aL5RTHk06Ucxw/Ko6v0fA4G4VHFpSKhdDT9J1JBA1lki0bnSmWrb2IAANlxi3ZzlYqXYO5FuY6rF57JvFVSaMUkZEcYAqeJjXh2ZaqBMhK0z06VSQK0WfYBetiyK6hLWODPRoqyqoGBcKSta3amaqwuCFTyQ9FRw+ESQRnSlJVyVyAf/ynP4TL9+3ZP3jMerwObbTi4szbDXUu1DnndDJVdAKZFetBLRPdoC2N5fJKLI5ETVdA2eZTStsTKhwezkiFwzsO8dnwWJN5LFDMwStRnEjLnXyyiVHpR0MuBA/d06hzpQS4N8I6hxcnpjpzH3uKKW11CnN+6tVoOJePkj2+LVOStiCfHkLZ9T2OpCyuQJPOXjoNYb514qEHFuZZqKSWvwOuFa2KznnPgk7RCSXo0WkuRInfG2fnGH5CMawPFux+Y9k11uMFi8TwP/2QZ5xieJ6VeZvmRFSDeT7H8FnF8DmOr8KxS2O6sOFg2lBmoWzOcfyYcHwNmJ2NwgMwA69CLemMRjd0U2i9s4kKk1ArlBKAoBXW0jioSXZaT6xbRVEZANbOysRGksEsYwO1/UKE0SJQ3SBTJYrQxMEKs03sHjzGd6DeKeScTqqhG5BaiE2wmWY8JtwiSVliTCtICaCMHIMgwjA3JLapjb600i5DX9J//2DeEhGUOiVhSUF0XIuqY15Wubxr2OL0sJRj9aC44p0xf++0YYijJ+3tAtPFZKnbeNpQhRAfk1LIgKGcRwYpmYpJmDyff47lMso2OeJjllukEDXYXJzYHa1MWrDV8eZ4OCHKinNp1+gOZStIJe2GzdIAqGjOCMNxFBWh1qy651smfB/ZEi35d9aMtcc4wMBkAgohDqUwobTHmHPxRK73OQxfgv1xwwzKtEEHhuerMJxfNq0HVgr9HMNnGsNwjmN9xFmMcJq+eo7jPziO4d25jSfrzBQeVKXWbDel9E4TnAjWjWlbMEbHS4DeEZ+wFkyHZNKhGSbkvE8j7V2rJfvZoYRScWpAmdJiVmdhbWuSlEKwfSd2Ttu19ABUSzvZwwKbCd0KDDc8U0l7Wkn5U+C4O4UTy3Qha/tsbcXSsSVYjpz1UqfvDYaSK1zoy4LWiWx/QDOnLVn5snfMHPqYOZ7sCEv3PiLrZPMklhatlElgo5TtRNMkFUUkWxxJbnOEMTRwpyz1HNkHwmCRb0sSviRnp9aC9ajRSuAa1O1E27dkRJNe/xWlSbbd1sWZSt7LEHDNhMkrVABBg7zXw+a3VqVtnJT5Z0z3NCfZqbV8ny6OlvGZPEYi5PXzQDjH8DmGb3gMwzmOz3H8hOD43UhdD4fYkwLc92CVKuNijjacRkqrRLAe2faTTL3TOli/a8dKxXsm4oVlcA9zGQSmvIhhjgwL10ByI4hSSsEC+t6JfSb6RQt0CUQcnUvqoTcFr0ovUIoM0DhulsxqH6xpgSAGO9hhWPhGC2If2FHPkKC9Y8cNX8fGzp5asrhDKMU5yVt2Ga+7eG4oT/ma1oqI5k6RwEOSEU26x4kyZFhlxD07RMrVTnxdwlIGlWu0TkmfhbxWli6CB5V2qaUroKTBT981+gwtwGtBimXQUGTFHmYYJRnhCL53piiUTR5EbhAo6pnp4BKnFT8OVYU+pYrCmhM+7iek8U0EUfJduowN7INBfp3WOYbPMXyjYxjOcXyO4ycGx9egeJydwkNlSJaGOYng9A5owbuzrkEJ0Jkss4sSayc2lb631E8PuZ9jKNnakkdo430Qd2xt6anfHL8cxM6yXVbT1EUmRbcKU7bEAqH3jq8FVQGxYSCTpirEqJYRrNc0Ujlxhts7dtmwo5XYp/zMl57Ry57yIxkVo2hqvCXiNC/AHU64zx5k9LFkNRsnErWiiGdC5DD4AxVESrbkakq13Acj3Dk9TORhmusYrjgigSmoCnqgrJeNGNkGjtC60SMZ0EHJaxaarTpzzB0TTba7C2UJPJyNFKz4uKZ5/eQk3VECsbEvT6RfhREL7cQaFM1rS3BlDio5T9QIarkW3J/cdY7hcwzf6BiGmw3H5RzH1wnH11pnovAQyQ+knnrogoArNmadPiRLqgZNWMPQWtjME2HOSiO8pDMcSlmNuVRKDdQrIQVzp5M2vGKa/IqdYvuGDi2zbEE3gl6YaG64jOpdsuouItjaYdIxO+zDAGyYrVjQlkAuk2zoo4VYAjFB3GjrQuwLS9+lHXEkKqOkPr0ebJi2hSiOR8dXx3bgLT+7AU7GgZ929VCmAkxCCwHLsCORgqD05mhptJo+Mqjm00w4YoaJU0Spm5kIo7tBGJRAi4IE02HBbz1gfWBPrEZI6rT7GoQqJRZqAWanuRGtM+lEi6zYm/v/394bherWbvddvzHGM+f7rrX3d06Spg1pEozCuYlBREr1QrBSkKRU4oWU5sa0Bg5CxUtN6UWvCoIgKGghYEgKmrQ30lwINgYkNwatorYV2ya1YkKaY5uc8+291vvO+TxjDC/Gs/b5PPm+/Z2cs/Zee3/fHLBYa831rvedz5z/ORlzjP/4/xmZ9Ktz2YN2Sm6en8nsJZoTQkwMjFGuki/6TvRAm2FNcQn2nmx90FRYVbjOiyoFRBMxp8UTTbUcGD4w/J5jGA4cHzh+PBy/FxWPsy2EZfUMMxnu9ARGciKQHnQpkZJFpnQviWaNX6mWp4BmqxnkDJoag4FLJxF0KLo3cofmyv7ynmUx7NmCroK0IK3c+EjQKG06nUpzaVLKfR3SFLaydY6ejJc7+QK4bqRXVr+MEvPKB2W98KraRRCiYAqT6b08O2PnBX2ebDiKorQCu2woQlOlu5ccbQhqJVjDGeQLxvVuoFdDKDGYvXeWk2KnZ+zuyDzbEtRYmkzGd8K+dzSi2OpWWny6LGzeayxqNfqi+HAsivk+9k47L+xjx3GWRXlmC2Ry/zVnvCoZVj/YU3CU2B27dtozQU8rmQVyD0FspXtnaYa0Bgj7lPptKMOUvMI1dmxdqkyYYAinFH6/zp6PGQeGDwy