ISS296-Test4-AvgPool-kernel3x3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\rassa\\anaconda3\\lib\\site-packages\\tensorflow\\python\\compat\\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n",
      "curses is not supported on this machine (please install/reinstall curses for an optimal experience)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sys\n",
    "import tflearn.data_utils as du\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, AveragePooling2D \n",
    "from keras.optimizers import RMSprop\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from keras.preprocessing.image import load_img \n",
    "from keras.preprocessing.image import img_to_array \n",
    "from keras.preprocessing.image import array_to_img\n",
    "import warnings\n",
    "import matplotlib.image as mpimg\n",
    "import os \n",
    "import cv2\n",
    "import tensorflow as tf \n",
    "from PIL import Image \n",
    "from numpy import asarray\n",
    "from tqdm import tqdm\n",
    "from tkinter import Tk     # from tkinter import Tk for Python 3.x\n",
    "from tkinter.filedialog import askopenfilename\n",
    "from tkinter import messagebox\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████| 13440/13440 [00:10<00:00, 1314.99it/s]\n",
      "100%|████████████████████████████████████████████████████████████████████████████| 3360/3360 [00:01<00:00, 2568.26it/s]\n"
     ]
    }
   ],
   "source": [
    "#we need to convert the images to a matrix so we can build our test and train sets\n",
    "\n",
    "DIRECTORY = r\"C:\\Users\\rassa\\Documents\\Machine Learning A-Z  Udemy\\Arabic Handwritten Characters Dataset\"\n",
    "train_data = []\n",
    "test_data = []\n",
    "trainPath = DIRECTORY + r\"\\Train Images 13440x32x32\\train\"\n",
    "testPath  = DIRECTORY + r\"\\Test Images 3360x32x32\\test\"\n",
    "\n",
    "def getImgId(imgName):\n",
    "    return int(imgName.split('_')[1])\n",
    "\n",
    "def makeData(data, directory, sortingCriteria):\n",
    "    imgFileNameList = os.listdir(directory)     #get list of directories in path (ie. list of image names)\n",
    "    imgFileNameList.sort(key = sortingCriteria) #sort list based on key\n",
    "    \n",
    "    for img in tqdm(imgFileNameList):  # iterate over the sorted list\n",
    "                try:\n",
    "                    img_array = cv2.imread(os.path.join(directory, img) ,cv2.IMREAD_GRAYSCALE)  # convert image to array\n",
    "                    data.append([img_array])  # add this to our data\n",
    "                except Exception as e:  # just in case there is a mistake.\n",
    "                    pass\n",
    "\n",
    "makeData(train_data, trainPath, getImgId)\n",
    "makeData(test_data, testPath, getImgId)\n",
    "\n",
    "# convert to ndarray and reshape\n",
    "train_data = np.asarray(train_data).reshape([-1, 32, 32, 1])\n",
    "test_data = np.asarray(test_data).reshape([-1, 32, 32, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the labels\n",
    "#header = 0 because\n",
    "#this creates a pandas dataframe (basically a table)\n",
    "train_label = pd.read_csv(DIRECTORY + '/csvTrainLabel 13440x1.csv', header = None)\n",
    "test_label = pd.read_csv(DIRECTORY +  '/csvTestLabel 3360x1.csv', header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_data = train_data.iloc[:,:].values.astype('float32')\n",
    "train_label = train_label.iloc[:,:].values.astype('int32')-1\n",
    "#test_data = test_data.iloc[:,:].values.astype('float32') we don't need these anymore\n",
    "test_label = test_label.iloc[:,:].values.astype('int32')-1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_label = du.to_categorical(train_label,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data/255\n",
    "test_data = test_data/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_data, mean1 = du.featurewise_zero_center(train_data)\n",
    "#test_data, mean2 = du.featurewise_zero_center(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "recognizer = Sequential()\n",
    "#for any kind of deep learning we use the sequential model in keras then \n",
    "#add layers to it\n",
    "recognizer.add(Conv2D(filters = 32, kernel_size = (3,3),padding = 'Same', \n",
    "                 activation ='relu', input_shape = (32,32,1)))\n",
    "#the first 2 dimensional convolusion layer will have 32 filters. filters or kernels are \n",
    "#what we use to extract features. in this case they are of size 3x3 (kernel size)\n",
    "#in the first layer it is necessary to specify the shape so for our case its a\n",
    "#32x32 pixel image and since it's black and white then it has only one dimension or channel\n",
    "#if it was colored then we would have (32,32,3)\n",
    "#activation='relu' this is rectified linear unit. the output filters or convolved layers\n",
    "#might contain some negative values so we apply the rectifier function (or other functions) to break linearity\n",
    "recognizer.add(Conv2D(filters = 32, kernel_size = (3,3),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "recognizer.add(AveragePooling2D(pool_size=(2,2)))\n",
    "recognizer.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "recognizer.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "recognizer.add(Conv2D(filters = 64, kernel_size = (3,3),padding = 'Same', \n",
    "                 activation ='relu'))\n",
    "recognizer.add(MaxPool2D(pool_size=(2,2), strides=(1,1)))\n",
    "recognizer.add(Dropout(0.25))\n",
    "\n",
    "\n",
    "recognizer.add(Flatten())\n",
    "recognizer.add(Dense(units = 256, input_dim = 1024, activation = 'relu'))\n",
    "recognizer.add(Dense(units = 256, activation = \"relu\"))\n",
    "recognizer.add(Dropout(0.5))\n",
    "recognizer.add(Dense(28, activation = \"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 32, 32, 32)        320       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "average_pooling2d (AveragePo (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 14400)             0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 256)               3686656   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 28)                7196      \n",
      "=================================================================\n",
      "Total params: 3,824,636\n",
      "Trainable params: 3,824,636\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "recognizer.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "recognizer.compile(optimizer = optimizer , loss = \"categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "        featurewise_center=False, \n",
    "        samplewise_center=False,  \n",
    "        featurewise_std_normalization=False,\n",
    "        samplewise_std_normalization=False,\n",
    "        zca_whitening=False,\n",
    "        rotation_range=10,\n",
    "        zoom_range = 0.1,  \n",
    "        width_shift_range=0.1, \n",
    "        height_shift_range=0.1,\n",
    "        horizontal_flip=False,\n",
    "        vertical_flip=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen.fit(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rassa\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_v1.py:1240: UserWarning: `model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  warnings.warn('`model.fit_generator` is deprecated and '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "134/134 - 28s - loss: 2.4661 - acc: 0.2574\n",
      "Epoch 2/30\n",
      "134/134 - 27s - loss: 1.3486 - acc: 0.5564\n",
      "Epoch 3/30\n",
      "134/134 - 29s - loss: 0.9652 - acc: 0.6831\n",
      "Epoch 4/30\n",
      "134/134 - 28s - loss: 0.7489 - acc: 0.7517\n",
      "Epoch 5/30\n",
      "134/134 - 28s - loss: 0.6169 - acc: 0.7974\n",
      "Epoch 6/30\n",
      "134/134 - 29s - loss: 0.5255 - acc: 0.8313\n",
      "Epoch 7/30\n",
      "134/134 - 29s - loss: 0.4698 - acc: 0.8501\n",
      "Epoch 8/30\n",
      "134/134 - 31s - loss: 0.4104 - acc: 0.8698\n",
      "Epoch 9/30\n",
      "134/134 - 31s - loss: 0.3788 - acc: 0.8773\n",
      "Epoch 10/30\n",
      "134/134 - 30s - loss: 0.3434 - acc: 0.8902\n",
      "Epoch 11/30\n",
      "134/134 - 30s - loss: 0.3243 - acc: 0.8969\n",
      "Epoch 12/30\n",
      "134/134 - 33s - loss: 0.3086 - acc: 0.8993\n",
      "Epoch 13/30\n",
      "134/134 - 31s - loss: 0.2806 - acc: 0.9100\n",
      "Epoch 14/30\n",
      "134/134 - 30s - loss: 0.2703 - acc: 0.9159\n",
      "Epoch 15/30\n",
      "134/134 - 31s - loss: 0.2642 - acc: 0.9152\n",
      "Epoch 16/30\n",
      "134/134 - 30s - loss: 0.2366 - acc: 0.9223\n",
      "Epoch 17/30\n",
      "134/134 - 31s - loss: 0.2332 - acc: 0.9256\n",
      "Epoch 18/30\n",
      "134/134 - 31s - loss: 0.2268 - acc: 0.9270\n",
      "Epoch 19/30\n",
      "134/134 - 31s - loss: 0.2125 - acc: 0.9337\n",
      "Epoch 20/30\n",
      "134/134 - 31s - loss: 0.2173 - acc: 0.9316\n",
      "Epoch 21/30\n",
      "134/134 - 31s - loss: 0.1972 - acc: 0.9364\n",
      "Epoch 22/30\n",
      "134/134 - 30s - loss: 0.1976 - acc: 0.9383\n",
      "Epoch 23/30\n",
      "134/134 - 30s - loss: 0.1939 - acc: 0.9381\n",
      "Epoch 24/30\n",
      "134/134 - 31s - loss: 0.1835 - acc: 0.9443\n",
      "Epoch 25/30\n",
      "134/134 - 31s - loss: 0.1870 - acc: 0.9422\n",
      "Epoch 26/30\n",
      "134/134 - 30s - loss: 0.1798 - acc: 0.9434\n",
      "Epoch 27/30\n",
      "134/134 - 31s - loss: 0.1752 - acc: 0.9430\n",
      "Epoch 28/30\n",
      "134/134 - 30s - loss: 0.1916 - acc: 0.9428\n",
      "Epoch 29/30\n",
      "134/134 - 30s - loss: 0.1782 - acc: 0.9448\n",
      "Epoch 30/30\n",
      "134/134 - 30s - loss: 0.1736 - acc: 0.9463\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1b88450a9a0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recognizer.fit_generator(datagen.flow(train_data,train_label, batch_size=100),\n",
    "                             epochs = 30, verbose = 2, steps_per_epoch=train_data.shape[0] // 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rassa\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py:2325: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  warnings.warn('`Model.state_updates` will be removed in a future version. '\n"
     ]
    }
   ],
   "source": [
    "predictions = recognizer.predict(test_data)\n",
    "predictions = np.argmax(predictions,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(test_label, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy = 0.9607142857142857\n"
     ]
    }
   ],
   "source": [
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = sum(cm[i][i] for i in range(28)) / test_label.shape[0]\n",
    "print(\"accuracy = \" + str(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def outputMsg(l):\n",
    "    switcher = {\n",
    "        0: \"zero\",\n",
    "        1 : \"alif\",\n",
    "        2 : \"ba2\",\n",
    "        3: \"ta2\",\n",
    "        4: \"tha2\",\n",
    "        5: \"jha2\",\n",
    "        6: \"7a2\",\n",
    "        7: \"5a2\",\n",
    "        8: \"da2\",\n",
    "        9: \"thal\",\n",
    "        10: \"ra2\",\n",
    "        11: \"za2\",\n",
    "        12: \"seen\", \n",
    "        13: \"sha2\",\n",
    "        14: \"saad\",\n",
    "        15: \"thaad\",\n",
    "        16: \"ttaa2\",\n",
    "        17: \"ththa\",\n",
    "        18: \"3ayn\",\n",
    "        19: \"ghayn\",\n",
    "        20: \"fa2\",\n",
    "        21: \"9a2\",\n",
    "        22: \"ka2\",\n",
    "        23: \"lam\",\n",
    "        24: \"meem\",\n",
    "        25: \"noon\",\n",
    "        26: \"haa2\",\n",
    "        27: \"waw\",\n",
    "        28: \"ya2\"\n",
    "    }\n",
    "    print(\"Your letter is \" + switcher.get(l, \"Invalid label\"))\n",
    "    #messagebox.showinfo(\"Prediction\", \"Your letter is \" + switcher.get(l, \"Invalid label\"))\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Enter the alpha value [1.0-3.0]: 1.25\n",
      "* Enter the beta value [0-100]: 0\n",
      "202.0\n",
      "[[0.0000000e+00 1.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      "  0.0000000e+00 4.1058176e-34 0.0000000e+00 0.0000000e+00 0.0000000e+00\n",
      "  0.0000000e+00 0.0000000e+00 0.0000000e+00]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD5CAYAAADhukOtAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMHElEQVR4nO3db6hkdR3H8fc3UwpdaM20ZV3bEglCapWLBEkYVGz7RA2MerRBcHuQoA+CloLaemSRRo+ELZe2KEuwUiQykcJ6Yq62rte20mSz1YtLbKE+6o/fHsxZurvd+bMz55yZu9/3C4Y5c+7MOV/Onc+c3zm/mfOLzETS2e918y5AUj8Mu1SEYZeKMOxSEYZdKsKwS0W8fpYXR8RO4JvAOcC3M/O2Mc+3n0/qWGbGevNj2n72iDgH+BPwIeAY8Bjwicz8/YjXGHapY8PCPksz/hrg2cx8LjP/CfwQuH6G5Unq0Cxh3wr8dc3jY808SQtolmP29ZoK/9dMj4hlYHmG9UhqwSxhPwZsW/P4UuDF05+UmfuAfeAxuzRPszTjHwOuiIi3R8R5wMeB+9spS1Lbpt6zZ+a/I+Jm4EEGXW/7M/Pp1iqT1Kqpu96mWpnNeKlzXXS9SdpADLtUhGGXijDsUhGGXSrCsEtFGHapCMMuFWHYpSIMu1SEYZeKMOxSEYZdKsKwS0UYdqkIwy4VYdilIgy7VIRhl4ow7FIRhl0qwrBLRRh2qQjDLhVh2KUiZhnYkYg4CrwC/Af4d2YutVGUpPbNFPbGBzLzby0sR1KHbMZLRcwa9gR+ERGPR8RyGwVJ6saszfj3ZeaLEXEx8FBE/CEzH1n7hOZDwA8Cac5aG7I5IvYCr2bm10c8xyGbpY61PmRzRJwfEZtOTgMfBlamXZ6kbs3SjL8E+ElEnFzODzLz561UJWmkYS3ypaXhvd9Thz0znwPeM+3rJfXLrjepCMMuFWHYpSIMu1SEYZeKMOxSEYZdKsKwS0UYdqkIwy4VYdilIgy7VIRhl4ow7FIRhl0qwrBLRRh2qQjDLhVh2KUiDLtUhGGXijDsUhGGXSrCsEtFGHapiLFhj4j9EXE8IlbWzLswIh6KiGea+83dlilpVpPs2b8D7Dxt3h7g4cy8Ani4eSxpgY0NezPe+onTZl8PHGimDwA3tFuWpLZNe8x+SWauAjT3F7dXkqQuzDJk80QiYhlY7no9kkabds/+UkRsAWjujw97Ymbuy8ylzBw+cLSkzk0b9vuB3c30buC+dsqR1JXIzNFPiLgbuA64CHgJ+BLwU+Ae4DLgeeCmzDz9JN56yxq9sgU3blutJyI6qEQaLjPXfdONDXubDLvUvWFh9xt0UhGGXSrCsEtFGHapCMMuFdH5N+ikSXXRM2RvyP+4Z5eKMOxSEYZdKsKwS0UYdqkIwy4VYdfbafr8YZC6N+z/WbFLzj27VIRhl4ow7FIRhl0qwrBLRZQ8G9/2GfeKZ3a7MGo72ksyO/fsUhGGXSrCsEtFGHapCMMuFWHYpSLGhj0i9kfE8YhYWTNvb0S8EBGHmtuubsuU2pWZQ29nq0n27N8Bdq4z/xuZuaO5/azdsiS1bWzYM/MRYOygjZIW2yzH7DdHxOGmmb+5tYokdWLasN8JXA7sAFaB24c9MSKWI+JgRByccl2SWjDRkM0RsR14IDOvPJO/rfPchTj74XfjN56ehxbvbV1daHXI5ojYsubhjcDKsOdKWgxjf/UWEXcD1wEXRcQx4EvAdRGxA0jgKPDp7kpcHBv9E1+TGdWK2MjvgYma8a2tbIM34zfyP3qjW5T+743wHmi1GS9p4zHsUhGGXSrCsEtFGHapiJIXnJyWQwnNru+z6tP8b0bVuCi9AsMsLS0N/Zt7dqkIwy4VYdilIgy7VIRhl4ow7FIRJbve2h5TrIvumGm78xa9a6gLbXd9nq1jzrlnl4ow7FIRhl0qwrBLRRh2qYiSZ+NHWZQzsRv5rO+0NsIPijZCjcO4Z5eKMOxSEYZdKsKwS0UYdqkIwy4VMcnwT9uA7wJvBV4D9mXmNyPiQuBHwHYGQ0B9LDP/3l2p87eRu12kscM/NYM4bsnMJyJiE/A4cAPwSeBEZt4WEXuAzZn5uTHLqtd5LPVs6uGfMnM1M59opl8BjgBbgeuBA83TDjD4AJC0oM7omL0Zi/0q4FHgksxchcEHAnBx69VJas3EX5eNiAuAe4FbM/PlSY9fI2IZWJ6uPEltmWjI5og4F3gAeDAz72jm/RG4LjNXm+P6X2XmO8csx2N2qWNTH7PHYBd+F3DkZNAb9wO7m+ndwH2zFimpO5Ocjb8W+DXwFIOuN4DPMzhuvwe4DHgeuCkzT4xZlnt2qWPD9uwTNePbYtil7k3djJd0djDsUhGGXSrCsEtFGHapCMMuFWHYpSIMu1SEYZeKMOxSEYZdKsKwS0UYdqkIwy4VYdilIgy7VIRhl4ow7FIRhl0qwrBLRRh2qQjDLhVh2KUiDLtUhGGXiphkrLdtEfHLiDgSEU9HxC3N/L0R8UJEHGpuu7ovVwCZ2epNNUwy1tsWYEtmPhERm4DHgRuAjwGvZubXJ16Zwz+1ou2ATjr8tjaGYcM/jR2fPTNXgdVm+pWIOAJsbbc8SV07o2P2iNgOXMVgBFeAmyPicETsj4jNbRcnqT0Thz0iLgDuBW7NzJeBO4HLgR0M9vy3D3ndckQcjIiDs5craVoTDdkcEecCDwAPZuYd6/x9O/BAZl45Zjkes7fAY3aNMvWQzTF4J9wFHFkb9ObE3Uk3AiuzFimpO5Ocjb8W+DXwFPBaM/vzwCcYNOETOAp8ujmZN2pZ7tlb4J5dowzbs0/UjG+LYW+HYdcoUzfjJZ0dDLtUhGGXijDsUhGGXSpi7HfjdXbwjLvcs0tFGHapCMMuFWHYpSIMu1SEYZeKsOttQXkhSLXNPbtUhGGXijDsUhGGXSrCsEtFGHapCLveFtSoX6mN6pbz120axj27VIRhl4ow7FIRhl0qwrBLRUwy1tsbIuK3EfFkRDwdEV9u5l8YEQ9FxDPNvUM29yQiht6kYSYZ6y2A8zPz1WY0198AtwAfBU5k5m0RsQfYnJmfG7Msf8oldWzq4Z9y4NXm4bnNLYHrgQPN/APADbOXKakrEx2zR8Q5EXEIOA48lJmPApecHLW1ub+4syolzWyisGfmfzJzB3ApcE1EXDnpCiJiOSIORsTBKWuU1IIzOhufmf8AfgXsBF6KiC0Azf3xIa/Zl5lLmbk0W6mSZjHJ2fi3RMSbmuk3Ah8E/gDcD+xunrYbuK+jGiW1YJKz8e9mcALuHAYfDvdk5lci4s3APcBlwPPATZl5YsyyPBsvdWzY2fixYW+TYZe6N3XXm6Szg2GXijDsUhGGXSrCsEtF9H0Nur8Bf2mmL2oez5t1nMo6TrXR6njbsD/02vV2yoojDi7Ct+qswzqq1GEzXirCsEtFzDPs++a47rWs41TWcaqzpo65HbNL6pfNeKmIuYQ9InZGxB8j4tnm+nVzERFHI+KpiDjU58U1ImJ/RByPiJU183q/gOeQOvZGxAvNNjkUEbt6qGNbRPwyIo40FzW9pZnf6zYZUUev26Szi7xmZq83Bj+V/TPwDuA84EngXX3X0dRyFLhoDut9P3A1sLJm3teAPc30HuCrc6pjL/DZnrfHFuDqZnoT8CfgXX1vkxF19LpNgAAuaKbPBR4F3jvr9pjHnv0a4NnMfC4z/wn8kMHFK8vIzEeA03/73/sFPIfU0bvMXM3MJ5rpV4AjwFZ63iYj6uhVDrR+kdd5hH0r8Nc1j48xhw3aSOAXEfF4RCzPqYaTFukCnjdHxOGmmd/reAARsR24isHebG7b5LQ6oOdt0sVFXucR9vV+WD+vLoH3ZebVwEeAz0TE++dUxyK5E7gc2AGsArf3teKIuAC4F7g1M1/ua70T1NH7NskZLvI6zDzCfgzYtubxpcCLc6iDzHyxuT8O/ITBIca8THQBz65l5kvNG+014Fv0tE2aAUjuBb6fmT9uZve+TdarY17bpFn3PzjDi7wOM4+wPwZcERFvj4jzgI8zuHhlryLi/IjYdHIa+DCwMvpVnVqIC3iefDM1bqSHbdKMOnQXcCQz71jzp163ybA6+t4mnV3kta8zjKedbdzF4Eznn4EvzKmGdzDoCXgSeLrPOoC7GTQH/8WgpfMp4M3Aw8Azzf2Fc6rje8BTwOHmzbWlhzquZXAodxg41Nx29b1NRtTR6zYB3g38rlnfCvDFZv5M28Nv0ElF+A06qQjDLhVh2KUiDLtUhGGXijDsUhGGXSrCsEtF/BcD+8j697BD+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Read image given by user\n",
    "\n",
    "\n",
    "Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n",
    "filename = askopenfilename() # show an \"Open\" dialog box and return the path to the selected file\n",
    "image = cv2.imread(filename)#r\"C:\\Users\\rassa\\Desktop\\Desktop2.0\\CNN\\9alb-Ba2-0.png\") #(cv.samples.findFile(args.input))\n",
    "\n",
    "contrast_img = np.zeros(image.shape, image.dtype)\n",
    "\n",
    "alpha = 1.0 # Simple contrast control\n",
    "beta = 0    # Simple brightness control\n",
    "\n",
    "# Initialize values\n",
    "try:\n",
    "    alpha = float(input('* Enter the alpha value [1.0-3.0]: '))\n",
    "    beta = int(input('* Enter the beta value [0-100]: '))\n",
    "except ValueError:\n",
    "    print('Error, not a number')\n",
    "    \n",
    "# Do the operation new_image(i,j) = alpha*image(i,j) + beta\n",
    "# Instead of these 'for' loops we could have used simply:\n",
    "# new_image = cv.convertScaleAbs(image, alpha=alpha, beta=beta)\n",
    "# but we wanted to show you how to access the pixels :)\n",
    "for y in range(image.shape[0]):\n",
    "    for x in range(image.shape[1]):\n",
    "        for c in range(image.shape[2]):\n",
    "            # the clip makes sure that the new values of the pixel are between 0 and 255\n",
    "            contrast_img[y,x,c] = np.clip(alpha*image[y,x,c] + beta, 0, 255)\n",
    "        \n",
    "#removethe three dimensions and turn image to gray scale\n",
    "im_gray = cv2.cvtColor(contrast_img, cv2.COLOR_BGR2GRAY)\n",
    "#binarize the image using Otsu method to get the threshold, binarize to black and white(ie. 255)\n",
    "th, im_gray_th_otsu = cv2.threshold(im_gray, 128, 255, cv2.THRESH_OTSU)\n",
    "#resizing...\n",
    "resized = cv2.resize(im_gray_th_otsu, (32,32), interpolation = cv2.INTER_AREA)\n",
    "\n",
    "#print the threshold because I'm curious\n",
    "print(th)\n",
    "\n",
    "final_img = np.zeros(resized.shape, resized.dtype)\n",
    "for y in range(resized.shape[0]):\n",
    "    for x in range(resized.shape[1]):\n",
    "        if resized[y,x] < 125:     \n",
    "            final_img[y,x] = 255\n",
    "        else :\n",
    "            final_img[y,x] = 0\n",
    "\n",
    "#Show the images so we can compare them (use the cv2.imshow if you want to ee them all at once)\n",
    "#cv2.imshow('New Image', im_gray_th_otsu)\n",
    "#cv2.imshow('Original Image', image)\n",
    "#cv2.imshow('Contrast image', contrast_img)\n",
    "#cv2.imshow('resized Image', resized)\n",
    "#cv2.imshow('final Image', final_img)\n",
    "plt.imshow(image)\n",
    "plt.imshow(contrast_img)\n",
    "plt.imshow(im_gray_th_otsu, cmap='gray', vmin=0, vmax=255)\n",
    "plt.imshow(resized, cmap='gray', vmin=0, vmax=255)\n",
    "plt.imshow(final_img, cmap='gray', vmin=0, vmax=255)\n",
    "\n",
    "\n",
    "final_img = np.asarray(final_img).reshape([-1, 32, 32, 1])\n",
    "#final_img.reshape([1,32,32,1])\n",
    "#final_img = np.expand_dims(final_img, -1)\n",
    "\n",
    "predictions_single = recognizer.predict(final_img)\n",
    "print(predictions_single)\n",
    "\n",
    "#let's get our label\n",
    "label = 0\n",
    "for i in range(28):\n",
    "    if predictions_single[0][i] == 1:\n",
    "        label = i+1\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "Your letter is ba2\n"
     ]
    }
   ],
   "source": [
    "print(label)\n",