/7xiGA8cHjh8Hx0XM+fh4ZxKPrY+p+1J5koaAVS9sD+HkCcPZEVyUpQmSUYze3XAGKaNKW4vQryW60haQrbG/dOK+c4pkedaQG7Bnz0pNtoHr13taMgSkZIFRStJXtUpIASEL46sXuAh928k9sF2wEaCCe5Xn0ETONeqFnNDthF6SVZXr3kGkxrlMGHlhrcYa63rD9uJSjG+PmutOIGxmkYlZ0m6E9oHC8zO7K2uDno7HIKXEYtYEfON00iKujcrOQ5PUQGnIChIDTSNt9l7D8MhyefQoj4G1niTojnmgCtvlHlsXCKu5eYLbU2Nfr6x95a7v6AQtUeXD7k7cDW45Iyev0iClYrhNX4NSSFzqM0WrRNidUy74cD7coY1Ok6X2M4J9PhU8VRwYPjD8vmMYDhwfOH4cHL+u2/KpiYeI/AzwJ4GvZOYPz23fBfxV4AeBfwj8qcz8XSmN1P8E+BPAPfBnMvN/+dTPAL64Ktfhs08ls3eVICWbe/XOitJQVKt/SIKfHFsaSS00wum7svdkuTjrcG5l5TSCILCTwK0gN0Ws6VH9P3Fq5AmtWXAXXAxLR7NBF8a+o5swLvfI2MAVncAulbxBu1nQs7OcV/wE1lYyYL860ZP764XrtldmKIKRpXXwhWfojZGLcvndjfHhgOvspS5GH/UZokF7tmLP23x9jTNJSFk2Z/VnTYWGVd/RFjIHsQ/sQeFPQBbDtwFRaoOulcEnlRXLJUl2tkvSd6U7OImkk3ugBOtyYu+9LJW1ynmOs9yu7C+Tky3s0RlZo2GSQu/lbLmNjfNzw05TlMiEplKEJZRr7LRdyWaYKudWRDJ9JtwsK+kwth0JOFlDmrD3jy/wvWkcHxg+MPy+Y/jA8YHjx8Rx5sfjGPim5rZ+FviRb9j2U8AvZ+aXgF+evwP8KPCl+fVl4C9/E+8PQM/qJ4lIuSKGsy5GsxqTGiNwL938MZLoSUNgCOGKD2H0xLcgLkHeBf7iiuzKyB27Dc7fYSxfbOhJwAbkZEGLgAlhSVqUQIwZ6cF4Cfs/2dn/yUZ+LYgPB1wd78FwZ/fBFjtuO/bdC/oHGvZdC+NWyEVJbXg0+p2z308SU8os64Eukq9Q4AAAIABJREFUJUoTLRgS7Bv0F1fy2pGsnpnnFN1pjty0Urc7GTSrft1w9utO+kzTk1ckK7VipKeDWkPEig0tZXRcY1NF2hJTUqUIXx5cR9JHsne4jo09Njx2Ih0X5+pJ4lNsSEq1DgUTllU5P18Iq1IlWYSmQRGltgxGGtsV9vvqUT6cZ5n5cDyMqQVVtXPIEPZM2mLoAna7wqkxmKSnT+4svnEcHxg+MPy+Y/jA8YHjx8Lx6+JTKx6Z+Ssi8oPfsPnHgD82f/454L8H/oO5/a9kNZN+VUS+Q0S+NzN/61M/B0qWN2uGeGk6GdWJWbFuS90TJJIk6GNAtlJ/S0c8S9fek5ZChCM3J7gx5KzIClggS7m+BhN0piRZrGVP2AL2ZL9u6FY2xiUFI8go+2YVI9fArFFWg4GfyyI4p6210hh7sL10trvBftnLhMfLsEdVSp30xhhan9+/tjEunZaAlHQtMyOXluhZyVUJrWcRKGOgy919zdJHjTcJApKICg9UKLLWmRPoeI1PJZXlKoIDnsEegQ+qvzvmWFZm7TfKyMBDsYwqqUWNWkkGakY2MBJbK7eVPs+NlLukE2w+We2ZWGY93Sw22dH1ntmEyJIkzizW9CBoSDHOJWfZsAhRDyS4p8DxgeEDw+87hg8cHzh+LBw/cEo+Lr5Vjsf3fATA/wj4nvnz9wEftab8jbnt9WBPqOyw5ppVBVGd4BeaKD2j/B8m4zqAfRulYqeB+nhVvskM7KZGrXJ1OJ2IxhyPqhEmUhGdc81RJz56EHuilyS2gG0gURPLKVLE57Vm25s1WIvpmwqupdbnUWVHIdlHsL9wtg87+31ndAevEpVQPgi2CrLWk0VsznhR5SqZY0k+X9sWwc5W2fWqpELNeBXk96vTxL4+FmZUPcsmwBQiY7o7lhMiSNkYk9OPgZrZz2R4gX3bnOEUgWrus2oWupyygiYRkvSSrTEUbdAanM8LuwQjslwvH0R1pPa7zxuMZmIkiwYhSTYjo2bMQ6IuUkCzhI14uJiJst+2Ocr1++PlPR6ODwwfGH7fMXzg+MDxI+L4kwt3j0AuzcwU+f2zoUTky1QJkC8sRWZpWmNkKVnCLNJwHzDLjz0T3NFZ9mmuII4zkHSWRWgnY10aN19YUFVSBx4NvE6spmK9nPVMnHRhvPQatRrg16C5oCNY02COcIUGuYA8V2grIg2RJGLK0uYU/olAWdm6c/3qzv5hZ9x3xu48GPM0E5qBNUiDkcnYk7g4OgJdGnixyZ3AmsIq2BdWUgVbpupcJJ7OcCFzoXsRwBSK/SyT1SQ1cpbhdREkKFEZrZfFY0SBxz2JkdCDPoK+O5FZxKcJppRiUvsSJWUsUkI5oqQHHVhUWFbj5qRA2VaHBz6CSDBR1JSeAw/qQuqzj9xAMAJh9EAWECvzJPXgJKUB4NTsO1rHQ6L25W3h+MDwgeH3HcMHjg8cvykcvy7z+FYTj99+KNuJyPcCX5nbfxP4gY+87vvntt8TmfnTwE8D/OFnluuS2LKiYrgPrvvGnlkzxQKClDFOT+72AZl8sJy4aRu5VhlITobdNNp5ZbQrJieExgivkxrA5ty0he2S6KWjW9KvG82Mpa3YHuzpNBq73GPrCbs17LZha8MTwoTrNZAcNFVaKhlO6EDVuPvtnctdoHdXxmVn69UrG75x09ZKfm8Muz3hJlwvO/G1we04oSfIFK4+cAZmcPsM5AsLosEQIbxIQ07J146rM3qnsTBir5tGlsqgj4UzC1e5YqYMLzU9awYSDHFOaYxq0RJ7uTRu187W68IglV12NMExRJWcInweC/nigukKJvRIGIlrAxnF0NYdW5U1ldiTzcuAaoiTWqXCqitCerKehGXr2I2RJizydaDuUKBPwaSxS