    "outputMsg(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b885f57fd0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KJA4EMlxgEpUJEtFUS+/Gbi7tQRgVgNoSZR5sC4eDkrFCG6SmAMi8sZaLUykGL4LeLYk8d83Y582D/NIylEHPpL23BWQ0RkQ4nFpJLmeISIROG1hfmbwe0NG9jJIJ3CHXJUM3HGckUznx0fstYsR9HIczMON66VBprr2ld2MOZcqcPxKZM6vxDkCUzMo44Mx85zkN4VA4VHSAW4Lr/u5KjDRtCDGE/TQD0ubMp0xcuHZhPDlj5Ya86KgywSaSO12a9/0lJuCzoCm8bA3IuEy/rIIlYb64mcS1qNicwl5RkZdrbQp0N4F2v8jeHodRvZ+tOHrCzCeUXSQzQyt2JZkloQThaWKK9MoxFERCnQUKcdJIDOkEaZ0E26DIvvTeSnKFEVubJUOJWpT9aL1op44ifwDPVxHprjmIr8SZicwj+S5pOJn/6xq89XQMOciKSQ7nBRDCQ5plLbErc06g5AwTrh29R1ZW2JvwfbgmJXlDanDXWmHOILOAqmNPul2r7kQDgNYRGnES+jaBRe6sL7aDbeZ7kdCbAiHimJy3xbwYHg/vhQVd2BrWR+VzE+iW9BH7pqfCokA9xy7Fk2GK39+ZWVohCMOYu98ehlGr7ccYVJUya0gkYRq9akIbkBKNTFNHH9ASANKZlKLyFhYBJjxo87beEnYRBhfHSMClsKFbaESGncz0KhupldqhBeE2D6LX95HtElHZPfTwBiyT5llFA70fSCngUDH6aK0X5jIG0ugDnIAsbVeSuOpYi2alAJnI+aNGnKpoR4t8XF5PrW7yTNGP7muQMJFzyz5kIr2F1Gy+Jo7eS3K7hEAJOgh6ENDJU8dCwUoElcirhaCkeVTj3B0d2tJOhpRYO80Krg1Wx6XCUj0f35nk/BHu+9cwaXXxY4o7JTyuKKkpUlrla5rIMvmqCQWN5wXSj3CZzzEtad+1YQ0tLH1a8oVtQAwHMAupthrYFvZ5cJw5F+EEaxQCBu38Th/nmtSRlRRMcwPMWErIPIvXI2CfT0h8x75JbnELZEOm4jNs46iFWfyBtN2XcEf0M5nTxVcyvUjY1RXWVnMm7F4U9MN1Qc4BaWVBYouwznWYRD7SPBtOPHsrNxbYWqZAShgPTPdDBFpSwHWoV74AoLZtkmhNPBFDdFVC99pDMGpoo+LYqU6bOgjLPOo4z3NZ3LRnk8kj19Yng7H+6iwIinPlOsagMg44Eo6jKgTq5yJFq0rpr4DhAxRiXEI9vFeZmbLaUiQ2Mikl/QqrEe+rhEmP7sSwJIRgHOuPBeIrxkgG5AZ49qh5hQSOCEBq+5gvuPOpBZHTLpv8G4kMIBJCrjn0prbM+aVdnjB5cerV0bdmHfAvmnRAMlHCfAGrxz5Canw3hYmP3IQIKzCkLU+7PdaBdAAspsoSDWibaafDNgCIhFB9qT0Eo3LiqiYNmzRgbF5viePpAeRE8T4yQubE0lbwqyKlDpie9MoYZZTsKP1qYlqe/WBObSUquPNneikPkZJWqGE6LS03d9vbzHNpYxjzLIQ5g0krw4f16c83bbhi/YxJVxs/WRMQd0pTaDGE5MQGDc3nWC3gnyGQeJM7rNS38fm7vX+R8C5VwC1mY+ZoI6+Jkbim5e/5uXu0keGeEMDLVAiHt5gpQkiB9MpLjLNCYo/NN9e0KKmQQqdiGbebDREDBiF2IgvCaX/46/j0MRgVQEwsB7rX5qlaiDQ1ChxjLKtqVMClphR/sVBSWmNoZZREeNoOoG1BAmkJp6gFyVyti8cbM7tl0X6etbI4RiT7uI+VcKlqFft9hhZN77TbasFwEsIhksrvNI6dxCtCckchcoF0YwYVG/shtq0tmAwAxLZytW4kRcjKeSVcTXgv8XtlQhtXFbikh8rcGjRj61WcaK2gm8TnYXfX+b71BJfKE74GtqG/Raqi0V/eX+H5oj1j7SgxMuut2uiosfPXcOpDHGlRO1jtCTIDQKLMNLwqMGnzhI3o91cHhUl3twcm0BTL713NM9laVmlAgdbsW/S3iXkWCfli0W3xMhGD46I9k89sjDtuFfqSYWeEjujSr7Q15/BFz/ffq9BQGbuK7Z1QVTk6jf2ZK+x3phlz2UQw5rh5P8dE2FqZlAkU1O61r3OaXc9xAeZVr5oolL2PQQuhWzWKTH4PO3LpjzPUYqbAoTgZXJD+i0QD/M4vj59ZkCDpQOM5KWzzVRL0sNPXvfaRjCoiv0tEflJE/pPy2adF5I+IyF/0nz+vfPddYsdY/LCI/OMf9fzaaY6xlqVcfmq5jItbFnl/Ztg99kn5MoXDJGFW5JH4J51bbsROEQy/bzgsVCcs2w9rMdOUprTVMtxTmZIMuY+5OmFsThQ6BflfQ/MyIdF5/6dqsT8FEwOwvL/OEdFLFQQKwVCzzziHZHJBqNC08er3kv2vXtQUeAy1JXNnTvK+jvb3GMMZOO39hW7K7yISMBSSz14cfkQvTgVTbLvZDDpw77gK5jB4HWsDXdYn/ATqYZbJNWC/KgpYBei+Hu01u2feRKP+qwC+bfvsOwH8UVX9HIA/6n9DRH4ZrOLg3+P3/HYRebm0Wm1FAWUdJPubNqgiFx1FMvLvqSwHoqHlFExGMFtsNsEQWxz1xZlN7G/vSrUp+MyI/bkNJ9S8RUvNkmpn67g7fDbtQWTgmrVWoaO0V7dzqRGg7uUukp/wPMI/5RmsFLhK9fQu955a3Xe1A8jQzPTN2Eb3FYZmOM2nPAQin12dQrtzsDoCGQ+nEKtEQZhc8FPML+fgRvAR99d5KBo1Pt9QzK0AXVEHN9KHycL7mNAyvX9e2sPW0xiYTChVeEwKx0px99tHMqqq/ocAvrh9/O0Avtd//14Av7Z8/vtU9ZWq/iiAH4Edb/GRjYvee0c7OtCbMZIzFTWYNgC9LcxS41oh/Qkv/XtOakwKNS0JU9I55eM2orQvoU0wUAQEqdMe5pu47VkDYv/UflILDwDaGobCdnVIM7aPjQeV0QRzeMW/KfVVK0MqmaaFR1E8rijM4Z3mAVUmzqBCzhgBklAqmsl0QkgHpG+wtkGkI72jq6aphFk1ZoX7WXb0Vquo5meNwsT/se+V2aYAwy85kUJ2OmqZETqx+yq9wFGYUoj7uhMOt55OxQnBhGAozHlWwop0Zo1SmidNlIoSUzitAuq2/Z06k/5uVf1xH+yPi8h/wz//egD/cbnuC/7ZTZNypMVnP/tZNGmx8x5IaNru5IqWFQI37LIMpiClNjVqhBk2qen9AGv81s+MniWep/AkhJklN+26TG6wfm+QrIwm6r36YjM7ac5p5UKRzLEM13/a/Y1sVFGgaSQUaR+mQd5LgkizKxmzhiwSJSRaiClzgWJ7wxNO1rTEyug2T5umX2hSUWOVY0z0nrWPchY1iRoGWwUSG96lChQwLprjquZV1fDRiyKgc9RJH0vIqBjJEbudiQ5yjDs0X30Mazz+vgkX1774zd9ZuycW7r5dVb9HVb9FVb/l53/6M2FPcHEtZa/HQ4N5ykOltfrAoKa4r9gtxFZaDPbQXZGIv72DxH4HLoUDAHBtC9P4gti5Ue8V5KI6N6+L7ZrAEISaHSyyoAptghMT2gDpjYm8YZ4OoMB6z0By5mXIKCHdLETVymQktLSdMwMinpLHeRAsBGdCiIS2Jm3wWdTidAgROicsT62oMUm5DvUn95JSU0JpgxJ8+3xLQWSOTFVyXYMuKjoppkmYUC01tRw9TLLK0JU+BFmChj6IZNycp9WPsQuwtf2datSfEJGvc236dQB+0j//8o6yKC0mj9rEJVn9XgrRk6mo7YBkyFro2CkuoW59H1I4sKKBiCV/B5zi9dgWpcysAp4HnMQW19Bm8gsdFd2MSZFQTLFtg2PRrgKRuN8TjFkugMM1y2YbEkKudnNuXiYD0x7P+KuiNc8E6g3zOrmRp8SUEYy3IwI6shISMy2Q81WhMRHFtosKKSSTkeJ1PreeRbWtD9eR9a2keFpvhDGFQkFx4mtRr1tqONlFifyEsfcUioAszjDmLdsrWYLmZb35d6pRvx/Ab/DffwOAf6t8/h0i8oHYcRafA/AnPuphiuLc8Ymv57kwYVyd8IMRNpsztCYQBZQrTAbWPZNk9gB65TsudjiokBJdtntvQiKFCyq7N0mfbW2RmCGSzrDtX6PQ4fUcD+30SkAB3RHvC0ecq5XW+polY7VUws6aXpHQYK0zMdIeC2aX1JQVTlZI529YBFmahbkWTToAs+HPqWHLo5kNzDWKtZD1XbH7pa5TXV+nkylW7YO0RvrgugbNkRHrejqzKtFZQVboVrXi1IlTp9mvauM4x9icfYUGY0wvh2c+UqOKyO8F8K0Afr6IfAHA/wHAdwP4/SLyzwD4LwD8T3wQf15Efj+A/xTACeCfVdVx98HLSwqkbOtiV9xOJqN048T7xTeMK3eeAZHcoFy6EItdJp9OhnpS2sLIwEI8KJ8FlA6Nn3V3TetrEsIGrQUlk4fwC7mo0/tSIVr0ubXot5mx1Ttb1Wid+xzR6qnNn7S/TGjYjSRs2v6pOav9VTfVJ3OqIlIjx5ihTJlGyXUOr7Al1ALKjQ7U5J5VNh0JtNTG4Y+o68A5aGUdi4AnQ/Pz1rutW6GT8E+0LEXT3ASxOi8Gm7jzqfnuqu7ChNdTeL0ufsr2kYyqqr/uha9+1QvX/4sA/sWPfPN6U0zMAnXcUUSthWKTyvYTSG1C6Uh4u8OcygRVm5EZ6cCa+zYvkSKAEzovOFJWhiCT2rMbmDYmhTjqPFCrVo8oiaceTlVRBfsTsM2fxW1r0CT4YAaqXF3jnKudScZbY3/UoHUBuCm/9yOFbgi2FYLXYyQSGuaRGMzRFi1n/jCdlM/g5m347ikSftgXGaqrGpWn8dG8qehkF7j13KCgJQogEE7fZzYbSov5T2oQn08btwjAErJYqWFpD5GZRC13A5/mzBKLhBiFCaRIf3vMrZt714D1zNG4h55jQl2HzN03Rwe89L7WPmhhjtCkQDgzxD8L5qpQ+s5UVLNLyrtCo+aLk7EKigitX1ABCb9Vp5FS2KywPu1XKaGSTJcD0nllqMSuqcXPLZOo2qnJrCKC4+jLWrWWjipdhEKiHjIDnUfMoY5EBiQSqZUoQmj4M2ryf5gUfBf/LvfEnPgoKkTej9OswrwKVyLAWtKGGUksdZP2/f32GIxatdwmjRkD3TUlAC8vIgvDUqvGoU67tgtPpMaz6aUFcjEW+ebPCckudv7ofl9AQX9XpDCSYLBC5dCO/o4QIn5tK4hCvJ9xZEdLSFmfhfJzwnKYLaRkf++1h6HIDeZR0pSaa9UyqoiMotS+/I7lVrvXV94Ij7nBEIyhns1VtNA2/1wvrkXQgsCcQs283seWW9xbvxHWu2lR5yjmubVg2kozXNPdoVRNn0BIqGtbrpm5HqSJMe2Qa4WhrOmfvdQeIymf0rUsymI7lIULu69IXTJsfQ7gsITvKDBTigavNWUr8VUijEORnVkbYJUOUBbOg+mtQCP2hye7Ndo0G+HwGeE15DP9Z5wDu2kYoNhTPsZ6NGAQlCCdczM1a2w28K1ohoR5HEMtR1PjpdwskJ5M5iwDiCT23Fwfcg6L9GvpQ2jNyh8Icv2krH2F5BHi8v9HXq8LBrvN15pojGtYYHCdewPet6bFbmYI3LTXzHhbPMFAVL8wzeooEBqZUeta21jmtPOL6gHde3sYjQrAILAvemjDAuXU7cbFKVK0b9W4i0by+6NsBomAHtIKF6nNyueLZq6wtWrDotn3+xYhUCg3BAQ1adUk3mI8qut4ijAIIeUMfTM/pU/cSEBqrV5hfkInj3PuvQXj0pS9shRwdoXVp1o94gAia4otBVsJvWzammYEUUqdp/Ciq8NJh5Q0V/aiZvW5WROqoIa6Vv43M4wW3wCKt768Q5B0VwXCLjRV7XRzzk+YXC+0x2BUGIydY0Dn9F3w1oIYy6QAqclunsN7CyMGY3DyS4ysepJJ1HHw78zau1UZhCYtn1d7pxJ3kn8yy65pOZ4lgQMpcOq76lzwXQF7C3xmOKfaq/GeAtPs+/I7DFvOCei0EPSeGWavX2OxO9zMrVwpvG4YEsWDyjkvDEVhSzNGyzoqpwIazqQQCt5JOqIqM94T8pzbMHuKEAzFMWesBQUBmXG3iVHetbwPhl60ENIYA9frFef1inG+fKLbY0BfYLXjCHf4Oy8p4Qx+NiszUlMiJ63avkBCnF4k9GJNlcluu3by7ysc472ERHPO5SxMIKE87Uz+vRS69r5VbSJYY6z3hAOJJcI+fk0cduz9gl8/NJMqOC2LRi2alpOSIZs6Dbcpj7kEWY8qPKZKjzbr7foI1Qk6qi5uDqTKNJLJLOL9idO8/R1RUb8IIwrqypgVhXGu9Y4G5hosJgWwxujFzLGYPjJzoQtq/3EOTLXjQXROnGO4sM3Qzb32OIy6E/bm7SUDjTnNtU5mLQsbky4Z47uJn6HUTnqhH6MwQH0GtaaWd5CY2EcyKY+Ej/6TWJkJ5WOgpF5KhlJr3EEMAJa+kShC22zj4xwFwy/Fvwqx6xp7pMY35gKo/ap2rdcac6T2ZHULQvY424YCQLEIVUCs2kbKh3XMnG+klp1zYMQca2yqkGI707S48RNwDpHvu+ec0+3zmjkGp8dAXUWpMO+oMn71h6gq5jnsEO26YeCF9jiMijTmFwb1RuYNbUBCpjQr0ClLryBjkv6cPIc1WyVovj8kpvclbI4qofdGJhdZbSqskl2QSIAooRJEtXerNgzEsM1PEIqw9m32Yc7VoeL1BOLRUik15E3GXKPvEAAj3kvkUDfC89os7VmRACFmEQYlxxpFOHO8YSbA7boirLNf6/znpBQIWuZpP5Dq7nOwCkKgQHIUpKO60kbpd5gjpW/863qeuF6vmM9XnOcZjDzGy7lBD8WosSWtEDe9rJw43SYsJsAnrpVFJ2Oz9GZAZ2+VPhUwBheJIl66ve/mngJxQou6m5+SNrR+yTcOLUMtWAid444xVUzpz1qarI4rJTOXZwFpNnCjO1CqC2jCX3tODpalZrJCRbvpL2JMd+xAUIsW29QXr8L02LK49TsE05ZGCjg6cOioqpDezahuduRGzEdZs3tMVP0dFG7hc2h5TlCYY35dRURcp2qjLqisd8zrFWOM+HeeJ67Pzzivzqz3MR6AB2JUwhnMPGUrNCfShtgbpX9MXJF0ZIqAPSiSErjVdpt0DISGIoErLMaqocMeq9fzWXsG0yYA4jP2mxpX0067pwlmQRbRv2YF2rKCYW6/UhFos/ecc6Lr6rW1vgr05gjAW2ECcFtaxi4N+VhP49yeurOFCKX0acm+YoirjLWaGDXJANXpyHWQVcNjX+NCG5I3Bw2NgqIqLXGtKGQX+5Rr4e+rvgMiwaBFMun1ig8//BDPr14Fjb4TGrXie80P428pf0O1nLYtsLIZ9pVPm8fz1v2s1XasXlpKxMoEJNzqdYRfxxbOqp0Y+MxpRz/sWpnMGNK9MDtRQd07Wx0flQDg79Aypgr3FseMPzudRlr6m5owtJ4AmIispuyeia60QzMDSSnVfJqYj9sWhk3hGXNWVjlCXNtahCBAYUpg8ZQviEvT61u1nE3Z7sVO5pNyTbnAtk7OUnJ1E85ACteYgOI41KKA5vW082LVSuA8Pz/jej2xVqVc28Mwam3VDl20HyfU4VroPEXZ5lV1D9POijQHFg0VWqxowmDm0qddqspLfeRnAFBSExcIW94ZP4skr8+hhEZxzPA7Ra0JVBxArkUDxpHhkVCcz1WVW+IH+7bCtyRee1LzBPoc1kSTA4q1Nm8eJHw7n+WTBdHESQLF8RNCsCIm9yXUzQhVSAUlaJoYnGPSQ8wnn1vQVUVmsd4o6K4It0Ug+DOqbaswBCIA5jlwPa8Y48R5XnE9n3E+X/FSe5g4akh+Ne+hWX5WlhJIgplzFmiVycwBO9WOv+OkkSlpu9akARItCTj+5j/2xz+vcJcLFmmIQeRlPJoOghVa5rQvn+NOokXpi5R7dCOApe9+3SyEGYKlSHybIyy2qdTPNesj5XtNCxhUO5fKEVUP1kqFe9pidRYuDrQyH8z0qrRRmQY+V62saf08YO6+Npo5vlVLL8K3CHQ+L+a1IC2U+/a+VYQWjF4gB4XXGCfO6zM+/Ft/G89f+hJeag/BqCEZvVmtWWoAAbuZC+wEgyohTcpNZ8iq7RZ7pU6qf7Y7FqrnMfpXrgsvZYFAlcFDewXR+3cu9QUIpxMXnH1agvtOiNGXIqF3mzfGG4hDl8+BNW4Lh5/iFy1eWtSxAjypLCs21GSG7B/vJaPbWtZ86MocktvTNk0Z490ZDCv0XeYqepKJ8CvN3K5vnb8FWm/37c+SzUat81ppIAToJjDoN7D6wQPjvGKOZ8zzGS+1h2BUTkwkL2DLekFK3Cxo5jpWspK7KMLbZw8QNKvWa3+qeDI4yH3J1N446VaCZM1Yss7IsnALEuBPJh/wehJUFLtOAq5awwL6K0FXu5N9qBqgzk+FtAuBbsTKzyDipWRsXqdgCX3cOn1oM6+Cs9bgDdiM27+Vg/ffmafLaqe1z1ULBxSu/eL3/vsCT7dWUVH8A26uDV/EpmmrU4m27Hr2jf1cMtqqVt6Et5kGpEWFzgGBadmX2kMwKoBVUmnZWEtvYXUawOwhY1TbSCHQIr0UPLdUSRtK/UzHTOrrCm2rxprL5PpPaoAqMVEWFYUwkAwVb6MTbJP6dR4oUBbYVmywBVYVrbJo/HIvCSuEysztg7bDxis8Qpe+JPTN39sG222sXkt3JvPF+12e2ge3/Q1h5hfVbWoLainztUBMH18rzLAI1jq/ZT4WYSK522oZP7+/M9cLTRRBsM89tzpy/QyZDNTzc3ntu5HrW6AE7SP71aW2HxdRxeDKKAm3sCxTIRg6PsIRwlc2BOk7UdwkVvOpIsneQUO5wAsMuulJtlhgyUC/upbP/qakps1WEyRoa5Fxa+lL3d5VbXjxbV38boEvN/dRCclSd5gzkwn8WN5BmzevcUh9V+dh0TpV64lIeM7vrUf6JW7DKNVerPPAPnGe6Dysudp8zw6vdwFbzaMK4+NerKVzTCt7qqILxt4PqpD7c4MHYdSwowI+2j8Wgxpjxklscyh0FpjHZxTCYBNkho0tAhdS8/g7WHqbwdIkEGquOsEIbUfdnNM3N2Kvgme3w3gCWmcGTzD3SsaEYdWpFJ/7syGCXreZ7doCKdD2nTUhJJY9qIUo5y3D21e6XZ/fV+KN0E55SpO22OUcF3+v3tiw24umoZeXmphJMrE+LlAqNF4qT/o1C5z266ptGY6qjfkXjVs9zkgTZDdJQpjSR0HTwemx9eax6BeEGN6AUUXkF4nIfyAinxeRPy8iv8k/f2vHWlQpmrYK4m+l68c1jRStprSPCrEDmxNDEDV1SLmuYJ0fQz6HBjdtZc4Sak1m9Fgf1iRtEkwlNFVbTBIOnTQxtgqtZ4YVTJunHRYQC1iS0isRkUEXwi2QioTZw7y4TxQLa0oaB2mrqgvR1YtZt7PZu29hLgm4Ml4Qd51Hn5vq4KoIpMLI6C0hc/lJmogKHZtAWuagvA8oDqnCdEGf5XtFKbRdY8BESxUCOzowMKZonQXU7J39eDla+iYa9QTwv1PV/y6A/z6Af1bs6Iq3eqzFAlWQcDQgAQfKynk+z1EBnh/GAwhT6B3MCbT3JPzVpZxHwxwwra0hJ6HxO58xQ2PwzWGvoefUUvOrJY1LKY8bwon9UwqekqBNZsZ6T0h/BtKBIGLGT+u1JJyCU5eaUGm/lbUotgWZVKQF3KWG1fAJVPiZQo/ra7bg6gWGKmSqlT0tfam2fU0WWDR2mRctPys8rt5X/l21aL5yNV+qEymUR5mnKLPjbZa1CMHq91eBIoClOroPJdEEXts+klFV9cdV9U/77z8N4POw6vffjrd0rEXVJBxMNEHsCeThsSGlKoQCidA1o0gSU6lYp4qodGinhvnZmi2ZJvYgQkMw0GECTQ0GzQXUWBCLnVocMbUN4NqQYy4xxrrkdePzbnNJYbognhLPU7UUtcXreC+WSe3EUI6/w7QOhRH7qVGEm+/hl7EtS0vtIs43kAXINmaNGCTXKQ3y0FqVDnYHD212UKvyn72kCOMinLExU2HCZV5QNoeUfizOr+WWhO47kqEWb73n/Pv8SBNcLgd6azeb6e+1L8tGFZFvAPDLAfwAtmMtANRjLX6s3Hb3WAsR+Y0i8oMi8oNf/KmfCsiSWtC9ugE7Q22GRrTvaqpXPB10MPifK/MXmyozfRDlMgzezfBiBtHCJpgwhsQZzoiwQcQZbh1zkPimEcNhVBB+nPAGs2UD9sbDDNpV7dA2qJyzgSDcxSlSBAg0kwDY/6o5s2B0qtl6hmtlyICsWEM11NL1ekFqWS0oYrnHx8excKwCFLtPlnFWAVQZNOakjLVq3d7W/ajLMzQdWvF9/Zxwmu+eKzLqvRuduE3avL4T52J11q3tjRlVRH42gH8DwG9W1b/5ukvvfHZjEGk50uIzn/nMrX1WZBptuznWighRcBlF4xSNEJIXpj1N+yho59LNUZmTnY1jHjSvpbS25yUD10LWJETeF88DbghjrxqQNY01iDF24pAJSAjlOdQAtJtqdQemQu52Igt6Va1sm5lzqSyUkPqNAjLrJSV0rmEZnhpAwcb14Vr64/IarEwDJHQ0gZjaT5E2N+c1NF3RyFXDtl3T+bWMhZIGllTTXcMVBqwatjqLQpOX59TrF8FSUEQegvUy/n0jRhWRC4xJf7eq/gH/+CfEjrOAvIVjLZqnDM4zT+aOsZSFZBhmwM5nUeFZnpleqEgmrgIginm5FM8QTGqJMUYwWS1IFdBS1bewUSC4Zp9AHLsXdp2CxyRG+bwJiH/WWncCAKzE1oSfsGz3uw3ZKnMVgosSILBEhUWyI4ma2j5sV7byHBInTzJTgR2GLSUpQ2k/80xVt9XFrhvzhGJAWknEnzOSR5rY80+dcR7MUMUQtYMoiAqcaZo74ULIATHeveZyDa0IMvxS56leH2YEVgapZspyD7fZ+d+1tteuSUNYbDC82uZSnsWStPKVMKrY3b8TwOdV9beVr74fb+1YizwuHUIZB0C1lO5ooQlXYiwMLSv04QKm9Ert2cT2qJLAaS8RPtop2oTbhGxwhVwndM0Rzfe7FgVttqJJFeBp6jVntGo+joGbCirUQiFaEmO1gWgbcd5qYviekkitEEQjPGe2HiKVhJn+Ar56tyskhCX7ZEKihUCsO5KWkxFKIgbR0CgVJhfnWWE0ji20MtbwWJ1bbpXj+EOLYvV51KM6qyAIzKOVXjeTYtOqda5VZ9is6eHGTamXvb3J7plfCeB/DuD/LSJ/1j/73+MtHmtBJgm4WiBRpGWNuV7r3ycJFmSj1S7L3NUGCUZpTYpks9CLeupqnlNqcDtSFENqlwUAXEJg+U6khEzK+Z4LtAPWZyBh8P154rtM/8Z1RTNEN+o2MtXwAlctuySZFGZWenjBsaR2BBiqSaJMJ5HBYm47rNvJJn0MLUNw0erY+f47wig+I8wsGUzQUhm/wOgmAvVjKThHfGZl1kBXXNcCu+PnnXXgdVWr7tUtK+MaquCZPib0jqPjKhKnEN5rb3KkxX+ETWaW9laOtRD/361XLZ02JP4xMhmfXtlJCS45lap2DigZlTZOk3TyBH95JCPijUiJbAcJmdRjvSYpfVNV6BhorbvN64TLFEXX4uwjFFYxT3OvovjikjHYan2mmFv/x/GQeKM495ZxFMSaa2PfyeqI4ndcAzuvdS3NWs0ArocRG2PNgMhtBQiWZrFzVdd31ZPzoBrbx8J2LD8rlI150VxbzhnK+tG+j/2mrfRvMwPIsINxWBhNHJ4Ztewb5pwg0Qn/XrY3cs3KOiQ9pwPpxobe2kPsR1X/HwuDqR8zyGLONhGWTB8a0LXm0NzrONxDS20IFYhoQk/YcYUWozWSJ3qrjgRWLE86yAkcc6I7UwrMAUQYawTg9yD/Dq2k8UZfVw3pa5RHu0oAzNCaQQiV0Goq4SxnuQAJu5BEwrFVYgfWmlJ8jkiDzpFwH3QspcCsTie73o9uZK5BMarmyCLZFFoQ4VlKqQWqQHqNgAghs31+VxgVgVWvpyYk/Q1HGfV9ZKYoObr3x/tsY5rxzGpeEOaHXTyGI8WgfNB+f1kfPkgKIeD2Q9gf7RbWgkROCKLrwgJGHUIbyz6n7UvmhjP6mNOuEcE5zPFkjqK0m4bvxdQCfaU1DHeoDM1kilxYi/UCSEmOVRLT9mUIpLUecNGiUka+VVPy53THTCW+2K1T4VdhYjJvTVfMivNIghbBPAdE1YSRpqDabbBgBlWc19PHiDAnOH5w3Vx9ixYfA+FkgTh1TPuxJPWzymQxjDIePpMF2ysElcJIHFfU2opHpDlB5LEIA/+s1n5e3uvzXqsUnvSJBHpqsfNIVXEcbyE883G2nAQCu5Q0lMR2XWHMmTZKxut4todpsjUNTAJjU2kpsg4v32cErbHqzBRCECwWQqHtxfcwhzhES9GuhOxBKBE6yhgx08qqDUxmNyZwIaYIoqCQA99a+ifFobO8225cqrznu7BqzyKoOKbY7qXMdV1DK/F30chAJkbEfIssJkdFCfeYMfJvtVTtIwNV+Lm9owqlxcQKGsvTCirzVoqsJX0476F9/XF0WFEDVy/7Uj4WdlgWC8zxuMiX2kMwapViRYCCDol7UMVc97RH8jQskdQWdpiQeCYgEwrUr+n+HinxTCxZS7mgdLwYoUbxaddQQxXnHLiO0/rm8DFhux9Rr4i+ABWiYcGAJIoKeW1+Uuveczgtwf+iCUjUtZJ8JTa4xk6iqxCU3c04Ku8LM8Uzsqzv5dQ1JNRHeWdtdGzZOiaT1u1u9wAhmWPRdnz+Nr+R+1uurwOsnmReo+UdMb+Ev1JOHwQWIVEFSVxf+lKFJbUpnUqvOSPqMRiVTesZkSHJTV2pa8TpGo9xtOqJpeabJc4ZzOiQeUlUiM3nRZo7YVYCMMamV5Oae0ZCtd2z2lfDTz2D5IK4OymEAE8+Q7lfvbpFJJL7jKSHtxB/lWoFStKjS0YnIXGMMTY6WAifq+AoY6EA9MdEP6S3RQusQmJlLxJ+JrMnhK47jwJeUsC5bbwzTYypMNfu0ZVybZ2LELm79ttg9bIjqmjhe1AX5XmLjVzCY0x3rNsMzS9j4279wTUqQImzEnxnjqTANjhP9wo2CbhFTYogJLcTKeH9uYARh3h4oGbhVC3D1EHn7yBeBvAjxKKpIcJBAjq6ZiGa2ge/TgFM2nSsQFEJ0YVDhcgCqHrOrShqbi2lPIl82fxevcmEmptW5VBRPqtweP08f5/hdKsbJSggtvVtAhwdKpasEmaJZpJGaPxZql9U+Mk++DNj+1iBszUuGjCXAqEwfL1Oy+9kLMY5Y9BVYwPrsZukkerg47oRRjNu2hoUYmpD3fPb+k3O994ehlEDXgGgmmIhLc7PcniU1K1sprEYAgmG1+1n2HkpCK2SgqfFFdhsjOIJ02R8Sj6pyez2ftpyyfQ5NpbdIMyyAazjZ8YPkcQoMDVsL8lwDe2nMcYisauWvSFQJ7YabK+Olbr5eoFplq4E2hM0UaLv0JgjvoPIh5pMkOVt4n0lFXAnhqr19rbsXPE5qlA/EIi/u5XEeM5hhKyKaVHjsAG/y/vjKA3N7YSxLlzbmLNcg9DwbUUfrXf03nFcLoAIjtdsc3uI8AxAaOKhFHGYiYSkAHA5jtiR4Uot4Slc09KzI3BHiTl3OGGj5KSG+z0C+Qmbq3NkupYkGVC7ViEChUdX0lqDl/wWNDuUyp8l/ll1tqyOhFXThXQWieM7FgL2z0h0dIwsdZ9QiIZvqMSEwhiK5XOGn0yQ5MkFdrRixrrjrJneMR3kGPHPWFc6ZJaq92U8i3Yrn99l2qoxcV/DCpHKHfQQNmYVhBoLEuOsFRqWdxeNDyBqSFMYVuFX+0C6UwGkidOk1ax6qT0Mo4KKPxxEmp87I04ynXMiA+1Vm0EQ24i4uLoQPhd3oW3YLrcGwO1c1dXWQy5kwiaPo+rMbW/lTVOnQ2Vj6N5byU4iPKV2Yt0fGJGIe1yBOEuHhENmrAdMVUdbJRBVtc3isnpXGebZGblJWwiZ6YBBZP6OXrQD53uBzQKA6IT3FG1KbRbMioSrQez+WU16D424n5hX+6yZVXXDYKXxe9JJmAdlPirD3njsJW1bcF0KjYQDiWNlRphr04qQUOzWe+1hGJVzGl5SeDyVtidQCDq1UcyrQ1UG0xnPrDYvc1iZ8FAXyDQrHVQNCE8wn0EGy1ifQiHTNGSDS1iYpAzpqLUKgf9gvzU1B3OSpw7LairQPuwXzXCJPUMSUmmKo0XS82fx6sb2tkJICkcgtNuZRsmJN2Ua6zNi3uiUW1sIFP97OfqD/d20nQm7DhPCWBhBfB7Cbt1SJHl/1cQ8CjHGTTTh9/QyB/X7WDogn7EMTpd5jxPipNBG6Z+qZhZU0fjdN5C3fnj66oPbqAHvAjQyoZsBb7+uagBZ6wMZAapLNaDuU60asYk7CYQ7TlID5fU1tLDZY243M9wRFQu40JqxVv6LnBOHgY2q0t9BB9bwrBVFOosi4yh2BRXpDW48TyINm1CyOHWdozAVmCRR1oAaATBNz7Q/MiTq++3PmNM617QV/ZLVe8p58H7wGvvbC69z/minqyeohPD080X5PimhkpqA4I12JXzcnKPqgIpnkZaQQrR+H+mLsfMqx1kFD/sV9rRsJ/v5M2y7YULhl9pDaNSQYiKO2QsDToVKxvYIzxwhOlxNGMmZq4F7PgeSUDOJL22VMTWYGAtqykN8qiOFQoQAzw44spstrdU81lxEUzwkTjqxPC4rSRhVBVMrirTYmNA6s4qKJkKOxca7ERQlfSHoJWlBsl8o89+aYAy+Q8wuFasKqaJoSEEAQcDHdUO/hhaLSS8wNr4HhSQJvhzErG7ftyTySPp3DUYBFIc4u9YLW3NOgLuVZlZ09IlbBHrs7S30sdjL/i/KlDpTmhNtrbCR6y8LI3d3JvXecblccH0XjrSgbVLhkoCxpZTRpnHIpDZlJHbVlUFzmxadPzDGEj4vJ29SEy9awzOcpnlk04OM0Kr2b8a2uPMcoQW4VYzpikO9hm7x/NL7yTnI9+ti+9S0RI2+xmOWDeJAZiLVfZVVY0ghbjbGhTPsVUMwEv2lTQogqmDQyce5Z79C+HB8vsa7JnMY4teXrX/byXC1CgSZoyIBlPeROZdoAVZkxn9wpub2ttzOlx725dllHOx/xEmpuX3M1XlJoXg5DggyBCmt4elywUvtITSqSXhNKBETrxC1BObcbaJmK4FEa0+gLRlV21WL9tRFY3Fig/gm4bJ3SPN9jI8CuSkdart4ph/vTi3M0M95UkuKSfAgChKdX4uEjZah04hBYTX+FTpLBQkXJJhAO/JUMXFvo9mRwskp93CiV29xQH6/NvJSG7wsq82b8vfSB0JizoeFLnJXiA/DX0sBJ5hIxgotpxlTpYbOBP4Zwqs54tKZ6Imnw1vXnGZoMyKTP4RmAFKYDTXfAqF26xLHTVq4riSC+KowXbp+TmasDryqfSkotIyb2p7ZazoU5/ngp7kZslljeCb9HZ6KLBBjve6eLer3tF4qAUhIvfgzinBpOHPUVeZSwiVgmfpm89SaOqY7VrZ8WrEc4jnhoQv73Uzv1R4h43IyqgAS70NrRqRdmgfbNUJYSRSyQO2AopwfzhkqzNZgFBdH4TRq3WFkz1Kny7uQxBe/gxpI3RQgQdu7aJ9PQSYMcNzg6XMKjIkmmSbKUSg3G4Uq9B+uvgNFSMzojZ1eaUwBHK0HakK9p9DVQpNwp1/RyjGPqjdHfYYGr+Mta21O047jeHCNCkl7Lokmlsa2TiUdgytUiQT5MRBwdQQBEi4ur3WiaW09FW7OGe8xRjHGPMdpG8lL3qwlhjO2OsAq6OJHPDS36aC2IwVCN/zmuYULK57v4gKDElsBYKp5k1UBP2CJk1LhXiaTO6R0O46ebABZgbAwKThXcI3lewWq0663FtsAwyNNHE7ko2sVRX6vSOEY2+EgfuI4zRgXl2q+idjrWvoIJAKh9oU/m5ln9VqaFwwpWZ63PdSuV19DP/O1Z7yXc1e3AzrYW4WUr1Nl2Lo2u3+g924CF8BxHDiOA+f1ZRv1MRgVtk+URL0azoKoITTJbOTaVRNzEin5zN6iM0DLPdmmwx8RSsqaZeRa1O3OMQZ0mB0yzhNjnL4/teHUM4SLOQhs+1pXt616R7+IaVM1bREd8vfZ1rKZAiTCUIx/MimD9JO2nMF/95DS+baVOQlJpYrzPIOAuHl78UoqPc/UhCZchmfsLPYsCkNyXmuivlIo5Z7L6QUAoqTqcDt8wnYrtolzFAQlMEFXbHdWqtQpkUVGKM81bj7ezJ0WzKFJQj7EGk2gAGXfKbw4z9V3EFRKJo3Mtm1OqG0pIP07ht6YPfVSexhGJbES9oUGFMc0qLA37SUyK5m4sjkZNwogl72pQNm2FH1IeByM6s4I1WmnQo8rWMtnuBd2nGfYd9Ia5BQ8XS5obWDMjqMfaM0EzeXSMZFJ/bHA0R8Or9bTTei/eGgZOlkMUfjCSx4g7PPIdymweDyXVDmpzjVPMhGPMTsB1/DSDOZKhqVKq/A4rTzrF985Bh1RqTX1nO6cLcdfQHJ+KBiKB59jrHSUa2qyvlZwBLDMqekDRhRSqJFByaSJzjyziJA2ydhfy3V1/0nNHUZ66o1BCyp5oX2k11dEPiUif0JE/pzYkRb/R//8rR1pETPiU8GJU6le0Nztsi4EJVParqw3a7B1rg4LZ3yBn1FSiS4cUX63ugYZI45uH2P4CdFXnOOK6/UVns9nnOczruczrtdXmNO+e3V9hfN6eszV4PL1el1S8kgAEcqpRBHQi8gBYZumoNZlTjL5QExrayKLmAMkxI2/JZ1srMQYdp3PG/1U1DJh1xdBE86fWZjfnzEmvd7GoCe95ufA9Xr1f69wnifO0767ntfwCdCD6xHlsr0xY6UUsilsDcXwdJgZ+4tdYBvC93lIjzrK2CfymvCI+zRXZxyvV851SYZpSMcUqEnd49t7R28Nx2t2z7yJRn0F4B9R1b8lVjb0PxKR/xeAfxp2pMV3i8h3wo60+K2yHmnxCwH8eyLyS/R1Bc5c9QV0bIR+aWtNEoSWins+cSRscPFKcns6DUqObiix6qUDogq/CHTmbnzAUu7IpOfpzOefkTAIZYywqLWuXsmB3xkk7TjSpEFugE8EkUy7jJf2EsisJBIN2JyOH8vCWhQuCYuEWFBF1UJkRvajtupYodOK9l5ZmNV/AETm15wT53mCZUdjr6yvUYW6UYQOEnBbp0aeMWlCBR4bXrqwoBUUu/kOAfocrnSzlFHxiZxzovV0EC2ozNsAIL3hjPmZkeXEvkmZc57U8FJ7k+JmCuBv+Z8X/6ewoyu+1T//XgB/DMBvRTnSAsCPigiPtPjjL75EcoeJueGTAcM2SEVxo1GhhLjVDi0LxEUIm8kuSo2RGmxxJuk0g3AO6BjQ88Q4B86rMes5BuZ5QrSkJopAuhHUcblYvWLVIgwc8s2J7imSrDlsrzSHSOO8oGX4yJlTFUBDzlfPqg+qRCW1GkEKrrKu5jxBfh7w0xlFfB6bNIyX5KxQeEx0ZKUMCq+EwZahNefEPE/Af5rwc8RR6LS1bllkveHpYkKntW4VMCCYKsHE4Q7TTIxhfjAhdi/j4/qDwtS3DQqQxcOLoKpzxvEFIUnSaaQSelsS8qVGElwwl903hvC+wlxfsUOe/hSA/zaA/7uq/oCILEdaiEg90uI/Lre/eKQFgN8IAF//9V8fMUoRy0wylJuBaUrssCvnLfHRhlLQeZGTqlpjiBa6GeeZAqA6PnxjOhfMpP/AOQbOYQx6vV59T+vEOM1bZ1qzA2q79T8AcOmXYMxDJ6A9oaj3pYYF1AnavrGx0Cst4lAuuus5sYrClOVe1zb2t6btCUS1vcVraRPA9bECZafDbsg639Q8kZMr63vLeNLhNN18OHG9nj6vpl3HHLgyFKTmWOvNsnagwNMTDB1oCjTFjH3J0wWWhr1rtuEYXqlSbTN/O3oITqOnAQIZVQ+l0RyisCoIxkDs5KTHbqaYu4JG9lrAJ+krpjoVR2tbcfStvRGjOmz9ZhH5uQD+oIj8va+5/CVssT/zewB8DwB809/3TeFay6yXtM3IwJXRIJqwR9Mtb8/O/FiU8pUiWQHCbIu4g/RpWUYeKx1jQNwuOsdpC3+eGM9X07Ln1d9jUFcnMMWJRwVDrjgvA30MXC5Pvo6egwvfDRM2C4WPL66jCrgmrumRiPvy97XGUUxfzBOhOBcninJTDfsi5eFLw+sgpwOOc1rQufdVQnjqdsZnmhbrP2PWZ1zPE8/n1dZxDJy+VrN1jNYtdNEaxtXn5+nimsjij0QMTcRMHlZUUGPso2PZZYQxw58xAcC92Zx7u9Xt3nGb2XbyZADC40LgiozJ8++YBwCCDlXmc5tQYLmgMTV8K/fal+X1VdW/ISJ/DHac4k+IyNe5Nv3KjrQQBBETSplUok1RC0TV4DvpmrBvQpWxsuZJ7vGK1FyScNeID2GP0Okzp0aivJ3M5mmCHp6hkygOk3ICP7rpDjEMGSlwtagX+9dE7FgO74NNMtxRUmBcQxFC6kxo38XxGq4V6FAx55SutmJow7R9p+e8VjhGDRJCLYSAc38REmZ+KSZmaL/QwMjfJ6bD3BPPr55xnieuz+aU4+fjesVUxXHYCdxPTx9ABHj14SvoB4qmByB2CppKK/0qW/Mms4WcQcUYbaKepKfhp5hggXSJOeFU3bVny0fUnOLoJIQfCg3H3BcT1GmVfQeQjs0X2pt4fX+Ba1KIyNcA+EcB/AW81SMtEIwXto2PaM1xzesA1k8ltJ3LXspRoCvhMK/zkQEQZxBxzWsQ2ZzEBjfnSGfH9dkJ7HrFGCegFmNlitgSOxNCn7SNU0DkmLmbhxucO7Vt2Wxg0J9jQcAyIKtQhCDSDDtYpUakqT0R0LE6QtjnBapJbgaPTdDOtHHMiK8JxGOcYSqm5oTDSQq7yA3251yfn/HqZz7E84cf4vrqGddXr/Dhl76E5w8/xIdf+hJe/cyH+PDVh3j16lUw9MkYdjnFgDIoqvqFCVFMAPbNNWhoWVLDgtpoNpC+Kpznc5NOOIdpaq3JOFQuzRNeLIOroUl3um1fsUb9OgDf63ZqA/D7VfUPicgfx1s60oLShgSS85RxwnCKYD2hzBwA3YnQ7tcxHU6WfFhCRy6GYUEwwydsU98qF97IEkIYnuignvjgHG3v8lhY6x2tG2TjSePGBCxlksOmB5e/2y+5sLEzKAjG56M43pIYiDyK9cv9vCJua6V2RL1r5l7KmhvLOeZ6sJ+c92DWOTFEg1kz1JPjCkEJj1VOE3LPz88Y54nrKwvLkJj1cmCO01Klm+DaxJIJwoxpZV8rIp3y1I1pgUAcYXfC5nDM6RU3/L9gspjAmKWq7Mj36vuDq+CscwSfo1bsaHbFUMzw4uQ8j/fl9iZe3x+CnYm6f/5TeEtHWlRba4lT+ee1cDSloXCGYqYQcAdCQvKyLVBAJWDY4kVGriEdLXMMzNP+jdO8k9fnZ5zPz8a4agtsJ5dl6IWeSnOENBy946nbT4ZuWi/7Dwt+rMkBhJeh+QqUZDkUhivSYZRMuyQhAcBS3TEhGZ+fIazULHxeaEdoamAtyEAVsmmC6CtKMjq2xAzxPgPAOTCvJgDHuGKKQM+O43LglV/emuA4TshxYJ4nTheKdIiZb1Gc6LEKE9vV4QIrw3Us3dOabdtLTiHKoLmUGpleXIVV7Fi89W569CYQtDR7ZtrMrJTZRKDSga44TyvHcs4HT8qvkjfhSRJNVjDgNbpcEwcKcwcJqgRnIrTbXJgxsbXsSs3dnWMa0ZwD59Xg7jxP6Onxvjk8Jsi4n3Wr9waBJVdfLk+Rw9l7t2LLvZ796jYySn81GVORGmH3ClfPaoZfChNwInmtOCG1FrYzvU71OHs+T1UNMWhqo0hgSHXjPyRtPCTUXbKSXPioL85kLWBWZPT3jeszWNZm4BV0PgHAkhTQIJBPCUS7eXGnYEpDF6SAnoTDPscz11+Rzkr2a45ZlIJ6LFbScZZFcGLMWe0i2Nr/L7H5gkonBEMxBwAzW66e9YYCm++1x2BUYHGDJymk/SnhzowvFwLnxmZF2qAk4iWkkLenplDz3EKnJzqcuJ7Pdt6nWpKD5fUOr/qX0pfxQ54gfTmecLlc0A+vMNePSNLPbWzsAaJf/Ih9jfgbEUaABodzLW0j5fwRxgliO1jMkhJt5Js5p4SvQHXbIRLzk2ClrIMTciSwb+UuCXj8hZkhlhfUU9ltHSbO8wr1nSlco947xvOB8zhM2Fw7pHeMc9gJ4T1PhjeNmjFlICF6Oh7LBg1q3JgTWjOZQsnnBQ0tW3iyxW4tjr0V9FboG43rbJUHdV5w9iteUyj/gRgVqz0TDqBZBuzXRNUGTWLb80ADupVtaqHIQu0mk6pOjGnxUvvb/l2fr7g+v8I4T0z3UFIzMFe3t47LceDydMHluKA9XXBcntCPDrl0dNqrtFVRNWFZncqw9AgHc7FW1ARrSsTcOSPTITSVWmWbW4ebDCEs95ffOdfVng1h4Ta5bkTKQm5AOsKW2Cpsvk6ciRqKjc3SOyACoubVgZ95/hDt6QlyfQYg6MeTefSPietp56dyi1iTAZEjeCg91exJkgirVM6ZIZXwiYDacjWVkg4rn6ZCoSmmqn6iX5lXF5iYRDC57a31VjZq3LbHqPAgtKk8sZ7SmTBNyaDr4rJ+EUBvH50vxUNabDDaI3lgrYNPZ/Q5Bs5xxTlPSxW8Prs2paRnBQqDPlCg98P+Ee5ennA5ntD7geN4wuXygSfoS0h1cehJfahqZUzVO0VoXs/ACWgpCTGr658/w+