5BSWf+eQWunt4bjA8MHht93DB84PnD8pnD8uvz5W008fhH4CeA/nN//+ke2/7si8gvAvwh87ZvpKUKNbske9BilNCet+ocdxJTog3xQvdNZuotgl8a5afXHlCLqqBMa6Bn8KtgQ+qWTW6JdeJkvq28XCSLcrrckSR+jyl064NnK+fRFOC/UOJdXUSmgeZWa0pURMck61Tf78MNBfhX260ZsiY+az04cM6OdFWsNO1uNW10SrsnalX3ccXNzi3siqizLynrT0OcrcVZ6OiYL4KAlT3x/1+kvd9SDlMAejlsOogf+IrjGzvIHz2y+IVB9vVQYNXPfSS7XDVUjQor1HLBEyf8asGDQYFkbZo1NR0kUK9zfGzpqVE1MYY55YTWituqJZkprCbqzf+hT0rgy5yaCI4xI9h18wHIyuAZjVTICl6y5/ZzVYCrjX0Rppqwq3JrzKZymN4rjA8MHht93DB84PnD8WDgW+TZ0PETk5yny0neLyG8Af3GC/K+JyE8C/zfwp+bL/xtqfOvXqBGuP/vNAD0TNG64Xju7JykBWdbA/dI5nVZaCrsXs9jECBfGKuz7hlqQiyJmNC9WrmjjcgG9T653F6wni0uRn7Sx9Y3bZ+cSv4kNVkGeG+tJsOffyXW7ILnUDLiAqKIINCWGl3WyZI0UiUE27r5yh15he7ETEbj7bJkWw9lMsRVuvnBiaCBDyT0Y24ak8/yDW+4zGeMKAu12hS+uDEsmR4rIgTYj0hiexV6OOlbuG2onkCod6irIrRDqbL8zaB9o9ey8EPNgbe2qReoa/oq4JKp0tpL0zVFMa7Qyd3dSGh6DMQZ2K/hl4HuxxNWU7sF+HdzcLpgHIQNryfmk9GdK3m2MNMIBhaBY1iB4Ki/6oKWwtCkrLIJm4insPYk2ZXkjypBIFWnG+gmV5jeN4wPDB4bfdwwfOD5w/Jg41m+n4pGZP/4Jf/rjH/PaBP7cp73n7/0/uN5veOY09AGzFRVozxY0hUVBxyiV/EUIHGuN9dzQpiQ658CdEMX2IOKC3ic67XvVBKa87rIaIzrLIugXl5olXwRtypBA2zrZ1XUi3Kd/gBgNJ9PqJDVhvwTjxRW/G1y3USScGPiYpTQtd4LIzumLX8Qt6HswXmxwcVoqui50EfZtAwan2xU9txqJApokkaX6t42V7sF2rbGwlla9wwwUp1nDqiVKiqDnxt4htr3snoViRasyfNC3ThPDCa7bRoySAs4MDCNEWDQJBh5CpJEaSGtYjHmh1Rchs3/qLLOcpxaYFnM9W7IsznJK9qtzyWKqJ7MvaeDTE6H36h/qWWmroFImRD2jpIspRr2aoSK1lk+o771pHB8YPjD8vmP4wPGB48fEsb+GbfRuKJem1NyzCCpFTkGVVbNOmmnNi4thAmqKIzCZ1n04BuTQch5U56xC7zun1GJbW5WQQo12DpbTiWgdOVkBfdUaCVJBBmgr+d0ARGY5UQYRSR+BNsU38GvS7wb9rhwOR4+ZEddYlxksTbHF0FPiDS7XTrwM5DpYRDndLrgIuycewen5it0uyJqEjCL3iJLSGJ5FgNqScQl8FxAHrHwGTItcpHVxt0XRxWo8axcyi9QUKXRPdg8Yji1FEVpUyKaQwmlZMJQQYASR5YY4xYuLUiRKEMhiiAu+1w2rLUbfE78O2gppgphwMsVazemHOn2H8HI0jMg6/1pjZZE1tw6Ou7A2LfJS0chBa+ZdtS6EAVy2/cDwgeEDw99qHDg+cPxIOI43ME77qCGUEyFZgzqiNResCtqCpRU5xrUOMgDhJMLuikSySn0PhyHOaDWWREukJblURiyrIOdGroqcDVkb8aD7L/r12WsVRni5/BGQUy6X6l1mZ0r/On4ZjKuXWVDMk59gJiwnOJ2NdlqJ8yjhnS3g6jSH9SzoSdi2IDwRC/R8grVRRO7qpdazRyvb4ZH4Xop1BKTMsmdb0BaIFNFJrC7goMqf/aKvlP18koDCnSYBaPkRiYIlnsGCTIa9lldD1Ll6mH/XaWkNRSTSpbLrsTkWAgQRQe9QIj0lSdy0npTsLJwFtj3KajsFREt5b2b5uz+MlCW9JRiklkfEanUhihbhrThRv29S/6PEgeEDw+87huHA8YHjx8Px6+IdSTyg2ewRSdZsspVLHposmZgFlkEEjFA8HQtjENiQV0IxTEgOcc5trUz1JMiq2An0VGSpLl5qeyI4iaJk1By5JMVW9lFlr3DSnSYC0ghN+odJfzFgc7JHvT4CiZolTw9Oq7KcheVWaWflqhB3Qdurr7e2OcaE4yPKsfDGYLXq9cnsZVKAlYBMZWzO2IN4sEVEQIK1CbLk1w/qHD0LF/YWbAnaFfcg1BGSGwnUknAhXF6Nf4lmzfALEIY00ACoLDq2gYmV1bUWA1oN2gLDk+1+xzRRE/bI4mCRaEShThIW4fQwIkeSo1jz3ZPWjJQoJn1q2XG7I5a0k9BsLedM6iKJ+T7L0pjWUm81DgwfGH7fMfxwyA8cHzh+DBy/iamWx42HPpeBmM3yDuSoLLcvUSWdrPGmTDA9Iz059aQ1hRiVnRmogIXhWj4Auq6wCCxFTPLFCS1w486pNVQhxUEUC+H+4rQaNi9NfS2jHkmjvwzufmdD7h0JgVAyIMKBgU0ArOeG3ShxhliCtjfu7jZsC9aWlXmncH0xUE3aasQXVnTNmg2fVsMi4NbQkezDGdfAdwqgVNa8Lo22BLIaRF38VfQ7sXmyRdAFwjvpJd6zaInmpMB22RAzpJV6oLKWbbUoYTC2YAyYU940BungUrmtLfWUI6loMzZ2VheWdSU98B5YgC7CIspqdUMbVqVS0STug+vwusBrDJ4QpR4IDBVlySJNuQsXnU9iUiBaxFllAS4Hhg8MHxg+cHzg+Alx/JCofVy8E4lHAmIwIsG9FjPV2JoaF080jYhiMWcfoA1R4Xy7cr1uSJbmvcegLYqdFJ4rQ0Eo8510SBWaLxCBnQWsVelsqtxZ1gTSTTNQRxYlQvBeTn77S2f76hXtVvr9UTbCIwLRArkitPOC3Tb0RoiFWsPdgJGYBPbslh7BdrkyxkBWZf3CLZyCEYPMMufJLNCrNUaC98S7I1GOhIngo9NWqYtFZCrpGZHCh9cr1zEQbfRtg6i+o4oi1nDvBSZzVBqZMHzUe8hUpotAzPB9r7EtmWzp8AKaVbbrMqfiQllPVmXM7pg2Ri/VPRFlZ4fVGH3g7jRVdAFOYCmMSQgjy7gJKWLWSGEkLBu4BRrBqZWEMeKkCfapRb4DwweGDwwfOD5w/KZx/BqKx7uReIgKF8vKdqkSmw+nu7CRNFVi9OofhiBoZZsIl83Z01lUaavSlsr87sXxTbmRVqUuLUa2ezBwlqWx70Fcds7nhTbHf8rIJxl7YEvSKBLRvidxB/Fh4C8Tdsd7EZzm5BEk3NwqegO6grcSV1FXsidxDRrJervSt1LqMxr6TOCLJ+JGZm1tgQSRpC3Q1hvuN2dcgn4ddJ+fF1VqO50b68kI60TewAigxsz2kWXms9+hWS6CZtAyiL7DzXySsIVlWl/Pahw+ks2TJiuXfmVIgGTNpmuNVm2RNCntAd+lPAUy8VVxM+4vzhr7LNsF1zvHbmEBFq2RMhHl+Y2yrvBV6eie9F1wgX3OeKkIDcGZhkZbyfP6AjeLsLQ6t/Ga8t6B4QPDB4ZfHweODxw/Fo5fk3e8G4lHJddSs8PpBbaouWa2smI2ETKSFAU1Yr8ieubUrpyb1UGzQKx6TIrQ3eEanHIBimwkkgjOcg7SGtmUCCCrL5YZmBh9Z4q3JNslub4c+Mud/cVAekdyrX5aBinK2oznJy21u5tkvTVSDBHDR3K931h6cn4m+Arb1wrs62qcP1gZZ2UnWDHSEsSLrZxa1sSZ7N3Z+yz5hWIIqmAanJ7dMMTZLx3NMiaKlElKAtWFDCNJNBwlWNdWRkPWeGimxpQnVEqIZ5k9XG1KlEghksLYigQXCE4rktOoLdqERQy/D5BgH1KiNYuwuRNbHdc4C55RngHTG+LZKSkqusIYaJTro+egi9a5ccenJG/fSljnRkqOOJ7oafHA8IHh9x3DB44PHD8mjjO3T8TZO5F4kDCGF0U2IL2sbshyyTs1o8c00kkQd9oi6JpYW2ki7OlzpEdBwFYvIpSuJE7vgQ/FVLhpSaaxxcBY2PogQlnDaE0rc14CVOmXQX/hjJeduAzSk55ZDozU04kJrEtiNw43C2MZ7JI0KZfAfhfEnUMMYr1hu/YqBwJuyVggzQiC7lcWPZULoAmqRo5k3/aazx4AVlr/UHbVK3QrNb9BjbOVh4KWGZE0BjsrNf7WTFlWZT0bYTr1/5OUYmyPdMYYMAyJZDfHe70f07shqGNtA7qXkuGA6nMGiBo9dkSEqwz2wZRCNsY2s3QP1rYQCGSNkMkimIBJom2Fq8OAnSKexXDQMjcSKTfFy6js2qwy7yeJA8MHht93DMOB4wPHj4ZjXlPzeCcSj8wizCSgWSNLKsxSlxHUTLVJZZViRbpBfY4qCSdp5DxhqUqmT2dFysBHsspYqUQo2+7swCkNBVyCWJPTupBknVdP9rvB9mLQ7wc5e2PulBGSTAU8FWwBu1W8gZrh9XHE7uQWaMCyCp6Nse84QTspcqNkqyeNZo30B5nZ6cRIjYT1bZTkb1J6+EJlxg1kNUJrrKpuWUIxoqjRphwsqpjWRaoKqjZn8Kuelwoj6mZSM+KTVY6wDS/TnxREjIePAKZNMvP/qJn+qAxcWrkgSQix19NBkzonnoI6ZZg0ZY6DYkObGcua7CNpy2RZe5lXjcz5dCGvYO2Z7O60jFfmVm87DgwfGH7fMQwHjg8cPx6O33mORwKxA1IH/SHhr4S5XPPM5nhRS3SZxJVIEidUWExrJpoa4+lDGIvQHkpFma8SsB7lfJhapJwpPV8KbGEMHF2VvATbndPvBn716UBYbOEkEFXa0mhrojeQ50ZEIhhElvvfVqIwi4EtK/tW4MgWcLPAuRXQ5qw2utTTAkpQDO3hUWWsKQWXOUk+TV8xxDOS4WUdbalTKS8xTSBYrRESRQjLOhwRzH5oWUH3XmcjqRtGWBG2oidFnYK6fKp86OHsGQTTSvrhfROuw0k1kMHSWpXkRpLdsVb+AzE/L+an1uqUlgmqDN2RVUAF7UkfdRGSJWxDztE7khEP2f1bAOzHxIHhA8PvO4ZrJQeODxw/Do7f+cSjUjSDVmDyCT6NwWk5owbnZWUPn4Iw0743F4xRAIzxKvNTB0ulD7AlMW08pIJDhfDEAk4nY9s6BKgq4aAyuMTGTf+A64edvAPfg9Gnen2UhHCIIy1YbuD03NDbZKeye7xAsr908t6xCGxtjFzY7+7IprQbg5Pi83OHBvhgXU7zkFSfzruzbTt9m6XPLNU6ndyntkDKYPRSDVxHAxS1RJqDBufzgorSARdBukBqjcNJ1HjaqNGumCXLEIim+LZjJJsmFqXRH1I3lZHJFUdzYYTzwOxyD7oHvSenSEyEYcoYTobzzKFlculWLOp1zn3nvLlFRzFum9Kb0BYpctg1GChjwCBpAkZl9p6109tTMfMODB8Yft8xXCftwPGB40fBcea3YRL3NiKBnh1zYRFhWYT1rLS2YCusutB7oJO1LGkswMYo/RMvGVedzogB6IB2gqgBLboXKcaistVVG/s1Ma05ayWqhzZlZu/vd64fbug1uV47PYLFdDryDZZFad+xsD5faSetktt1Z9FGV6FvA92CFknTygrvtwshznk1lmfP2MUZfS/ykQzs5gZbhctdMJyZ4QZ9T3qvUmaSNDNWhXVNOAVOfQZNpxhN9YnVFD0raYlHZa4i/uqJavSguF6D2AWVoBwGlBHFmB5Ro3EatS1FScDDH7BNzwl0pXqTqdicK89Wrw3A5vHrw2mmbDHwiyII1mAxxQy2HU4Kqa1uTFagjhRuRbhMApRnzc6LFjvc5etPIm87DgwfGH7fMQwHjg8cPx6OX4fidyLxUIXzTUO0zHKWpbGsbRrRBHsOhtdJVjPIxH1wo8mqp0nCmbDOKu+tJ6GdoSekd0wECSNqfJpNYOnVhxRTRjpbdE7AicZX//HvwgY4xS7OGkFqGiystFOiqzKo2fEi2SxF6ulCfzFoe2JNieXB+ljY8oqtwsuxIWosGCaOLkpnJ3ehj0JRmfMk27UYyjEVAX04eSPoyRAWTnZil40mVcaMSHIx7GzleWDGtg1YhBE1j65qNG24D7Z9xwOaLKAzw4+sMmBbiG0rw6gUIoNBkF5lQG0rvTnqZetcvUWnS3I2xUxnv9hruybblvSE26bcdYersKyGLEAOzouVxPLSaOr0OZq1nJXQZImEHnhMxYOYTz9SssRPEQeGDwy/7xiGA8cHjh8Rx68p3L0TiYcAz06DkUlIwzUYGqgkPQZnVUIHngGzF9Y1uLXGoKPNaNFwkhRnMUVOwul8QrZgHx2XxFsQOtBohEMnUZ+EJDEkjf0qXO6v6AZ7JsMVk6hyWiqtGecPDM7BZYzq/omiUGZFDv2rG0sXVhGSLKb2Lgzfefadz/FzIlrZfCcJCxZOhDu7d17eX1FVLNb6v14OgVWBDUyV1upppC0CJJFa+y8dler7tUzUlOide79nsTOpwRhaMrvXgTevMiFKzyJOjUiGCKN3FllwbUgouQR9OH0r4ZrWlLt9cEIJUbas8quaskRj2+4xWRAJsOJfaSpyUsbWiRCsrYxI9lE3IdPqk95Kso/AKIfFwaDjnNaV6+gskuz7mAZVUy44Oq0tB4YPDB8YPnB84PiJcfyq3/Yx8amJh4j8DPAnga9k5g/Pbf8R8K8DO/DrwJ/NzK/Ov/154Ccp3ZN/LzP/20/7jBQIW0rERUqxLvvADRbRV1K6HoFTqnTGwojkGrCk4t5JCVoTTtZQN67XnXCtEzPB2in3QY/BYoI0Y3iwjyhVvC3Iy6B5MiLwVLooiyWnpfqUNCMMMhTviYrTUGIMxgX8AosEXaUU/kIQCezGkHOWGM0kRSGg0UjJ6RGwcNYiRo1t0O8T9lLHCxFawumsLGvpEY+YNwEvkRnNmrkf4ewOXIXRQXwlw+jD8X0QXXFTpBmZddH5CJKaL6fvIEKYYgQ7vSymM8GK0vThttM9EWpuW01QMSKysuFVgXpyikWq3EfCgOXWyL2OdwAxwDNQlDVhO1mJF6m8IjtpCqbwnTeNi5S1s01XRw8QrPR6ngDHB4YPDL/vGD5wfOD4MXGc+cl6NN8Mf/pngR/5hm2/BPxwZv5zwN8D/vwE+g8Bfxr4Z+f//Oci31zhcBtRFsNZIBvhpBd7t3Z/MngEMqUskVHwyqqsCcvSaG0hAjzgch3IPNiSgoawUmIvjSpxOVnEnlFjZNvFq48XQg9hw3HJcvNrytCgM+gRRHcY1Y/MSMY1GZeBetCa4ZoMKvOkBeuzxsDxhz7cDNGsV41k9AFZo19j98p4MxjUmkxr9ltVJ/19jrMl5Igq7VEcsdGDsUeJ3oTSu5NpJEqEM3zQQ0oNsAc+CjSREGgJ7mRAVvl0G1NMCKUH7A69+FtMGCNzvKplkcTkYTcNsgk0Aa1eYEjiUk9TBsUav3qxrB08itRFJIogUmqKJ2uYwdqEU4NVwbQ+O+IT7tpvAccHhg8Mv+8YPnB84PixcPw6lsenVjwy81dE5Ae/Ydvf+Mivvwr8m/PnHwN+IUuy7P8SkV8D/ijwP7z2M6gZ5I82hRqC5pxlpvpSNZ5Tr8lIsKQpdbBaWQ+XXr6/cjW0cx18RtZYVdZMt6BoCt0DH+A7jC3pe6AO0eokpkWdMBNkUVylxr4CCJl7U0I3cQ18c86mIDKBE6iVBTQr7D7QpphSo0gyx5dGkbUivEqPe9J7MFwZ8/yJSJX9atETXPVzTNBm1GhZZmWu1Wosedt0ymiIsi2OcIgg3YliKSFSTpQOdbNRCNFiK09CGZTLYaYWvKT6rplFapI5hucPbokPa6zpq7o4JXANQoX0rHtZUlbVvUqYZN1E6pZX/clMZi94Po1R6xOSLoF8QpL9pnF8YPjA8PuO4QPHB44fFcevwdljcDz+beCvzp+/jwL/Q/zG3PapoWqvEmlToamh8wCHgqeTDopiWuSZ1MTWytaEUn3zDEQSI1jMEMoOeNFiWPsWZAgpRvSACK7XZN+S7EF2cNG6mDI5UfPX2RI3aK3VCXMhTRgRZK8LRLZAPNGzsY0SuEFL/lfWRs/BIDnP/Q+Z884u6KgTHirs12AbMhX+yp9YBJYmBYImhOaUzAV3ZwRsPv0IoIhYBjYSlWJYG43hgxEU8SrA3Gu0SoSg5teTWpd7sprRFUARBh7JeDCOApZ5g5IEmKBVwb0Y64LhmVPcpubE1QQwwhzM6GMgGajUBbxfHdFGM4U5slYXTI1wpQ9UBTPK1dLqhqfhxDdVxHszOD4wfGD4fcfwgeMDx4+F4zdGLhWRv0Adt//yW/jfLwNfBvhgqX7U8E5TQURKOEYbEkmbdrxOkCQm0JoRqyCjsq5iTFfG16z8AmxJ7vqVEyeEUltbVRgCEUFkjSSNfTA2R10AKT36EWgqzWHWkggFFLQJxBRpQRgexD5YHM5N6JH0PatvqFIKfpKctGGUvXFqldg8HJHkfNMYwxl743od7Fsyes25pySxJzcKy7IQWjr+EVl+BueFjrBH2UenlwKdSM1UqwR2OnEdSfes3qJXn24ZQdciD51M2T0Iycp+Ey7daTYIilgUEvOGUmt6yOo1wVCa1tqSUsZzaj+Hl6pdUt/l4ca2KhZKTLa0SvkqxTa4ubUayfO6KSBTZMjrScCMYrMPx3pwMmNvCV97Ozg+MHxg+H3H8IHjA8dvCsf6SaU7vo3EQ0T+DEV0+uOZrzTKfhP4gY+87Pvntt8TmfnTwE8D/KEby6BU1Jwad2ojUOnYqoga26UjYkgzugmugl9KoMT3gegyZWUTPZ9hrVGq7I0Y0KzKS25V9gPnbku2ey+VvKzSk6cjubBKIi05nYzzyWhNSAaZ4LaWMt8YhCexg25VausK930gKbQGdhK4UbJJCeakcX9MtLQ8AAAMZUlEQVTZCaL6g1Juf2kL9/cb9/fOdevsPXGHul4UuZF6eriFMCGkLnyRMvkZkrAk115a/Rpa8rhRZdO8XCsJFkVZSUkuW6fjjFkn26jyaPfAY7CWH/OrvqagWGtkOvfXIoSdlnrqGLu/KuPhHSPZU2ZmHK/KcEn1JoXkdFq4bImujXEdxD44tTmH78J25zUqN/u/+74jKKLFsB7DUVWaCrE0ElglPw5ubwTHB4YPDL/vGD5wfOD4TeH4dQq831LiISI/Avz7wL+Smfcf+dMvAv+ViPzHwB8GvgT8j5/+fiDqgHIypclsQKmQonSH1tY5zyxIGu5zjEgMORlko2VgEpQsfYIH1rREeaRU25ZoeAZ7BncvBjLKjyCAIeDZOBHlVHgy9GToAkaCaEnq9hKRsV4ZOj1KbOcGLluVCk/LSixJtMSBNeHaA7tCiLC2hUxleMBw3Dd6Crsrmw/22WcUYE9n5cxgp6fQM8gUIInoNDH2gGsK4sJIL+Z2GDmMiB093eLXS63B6gljiLOPRpNyMNT5xMBwxGHTuiAvI1mtWO6ZdWO4aStjdK6+g6wM6slDexUCTZXsA1o9ZQwvwloESNaFetlLZndQMsx2WvAIxlybUsdXGdUH1kY2yjQJwUXIFFRK7GbHH+bc3jqODwwfGH7fMXzg+MDxo+L4NfHNjNP+PPDHgO8Wkd8A/iLFnD4BvyTVyPnVzPx3MvPviMhfA/4Pquz35/J1MzUPkdBsmUprdUAWaYwRdGrhPsXshSR9YJK0daXvAyJJmR4CWgdTdMEXp2eytAXS8Uj6SL52V0IwfRhKoGE4pTpnKKbO+cY4rUvZO0vZO8sk5fTu5HD6pohXudFWhedntu2eINlscFKpE+ZFbMoI9oC2NHat/l+KEGZoGNs1GN15cS2qElKGPRbCKZzTB2fuRxZBK+oCTRo9ionuY7K4UtkzGLnzfL3BZGE4yLJWWTOS4cHmxUDXhD0cE6sRLRFaKxJRaGAYeySDRKVsrjUcQ6CtDC9CkyRkQM8yIVJbcIFL34uONO+nQUn5olWa6173Jk1BQmmiDJxt79iiNC21PKUY2xHVf02ttzSp42UY0j7+afGN4/jA8IHh9x3DB44PHD8mjh8+5GPim5lq+fGP2fxfvOb1fwn4S5/2vv+//wHGKMKKzDGtyAFhiIDnw4hOge2BfOV94GPQZLoYQrF0Tas3GI6IlXZ9Vk/tfnPu7pwcoFp9N5/ZmWrZJy8GHzxfiHly6/hNY54sA6D0yioloTVFm7HfBUQj5MppUUJkKtwVgUdEQBNrED5Ai7C19bp4+4DLXq5+nvXgk7N3WSzkxOk8aPcEFIt6UKNfQ+levcZUELXKak1BKaa1zj4f1fPT2etLE8ggH37PpMbfjHBHETZ3BEoSWOv4IdBIFF4Ryjyrj2gWbLtXyZV5Q8lBRr2ud1g8XxGZIoIRAVI4sKKb41F9RA0hRkzyW43htcm4LiZ+zc8/BY4PDB8Yft8xfOD4wPFj4vjbGqd9W5EIMUt2Vd0pzXtFcS9b5epOCSBsI5AMLCnnPmpKSyabOEkcQyLL+CeDfU+ulznzndSJT8gMmpRAjrbkdFLMBMJZ1lZZYZQ0bfkfCX2U22Jl4FrM573XSVqFdhJm8olkAlmjURElxhKlb+9ZBJ7ug21PrtsoyeHUVyNrqQkLDHFMtd4DrfKWJ+5O7wGh7F6fpWqYGD2CMZnNhKOmkzVdwMic7GZ0krzqYlMBkbqY59xUkbhIHAWt0SqBeUOoHmJmHZ9IofvAPVkeZtsfLtqsz82Y7xATBFKseAI0E1sUj7JfFoE2x9FMIDQKvBHzQimQf73F/fbjwPCB4fcdw3Dg+MAxj4Lj1+Qd707iAczsruayExCdZBypBT/kq0TSR40bSVPSip37MDZkIohJWQF7x1O4dmffAt8e+lpSGWkljJjAYqCLcLpRIgMR0FYWzzGzzxiCppK9xoZEYYSwj8RHZz0Z7dTQRWvuPSolDim1Ow9njALb10exjX04d9de8+JZQFQEo8yTbBV2hVUVPHlwAfQMfHRGgHvti5KQD4I/9SH73jnZQ0Yq81jXRTUCTlL/80q8SLRKme5F9sqZDc8dj6zyXK1AcepmU8ezZtPdaw2Z8grgiL0iPrV4YGNnZccimBWTfQzHbCGj1iASCIaKFC7maBfz/wCIukCfMg4MHxh+3zEMB44PHH/7OH5dvBOJRyb0cKo+U/PeJpBeSnFrWxg2wKtE5Tg2D8wOrLYSfYOIOrjaa0SJBcG4jsHdZdA3B6/ZdM2ZOSOsTbGW6CrcnA0zRRo1spQlBjPGQ1bY2PeBhsBZp8qdQ5T9MTLI9QSpEF+fl7ZZunw4+UjO7LvmwgNlH6UUKNLogGbQVFiavWJip5dXwfCYLpYFLDVj2wdDFKtEm8ykqeFZIA2tfl5mrasY5fUUgs0SmhSAc27vGdyqEgqJziy8Sn8i9QSUWSNewbRTlmRknzcb2HqdL7JY3xECYmjCxb1uNrPUCMnQxCWnc6KV8l+UV0RrhmqwLA0fNUYnUtLHSd0IniIODB8Yft8xDAeODxw/Io5fE+9E4gFUGS4qc2tmnE3pMUiKSeyDKSRT5bLujmkjES4vX2DaaK2hTbBmnKxA9OH9zuUyKivOAmfHZjnTWaXGqLCENigatszMXZDRcVcyDI+Bj06/BjdNOK8LVwf3QGTQVkGfN06idKrPZqI0gcTxkaxSLoX7Vo8SYsoI4f5uMEZyjWKSxxTfSRP0xkgpwtXoAWo0qYxzeEypYWd0r6eSRRk2S2+RoIm0pcqGEXV3QbGl5tmKD6R0L0a9UGQrmT27kYA1xtiJnJK5NCKEnCSiuhFUJr1nlqhNJn0MPI2FQEkkkgUlgGvWbcAnncwUtBk6IAbsEnS0bmwzU48RDIM2ynp77KNKuqakKW1ZKduKA8MHhg8Mfytx4PjA8WPgWFQ+GWNP3U8EEJH/F7gD/vFT78sbju/ms79GePp1/lOZ+Qff5gd+jjAMT39+30Y89RrfOoYBROQF8Hff9uc+QTz1+X1b8ZTr/EQMvxOJB4CI/M3M/CNPvR9vMj4Pa4TPzzq/MT4v6/48rPPzsMaPi8/Luo91Pm28vhFzxBFHHHHEEUcc8YhxJB5HHHHEEUccccRbi3cp8fjpp96BtxCfhzXC52ed3xifl3V/Htb5eVjjx8XnZd3HOp8w3hmOxxFHHHHEEUcc8dmPd6niccQRRxxxxBFHfMbjyRMPEfkREfm7IvJrIvJTT70/jxki8g9F5G+JyP8qIn9zbvsuEfklEfn78/t3PvV+/n5DRH5GRL4iIn/7I9s+dl1S8Z/O8/u/i8i/8HR7/ubis4rjA8MHht/3ODD87mH4SRMPETHgPwN+FPgh4MdF5Ieecp/eQPyrmfnPf2Sk6aeAX87MLwG/PH9/3+JngR/5hm2ftK4fpSy5vwR8GfjLb2kf31p8DnB8YPjA8PseB4bfIQw/dcXjjwK/lpn/IDN34BeAH3vifXrT8WPAz82ffw74N55wX76lyMxfAX7nGzZ/0rp+DPgrWfGrwHeIyPe+nT19a/F5w/GB4QPD73scGH5CDD914vF9wP/zkd9/Y277rEQCf0NE/mcR+fLc9j2Z+Vvz538EfM/T7Nqjxyet67N+juGzvcYDw5/t8/sQn+U1Hhh+x87vO+PV8hmNfzkzf1NE/hDwSyLyf370j5mZ8i5YUT5yfFbX9TmNA8NHvO9xYPgdi6euePwm8AMf+f3757bPRGTmb87vXwH+a6qc+dsPJa75/StPt4ePGp+0rs/0OZ7xmV3jgWHgM3x+PxKf2TUeGAbesfP71InH/wR8SUT+aRFZgT8N/OIT79OjhIg8E5EPHn4G/jXgb1Pr+4n5sp8A/vrT7OGjxyet6xeBf2uyqv8l4GsfKQV+VuIzieMDwweGn3ifvu04MPyOYjgzn/QL+BPA3wN+HfgLT70/j7iufwb43+bX33lYG/AHKLbx3wf+O+C7nnpfv4W1/TzwW0CneoU/+UnrAoRiy/868LeAP/LU+/+GjslnDscHhg8Mv+9fB4bfTQwfyqVHHHHEEUccccRbi6dutRxxxBFHHHHEEZ+jOBKPI4444ogjjjjircWReBxxxBFHHHHEEW8tjsTjiCOOOOKII454a3EkHkccccQRRxxxxFuLI/E44ogjjjjiiCPeWhyJxxFHHHHEEUcc8dbiSDyOOOKII4444oi3Fv8fHID0BxxFhyMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 648x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sM3CRUAQPu9V"
      },
      "source": [
        "### Visualise our segmentation algorithm performs on DRISHTI dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UR6jHFF-QAsM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7eafae0e-d4cb-4507-b834-a2b1ba8274f6"
      },
      "source": [
        "test_s=int(X_test_dri.shape[0])\n",
        "test_s"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziRcdDQBkWzf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585,
          "referenced_widgets": [
            "11db5a89c9904f0eb4a1d65d204a1a93",
            "09446d9fe6864c778ae84f0b306f896f",
            "0deee4e46bf441f9868d5dd3deb80469",
            "6f15c82de98a417f878e876b1512683f",
            "1d5dba232bfb4174a90b4902f069e9be",
            "fb1f6821d40b4bbda903aef165665f6e",
            "36f137f8a13a4da3a577a8b594d43bfe",
            "5b4896f3e9ad4bc08f70fe80a0c1c844"
          ]
        },
        "outputId": "ad7133c5-6f9e-41df-c1e9-2a06463613af"
      },
      "source": [
        "#%%time\n",
        "pred_dice=np.empty((test_s), dtype=np.float32)\n",
        "pred_iou=np.empty((test_s), dtype=np.float32)\n",
        "\n",
        "batch_X = X_test_dri[0:test_s]\n",
        "batch_y = Y_test_dri[0:test_s]\n",
        "print (batch_X.shape)\n",
        "    \n",
        "predv = (model.predict(batch_X,batch_size=test_s)[...,0]>0.5).astype(np.float32)\n",
        "corrv= (batch_y)[ ..., 0].astype(np.float32)\n",
        "predv1=predv.copy()\n",
        "\n",
        "for i in tqdm_notebook(range(predv.shape[0])):\n",
        "    img = X_test_dri[i]\n",
        "    pred = predv[i]\n",
        "    corr = corrv[i]\n",
        "    label_pc = skimage.measure.label(pred, connectivity=pred.ndim)\n",
        "    props_pc = skimage.measure.regionprops(label_pc)\n",
        "    sz=len(props_pc)\n",
        "    if(sz)!=0:\n",
        "      areap=[props_pc[i].area for i in range(sz)]\n",
        "      k=np.argmax(areap)\n",
        "      predv1[i]=np.equal(label_pc,k+1).astype(int).astype(np.float32)\n",
        "    pred1= predv1[i]\n",
        "    cur_dice = np_dice_coef(pred1, corr)\n",
        "    cur_iou = np_iou(pred1,corr)\n",
        "    pred_dice[i]=(cur_dice)\n",
        "    pred_iou[i]=cur_iou\n",
        "    \n",
        "    \n",
        "d_worst=np.amin(pred_dice)\n",
        "d_best=np.amax(pred_dice)\n",
        "d_mean=np.mean(pred_dice)\n",
        "d_std=np.std(pred_dice)\n",
        "print(\"Dice mean=\",d_mean, \" Std=\",d_std, \" best=\",d_best,\" worst=\",d_worst)\n",
        "\n",
        "i_worst=np.amin(pred_iou)\n",
        "i_best=np.amax(pred_iou)\n",
        "i_mean=np.mean(pred_iou)\n",
        "i_std=np.std(pred_iou)\n",
        "print(\"IOU mean=\",i_mean, \" Std=\",i_std, \" best=\",i_best,\" worst=\",i_worst)\n",
        "\n",
        "ix_best=np.argmax(pred_dice)\n",
        "ix_worst=np.argmin(pred_dice)\n",
        "\n",
        "j=0\n",
        "for i in [ix_best,ix_worst]:\n",
        "    print('image #{}'.format(i))\n",
        "    img = X_test_dri[i]\n",
        "    pred = predv1[i]\n",
        "    corr = corrv[i]\n",
        "    print(pred_dice[i])\n",
        "    fig = plt.figure(figsize=(9, 4))\n",
        "    ax = fig.add_subplot(1, 3, 1)\n",
        "    edgesp = canny(pred)\n",
        "    edgesp=1.0-dilation(edgesp)\n",
        "    edgesp = color.gray2rgb(1.0*edgesp)\n",
        "    ax.imshow(img*edgesp)\n",
        "    ax.set_title('Predicted')\n",
        "    ax = fig.add_subplot(1, 3, 2)\n",
        "    edgesc = canny(corr)\n",
        "    edgesc=1.0-dilation(edgesc)\n",
        "    edgesc = color.gray2rgb(1.0*edgesc)\n",
        "    ax.imshow(img*edgesc)\n",
        "    ax.set_title('Correct')\n",
        "    ax = fig.add_subplot(1, 3, 3)\n",
        "    ax.imshow(img)\n",
        "    ax.set_title('Image')\n",
        "    fig.savefig(os.path.join('/content/gdrive/My Drive/Medical Image/cnn_term_paper','img_drishti'+str(j)+'model1_cup'+'.png'),dpi=600)\n",
        "    j+=1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(13, 128, 128, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11db5a89c9904f0eb4a1d65d204a1a93",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=13.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Dice mean= 0.89671296  Std= 0.056405924  best= 0.9680246  worst= 0.8019354\n",
            "IOU mean= 0.81743157  Std= 0.09135907  best= 0.9380307  worst= 0.669359\n",
            "image #11\n",
            "0.9680246\n",
            "image #7\n",
            "0.8019354\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}