dGYUSCK89pnjiAwpDRH1W3aVOr1+Ms7QnOgI1rloCemqOqoOoNzU3w5TPXrHkUiAnjc06MYWvx6vlDz7f2WLb/ozc6TAH4Rm1HFVHTiozvZoUScrvVEIw483tqvWqr09ewkG9YEhrorx68HXA3lOua9ZSm06PvntHVDnNDKrWeABZT3aW/a5j43Ix4W5RcBNOqTgCk5FwNMIah6kdRzAHMiXE98fz8jHOcxkiqHuOghGw4+gVPTx/g6Bdcjg/Qjwt6/wBHP3DpF3Q5QvPSW8nkdse6Bu1G1vOZHgtMfURBvUL/GurhZ9WWomYQMqtSCExIT5Xgsx0QOnaaoMRRjfLdvhwhMIiBIjQmjgJIzGo5vhXVCLxcptueo4k5YDaGwJwOv0/M51eYx6dwlWf0fnhB7Y4TgiZPOOWMIyFaB+ygcj+HgMkkMW+c+jxkOjpb//R5WT3lVBJ5S43J089sY8gUQfoBpu+9pSzls3pr6K85ze0xGBUu/TxrhG5sNpNsKTVp69QANT8PWwoIbyAJmIw7hse0kGGO6dJbAEtmOC1zZgwLxJN52R8RwdOTwdvjOHC5XCAOwaRlwSqTktyE7ChgCiCK3o8cj9toBhFlIY6pE62MaQ+Z2ARiy2xiYkg6zyKBxDXWclCy7lKeTGPvmTrDVm29B3RD2HQlt7UecYEUAL01TLGc1n6Kb+Q2JxeaQISedGDqANRDZ6filX6I3j6IVEHpRtj9OOyaceKQA6qKS7ssgiE2q8PmIMRkgbOqa9lTashbOuSWSqIijQyuivSqicaJECD2o84x/M5EWKSHe+1hGDVq8IS4c2KRsqOiSi3Jgs7UkiIJyyy2XxLyw0MnYV/kligL+WAqxvU0D+Q5MK5nJJBjOMwmbG481t1S2lrvkKOjX47UsofXqO3W7+6pb3CJyoUkbGfJFUIlCy04JCRUqxrTnR1GBKmR9vpFZAazElKMxybrxZzQQCFpPiDVLlLb8lwganwyQVRcQGV4hmissWh4Pw7onLg+m4bs/cB5XsGV1DkANPQuOM9XXh7mijmuOM8D/TjQhnmE55g4LkdkHrXmdCD0s0swUfWah+POx7g6m1B6jVj7POPUGV1S0KWt6/M1tdAtBYiZT9OvTRv9fnsYRk3JZ3G5kOCN0r8E/yntUDSB27JJoES25XBkoj3HHfyOmTLX0x1H48Q5TvgDLc43Txy942Sq4HHg8P2ExrAHDtegl8sFxyW3sTFJgX1gVCJKkwIJGynEpXhENb+3wDk1ezlSAwimZGJ68GRokIRiTdIOHWNkpT1BTYF2RkYIRyYL0GNOchxzRmCfSR5ZXtUTKcSIvHvOcRQrU0Xrh8VjtQFKpGMx8ebPgpwY/Yrr8zP60wV9XHC9Xo1xBJAjkYioYKidQRq2ZRFK4dipc1fQAa+jo4ktofm6NjX2DOVpgy2eFTTOiIG/KBXHy0wKPAyjkkE9bqisPp6S24jpTngjmO82w8QtjKKpqaWN4E7XKkwTHGNARSxR3NMF57haojkJeU7b8Ht0QMwx83R5slBMO3C5HEAH0DSS4EVqpfgGpuEywaJ7DZ/mNiBkk+GqlpMasFYWJq1OKnHPoTmcph2NqExenwHx1JNd1QkmM6SwEHMICJcwkX7nYQsbYItVFIFVljB+s4EyAaHB4K1YWMeL5pr9Pz1HqDfIaJDGChcNcyhOHQAGxmG7bPp5Qp5fmbb0pJPh2+JIAz2OvbD9zLQfsx6SCXtHnoVZSqK++0hq0heLt0UThHc+0In4WNWM/kWINmoRbknnc7/CbW5fjVZhr/kRVgOeG6CpUUlAWc5TMuxAm0q1DL5KQtsdQukYlQkUOK9X8yb6fslZKrIPT2A3bXq45rz4qVwOwzyFkPV7auUJuDbiu9gXOhriZPNi77DFkX5BTAIWDIgN4sjvqB55xoyBCHdozGRQIpI8opHmxIxJFmx7ffkdGTjWT60qh2ZXaGII5981qjnY4ExWNnRLQn9qJ45xTsv1lesznl/Zc2brwOViCSinbdLHwfpKiGehlX2/69QaQ7bsL5knwoSayCFgtcPVWpOZgw5Bd2NS3CY1RHjGBc5L7TEYVZIDFWopcOy0w9RdkSIkVDoY+WVAMicmIxDJxXfNwl0vnNQ5zCY9r1dMh760R7pvn+qexyvNJLalBDaLlR4d4lIdEPReE8K9v84MTD1W1bBTInZcbmHfRmGiWFzOzZ4n6gzduqBSZbWddqBFgrMaSPmtenkaZtYYiuhlN1A+n5qJY7W/W9i8wAjmFHjJHLWwxOzmGGqn5U0Pj49KececBdKf09IOuzn7mjTIB205zUCa4JDjJhOJWnURfjRRCq+0qhyoiUvOOGtpiQpUbk8JrGvF3xfbGKw1rJhSLfjb9hiMCoRTp/e+dDcD9QAdLADhW6nbA8KUmhyRJ6lRm1Kb0T6ljRp/zwHIDDuvEjaN/vjXPa/36FbxTxra0VGnPAQGnSxcKBdMvTf/iF7phFVV0i+S3aG7oNiv5W//MwnLJphya/UoIysTWJC+aEdNO56cTVgpqIRnPSbR5ZoCqlZ/qSapzLmdzF3XWk0bDjlzrsLH0HCOAblebd0vB/T6Cme3+dfW0EupUjJt0+baL+linU9J5g4IvGclzcUsoLlmAsH6nyczSJhr8HlZtG+OGPTKQ+Tt7Uf9OJul2GGxNcObtlR60Kgv4zoqtNDUid46ovo4n0Uk6Mwh0IRecPuCe8HCNkEcTBS2ZO9Aa+gOeftxgdBWJaRzeJvnzjCSWTYUK6LqQDon3C4qtiIk43/N7eFI6qbdRxvAWziO7K4SE9WAlTQW4mfpB+cfKAzS4N5Xu6sWFbO+SiHeFfIxTCGUUW6e0MvZBObYagrVgX7p0DkMug5A4WVkVWGW+sBUwZiC69kgHZBxtbRCXAEVjFLiZIwB6XuWG+175ikXIak5f4G0IkUy90NTywZaVSk+FEc0ERdP1LYyLLPOTMgv6nxrj5GZhB3Da/wjHe7bukLSA1HGJYpfVWKHTzhjr2GP0o7I/NdWbC72Cc5cDMH0biGYsEdbpggyTTC8vIgOwGC9BIzMxUMQwW1QvcyJ9z0zgJJY2MxD6YYrksiIWqEaQifGCCySniGX5XcKjULczJJaHSuyraN9xqMbKGzpS+CGBtrD/WgF9kv0wafPs7e8EPrk3lSvCOn52NOzxyzTjPtfV8gZMNfnUVW9blPN4WY0IE8WhNDBligpngmu0y3srUKL+6Tt7ozHks5eag/DqACKtKd/rnResBAtuauGA+KzgG4OqR0fKuHnroUmHUp50M8eZ6vZRbaVrUXldX7fjx6OkskaUJDApwFzgNyi5lxkm8fhpSX9mlJBcKo7QUSAZmVEJj2NRYJH2IrT5sSlKEwqBZEUSBxxWSCYvGpbKe8K6LzZYDYXJmxT6JJBfJz+wuYVIXjQE30JR++IYXgShDE24rlRFfJ6xfn8jHH63mFnXiIhE4pZ5ZCCnLRG4qrjqWvfo7xG9Tck/YStSWG6mArc4haQLgX5RlOkiZfaw0DfUH+V0AhNSc/IivGEqMpbKQkj/c7hMokAtJm4CKyjk0nyIQRkzQxiY7y0H+bZhYjZpA6JIc1sZMYAkUce7oJhYDiko/Ch2kMQAhk9dg+J+JY15/1Fa6dNCTD0k5JcZD0CA8Ba3Cx+l9x+BizMyq5ZeCsBtKp5oFNAuv+gpVdeJEqCoVbM59qbhlMP00xApjvnpm0bKzFgUOC64+v66hmqDb1fgD7wfL2iWnvH4SVimps8LEfrRcrNXGIBOZ97nz8ihypIqpnAvlPA2XMsrIRZmU+gJRSTwq+gkHeBUXlCm9G0SaE6SalJs5FJKwNK+d/0yUzZqWkrkd3VCENVcR3XcCxZjG0Gk9hxG6mZe++2U6ZxK1sPzcUDeavUjvKVXvUO0Vt3aPmflDndbaGa6lfEuScXqCca1LlJZw/K/FFz1Z0ztQIGBSXLZkoTiB8VwVKhrNDHXSH2HobTrNZuddLUBHs6qfI+X4+W743zTwfQj8NCMa15dpY5GeuBzWMOyDBxOMaJV88f4iI2TsufHgnKDwlEtccrWXIGbpMnNEUwdDAxZZiy6mENmSHmFerMOwudbgT8UqjmXnsMRg1Gc+3iE9KWSdP4nAcRSYG56Z0zIolzU5ZGNrCfYw7XRKwKQeYcQLPN4wpmOimOC3N4n8Leas2YtLlUtv6yUJjbpG5bMeMnCmh5iIACSQvDwK/vpVQMoeKY5ahHKZAMhNiEwTliG3aDSJZdgVKDFZvQ7eXh1QbScaLgADSOdlSEN6VoIn6gk7f59YUOiYwoCPvR0S4XtDFwnm5KXC4LMR/OfAkZLSd4jivk4qmH4r6GbumnXZ9CmBxyZHGB1pBchNhcnqBGQKuFpxOw4xQYZFJzDDJenfA6zC0+t6zXPTv2dYz6xjaqiHQR+TMi8of877d3pIUvckid0IK5ukVHgC71ihbrz6Jbo7Qknx/EAQkNkJK+eRkTgQ7LjDEHUUbDF5uttZLQkHYJ6mKDAse+GqOk/REqzuJ1hcNNJiU4CgjmBg8asjdoviz7UGxJxh7tdLmSvkZ/gORhwPXwJXrdfT0RRCw8s3RFMuw35xG6wuYMOdk2NSDfx7xf9o9OuUtx0l1KeiZTEC0JxkwQs1df4Xx+xvPzK1xfPeN8vkKHlXix+sEjSoCq+yQCghlJhWivAoLlR3NuV4YLXwQKpE0DjEu9JmGURhrsr4G+X44z6TcB+Hz5+zthR1p8DsAf9b8h65EW3wbgt4u8ZkcsEAjNpHCpwSpkMInJsY/cTgES35dBB4GX70kMtNcWV0KBQrEnsjWweFXUOOK76AyoDgHiVgJg9za2JtFf0wb2+xyEtKlFSeDrOgp4KHJqtwIfizyL+zTnNMdWvOJTIOi57W1hyC0EoYizXzjHCbXLsYtlMcNxA2r31cbrxxH2tMAdLj6X1Drc9NDdL3C5XCwTzDO/eEo3C4NFdY5xxTh5hs2z73yyTf+KEebEOu6cO2a01T2uSXMpfIhcQljtGtEXh7QydeY8hi2fc0Tae6m9EaOKyGcB/JMAfkf5+NthR1nAf/7a8vnvU9VXqvqjAHikxWsbmcsKhPlRBHOlPB7ixMErmP1y74EVaRVnSKvB+nw3r51TbRvc1GBuEn33sirgAqHYaa3YX5J/07kF0FvsMouwaLGXrB+dmUaSHtv90F8AsfuF/3jWZxIAn5vSnmezzuJM49YrlmOJQ5k0PaWxCpw351XmSOc6YkEe8V6umcK2Exb7lVLGtAoLwx3GhJ4JxrpJl6cnPD19gE99zdfguDxB+uG5uLbVzTy/V/MGv3qF8/oK42rhm+fnZzyfrCVcD/3y+sxaKSZnzoRJztcKU1dmA+g/QJpxsmpo0klt4St4ob2pjfovAfgtAP6u8tlbPdLCngMEuHUGYMCGA6let5okoHBbwRc+whLO2HSSAHYIlfQDz2NgOMeJpueu9YY5BDJNs7Te7ZAqMe8uNX6EfryxT6YzNeyv/cj4G/skZwUxA+7AMLqfER/VSPD2cIVrg6l+lg2NBr0lIHv8jGdQE9hZpWYm6MhKg8t9msQlDe4UszXqfmocPcvhN2DiiQBeFAs8L4ZHhkR8dQKHdMzWcXr6IL3sUNhupEav+vRqf4quVnZ0Ns+fVoEOYLx6RpuK0728l+CZBhxlLO5/mDPXjocq28nwq7MoU55zCyV3DYVWntW7y3dpEUyVXgSiaqcBfCWMKiL/FICfVNU/JSLf+lHXo9Jdtpse6H6kBSGuwwpqPSNEnxTJxxF6cnuVCX7uwOGEJEQWodRCOHXCfnVt3l2bC7WB3xNxMsn6vYQ6FAgMjJtjqvGRoeGav0tcutYDlMgI1W60EAzjkj2YitqwsW5SfhyjFUJw/44eWdq0DAcJEKYGYATWfN6ZfVQZD1yTWFLJeXU4n84Tq55PiM4zc+bQiFcnVXiCu//V+xHzT5jLcqzcLhib7cfAaC1qAlu55ez786tXkWHWnz7Y7H6JodTEChYJjzrFriGztlKFzLfIYXcQ1c9rTeqapWRL9pVp1F8J4NeIyK8G8CkAP0dE/jW8zSMtvJEhK7zKUAAQeeCFqY2oXYsMP/eFDgKEmetmvafytSy+Ve1PQuDlsGAtgsKJ/zhYqCyvIXMEu5hoDq3KRVGlp9PCPSY8nGDEekjIRI1DD7YNK7VmzlslEPbB+xPv4d/xTd6jjhq8gl6TZprE1cf0w4Zbz+vnnK5tGi6lhEg6V0wIMN5scNjtbWWG0vRDjf3IEPUwlVf7EPAcGT806nhCPw4OGtCJ2U/0aSe9nedZ7H0aHBPjfIVXUDw5jQnUxNxhh0q34ylMDEChDI0xfq0+/tatVrHWfc+3wizmYUdbTigCD5Xllwsyu9c+0kZV1e9S1c+q6jfAnET/vqr+erzlIy18eMFUAIrnE+msMfG8SjJKyJ4bdfdYYbUpbVzxurQrQ/rns1mBnTswbGGKxMTtM9jR2CRMh4nyxdZrPqMiBZ/z9E5qPYhZY3seHFovtpXwvWRMg4kM5VCSU6NyCxwZifWTa0GwQCoivhnaU/KQ3togsiIfrPSLCdFZbFiOg300rbXa6ux7PzoUefpAOw4cT0+4fPCEywcf4PKpT+HpU5+yzz71AY4PPogyrQDiCAyr2PFsduv1ilevXuHDD38Grz780DzEfgD19Xz2ogFXy2ZSC99NNSeUlY+1uDu3RtajU4CyC2nL6gJhLhB/a7lmltTCe+0riaN+N97WkRYgAQGpCZNp7fiBtHngtDVdGy2u9JmH9zYpubRSd3A4/EFCTNu5IJHPq6PZhmdPKMe0cixMpLYmdmhUS3hLLycJMfkg4VJiXYSWpybKJyO1Ehgz9HeaSC+VByQYoCj16CcTFYL5qAF4t2RyiAQikBhTRzfiHBR+bi5QxkzFlBne4t56gAQtz6qao4knMHiyvypwjhGaP9dULVZ9XJaCaGFbjmYpUOcJO0SgxVlBgC2Zbbq3vcZkEDvrh+hAY90JswEAvdv5t1UIe796oTkURozn+/w2SUcUtzOizndhzut53vAF25d7mtsfgx1YDH2bR1qwuaMALXfp535IJ4qicTN2lczAMzRrWIIMEPWIUKBRgaV1wkXgNW2Uj8l4Y0nqDxt1JMTNs16xMKvPzfI5Pb+qhcO8HwDCpgnUKpl8T1ajHWrPlNCCym1uFGreBx5qBVVzjhWNXA/l4uxx/LTfSIhVQIZg1bS/1YsCie/04ecszcpi2eZcytitwk4dAIXfNEF6ebrgOC5WtFq9ov3lQL9eI6x2Ks+qNYYdOiETtpjwbWfKNFMPo02gHzau3jumM6v4nKxoCWH2BDZyWui9R644YLYtc7LJuPQ1pClE823e2QaX7TEyk5D7S5d8R8IuNaYMiMXBlft5sLGd6UFCpXMmFIDfXr2vBkt761HlIO3VYmPAKxXOiQsIqYG4xAVIMHBUu0spUxlJClpgZYWqWSNBwd/N+wEsDML+K5lT0/bmiBdIFaYEIg+apgTtp5rsQPNjzBFaHqyhrBrF12KXieRa8F3qjDhVPSxiHlr7PeFhE/PIT4fLrecJ5vaz4ThS84kIxjxxkQ7pByDPEDTos3uVBWgDnuxwRmmWplnV43JM9Dah54D2hsmDvZrE8Y8QiVKkUV6n1TzuFAyAhddExIq2aynu7dcvZ6gWlPfWNOrH2aIEo6aGsTNESz7lSp8FqsGhlm/2dQfMXOB0amZTWlysAYZv6PyIKnqaQejwZA6eP+N2Ip/rtl3A883Gs2cRipe+zhKX06LBkFDaCJLP4yRMuj44A4gfksIouBvla5E4O1bIDEAKjGJzEdWkMc6rEcxCp5M9U3wjNWsIATpPOwmPxyZevcD5Oayg3Gm2n0FBGKxvefAWiwl0h70WU7XvLnIxj+55QmDx134ceH5+BTxb4TM70dzs+fOcRvSquFrnoN1OSeij5dbFfgAzk+XPuSa6cG4IZa8uCKCKUTTueZ55fcsze6+LGWbTe16vL/LHwzAq7auEgyRCaikEg7HUCVxzqDrEEgT+1xIuqfQqMFuBTvHFiQG+x+0sMqQA1/OK47hAYdDxnAPNy4zS1vGH2JPruSNIaVptr/gyeDrHq+V6Sl1MP0FMtex8kTisV1D2Xmp0ZZtj9VKq7jxiQob3QUQwdIQgC0ddCC/NnxEWmzGPOid0WBkYaktqr/O84nrl/lGr9DiHxUHV9+5D7DQ2e4d4SMbL2wjXTDz5xO3p1iDSIfJkcVNJJjqvpqXEIenp5XZUBOrjn3Ki9Y5TXIv2jtZP+90ZFc2SMSDmBe9Hj1xtEuf1labmFYtJzzlxuVwy44m7m+YE/Ss8P7YmjuztYRiVXt0sqZmQjHAOYIAYGMVJYZNZvJ2utmj37Xa/2R3UVs1TAb16+ylZMsWZYZwnjqcnDD3RYXCI53teDrsOJePIVIn/SWmKPF4hEuaFtmyz+kdu5yWjmoGXR93bZzo17GeBGtwDvcAJ9YkiaA+11qAyc0tbzC1SWFDSM9YZbpDVBmYtKdQE+SiRKbG/VwR2ZKUfPDzmmf/GaXnxPlfTC6Nzf7GgobfDbTovILdkKtnOGoPbDcelYxwXXI4DH7ZXbuNaqZxXr15hqOJyuQCDjDHNGdg7xPO65xholwNdO8Z5DUbsveNZbX+qNME4q42ugHgMfziT+j7U3jt+5kt/265pKSzHsALjpNlq295rD8OoZLjYywcymU1GHuWnKc29MU0voKdrXwCFCNPTlromf6czgKeuTabjFa1wXq84Lhec12cc/cDT05Oloc2G5lpceSiN2zZk0DFHZLBELR2W31ANJw67Z/wgkcpIPq5Qlja4FQ0rg+YZoF56lDE/cyIlMcw5Y3fO7jyKnwEWJATBnBPNOyrTtIudPjBjANN335y+mXuq2qHF4zTIe/XN3ucVCvWzfTTsNq4HXMvRNqXDjtCXQkKbQueJ3jWY6zgOvPrwQ5zDnE9nGxjXa9DOnBPX6zXMh3MYfBUdaGfWxhIAk3tWYZsGlOYSPBqBSUsn97eeGSEwdJWKx+bcz3eFmQ3vAKPmIABAdYRzBnSygMzsd8QeQve6EiYCSIArYSdSg8A9k5C0sVrUf62OC5PitLvmnDgOQhiLJ16fn8F81DFG0S7itZ4QMceM32bYho4ylp9MNODT4o4ZajJVwnPJjCJHDaoWxGesrgFQD1Nl1QJPwawzrysyqVA1q10w9zfXaHhMVSC4Xq++NxOAstTNyGMqwz4dYaeO84pz2LGWc57Ic2wTth7HERUzqPG5i6b3Y2Ha8xw4DhP0c6olRgjQ2tfi+dUrRxRXXBVQF5QZwzZIbNPf0bRjCjdyGOw+3cfQWsOYDbP4E2pyA82OOEtnFiEpzbYPqs1fj9Kxaa681B6EUREahZxI7ZnFkn2HBLLsxRysskd3O7UgmdRg4cSMekN04tirmJ5nbvkxLWbWWwdax3B4NxsimX1crxit4zyf0Vs3whgDszXoCRCeZ9V4ExDTq/5D6fXzPGIPP9h5pumNzv20dFZ5gS0hrLbRDGXNXLeLZ45tWndyjqEhv1bTwr4fvqdy+CHO1MojiDizcuAQlc41S/uzqhbTz5A9x+l2oW0vG17niPbq8LrJcwzwSBNuErDqDraBvLWG43KkzRhOHc+xnQgnkyUnmVBqrWE2T7gHcF796IvrMCh+nsCpsAJqPcq7cD6CMUd64mkTm0xKp1eUsGH9Lsi6kcKdTgzBnGNYtlEgycxFv9ceglEJFsNe2pqVffTdC7CDnxo66DGJ3F3AJsS9b5YNNL1oWcKdxoubxeK6NAxqwybhvDg853SepwWm50Q7DjQA87wacx4N4+oODsChkRGtnURmNneT3NrWkfAOHr8dM6FmJD8UDWPa0xiBUllcE8PnbsysmNh6Rw+NnLaxPUYjn3n4T3qYr+OM+kNzDGPmkWl+9uKMg5pNaTCvagtzkAz36CbcG8/XOC5kztPh46SlAGkKEUU/Go6nJ7SLHRVyXC4pxN2Ot6LaGYoK505STu7EUnPE9SYY/Qp55ntM243zxBRzgDEUpWgeuvOYdSlDEnt7O4WVCzZHGScVCQh7AfQMMXJjAdyUmDd9X9tDMCqADHcQygm1jakAVjUgtDU4VpMYCGOyvg61W7yD27aQ9h1jlNSCl+PA7B3XYTV6e/HSCQTjHDibEem1XYHnjieYbTvnBC5P3kP3KmIsydfwWCPUAf1ELC5bFre2JirlmsFeByy2sSVju5WIE1ZTKO1NjX6Ec2uunkjCVKha7qwLO9OswysZ8C2ZAaSKsL2Z9jjGDM1FeGjw9wyUwwJtaYRbKujlYskN3I/Kkwm4nJlsgpw74TEWE6omIMcQXGjJn3ZaH+H09XqNcV1bw/X5ChE/UdDpjmNlQotCccjhxc8BYRF3mA+DSIa0EuaaCNQ2BZWsJAQjzzGWLKW9PQSjWtJIHu5ETQl3SEAnujtmTNEY8RuUM8DLQpoKYNK5Ikn0dCZZMvSqray+jtXk7dLwdFwAHSHlQtq5XTkdDspVoHhliwDFoU84BZijBeO31jBqRtSoGwF8rFujANqvoWCmbcqdLvVGIhMmbTIJIuKxHsejVBtz4Hq9RjLAcO0wpzP5HF6RARjz6us141yY1sTOdi3vGZNM6JC3MCrcRlTXzg2EkDlnRz/Qu8Hdywcf4OnpYlsP50BrB8hAIqt9CNDDbfJwDCKFbk4msSoK2uwAr9YF53nFpTccreHJ5+B6vRah48yFkmGkebxnooWRu2x4DMrMEqQG211kTuZfZ0x2Uku/0B6CUc1zRgiTCc08wS0dHYn54xomBfTiEUYeWRB5rq45B20J3wURFeJFcPTDXPfNjlO0oly6JF/DM4YGBCJ2qBTkGYDZmsM9hwAyb7QlvL8cB3QQDrnVHJqhOp3ys2WuNBmizSRWZgYZ/LTfz5LzWh5gc+MIYDpzngFRJ87z2QSE26p0Dk0n0Mh/ReYnxw4bj12zlGcePtVCEOQYJg737NZN4h988AEulwue3HFkxQRa/M7nEYpXyFgzhYggLPbcosyqBbUu6GrleObVNG0rNj7XcDoq6K2Hz4HOIYHFsKcazhHx9MzWIKpWB2pKZNsxGcZKkGpkhvXWIFt9qL09BKMCLvlnOmKMiBK3k7jYwslEm85DNA0w+wsGS0jM5mFVumd8km3DrjarvCAeE7VAvx8j2A/0i90x5gDmCBuJjmT1Snh6DMjo5mCQtHkVGie2jZKCRgHR3DkRjFoWkA4hOopqXHkgS4cks2YYqmYaQdzTKLl1LLTGJKw1mDvVQiignToVV8YU7YbQ1LF+ri1PzeR8rofBW3tnox3XBIpMsjez42IVHC5PdnK7NBwQNLX5Pfyg37rvl/tYYy69P+aqMGY7T0VvB6Z7WhUTdlpcQ28Dchy4Xs9wdPVu9vY4B9AbLtrdLjXh2prgOQSbeXjb02HCatpWy/P52cZNod2tmJpC/Fk2ju6RBStD+w4cZAysMCaLSeciMDSx21kMt5DAFRnk52TWrKAIVTgR99bRLoIhzuQ6MXHiMi/2Hq971EZHHnMvriFO6ASurywOZ+GCI95F4r4iw0G9d/CYCdMQmZgAd84I7BjBFAo5N1VLRklMZ2DzLA577nKfms1YCoNzfoan2E2Ps86ZJ6+re7VV1VLanekXyC3Vmy0YSOEBCkcXDsysmj4fzQuWNUcxF0+6762cPyuC7h5zO1Yx29zWlUd2Gqri2S/shyVIzAE0HC7UOjDMCdZ7t7noE4d2zCdHH+q7fKgAIA7FZ9LcVDsvR/wEvaenGDM3pavkZgj7jgeOuS/mXdCoAFI7klkZwqCEdrgVe0zv2HcAwhbj77xsZ1Y+08S+B9h14oIOxQVRmOx6NTdOG+aq11L8SoBzvMJUyxU9jgPzvAZTjnhvi/2bVy9i1o+OU6/RJ8ZAFfDK8JKaF2nH1JxR2qSZ7ue2p54LgyiISugF9WJrsBzT8MrOE3AnFEMvtK1ITExMCEYsc3qWv+t6NDEnDzwt8PAi5t1DMcdxycyj5kdZehXCg7BQMuWTmsx+t8QNE1q2G8be644msUgBqxciaKccsAwr64ImaPMEwCT7bubPoIlhSR8HWrzb7E7BGCbYxxw+p0hUpQpIKhhbuV6Kf98WfK/tIRhVIMGghL8ZS8QKgcvOekJC3svDjmsgGopgKHoH99Qvi8dSoAnAo/pe2d/Nmasf09LhzhP9OIx5nHjFofW8Xo2YW0nmL/2x2F8DpGG6kyY3t7v9IoJBgpcMnKP8BJBnn7ompd2jztA1hiciZoe6XcprBAl9AeA8r5bCW54xS2oiz4kJx1RrsU+oVnqs86yuacW1pdXstbN7RATHxWOlxxN6MzRyNM9GulzAc39iA4Fwy5jnGKubM4IoUZPx6AFgokuenavNnWCzYaqhoBCI44QOD624s0znRLscgV5oUgQDqjnQDrXjKPsUPD0dPvY83SHogISvZp/H0SjvQniGDBWLGwkCueDMBwZoy6XtGgQiJIzUwlXzLhkkceK0yzht5lWUhtbdnpArRCxJ+zzP8OBZfV5mR0235ZzARTDPUQjWnB+DjqvGHNaEawa9Gk6Hu51EBcaWJWG/w6so0ljmp25U5tGRHPPz9RpEzI3kOrOygHkoJ84rQy4eKxSE/cs14FoJHUFIZyDhPXcxdT9L5vL0hMbk9tZwebqAO6RMu14MSXQ7fUB9bPa8FtvPxqDgVrDkaNCQqUxwEwGzi6gEWjMPL6C2mUGPxaRqvUFPwezDTSY7YX6cJy5PT4nONhv9gjRJWDtqobVYKok5hbZAiAAg/R1gVNqKEcbYIDCQk8OB8fzL3WaqzLkzaW/lEF6p2pWT6Iw3Jy5PHSIHrv2K1g+0w9O+3Ks5huWOjpM7dNKpExUNo+9ZVrP3brmtYFFmk8imjK0ESXeng6+oe7F9P2UTK3Ep63z11nB1e2sfd4ROfM4G6Dm3uCxtOtqutOE5vyxWDgCs0pwhqB7XEuGI7+uktxZAhLienp6CQXvk8DaItqzn6/m0mW9LweJjmhnXJKKBSISZGCGgwAu73SSXw2LYZ5YiZAymCm3ioRWDx6qekqglVTVs8JzrCD8h49U0KbL6RyvKpYOax8ycl/njYRi1H70QenorTbI6MXr5RiCl9gqvUouyVcZmcH+3BSLEw8mc5iyCKJ58A/H1PNHPK2YXXK/Pzmwnughm7zivdvL1GOYpbGiRtB3hFK7EzNDQCY9rUjuplbqcDrsA7kSZaI1lSyitWXfX5mQIt1jJIugALOVJ0tZi6IvMbPHHvaSM7QclDAfa0Za4Z2jQlkcritubJkB67vMNc4V7P33Xkp9jt5RaKWOpZoB5wtPpSLQBr2UUWl4AnSeaGAoKSOzOpd6pTV2Id8+nFmPWpqkVzcwhiiAiIxJbfSUMQfEnnY+0z9ULpdUIh/q8vNTeiFFF5K8A+GlYuOhU1W8RkU8D+H8C+AYAfwXA/1RV/z9+/XcB+Gf8+n9eVf/w61+wvCslsDKLJ6FuhBs03e+qjKOm9iBEq7CZtY0CPjfGWwXwQ52aWKU5aYo+fd+n+SghvqsGAUGb5QT70fRw9/rzOGOhLGVR0staHWDmrg0bJgWIO4Qi6E5oalAsGXdlyDixvTxnlBIxnN+qhVtxZhwHJXzOn5UX0Qg5AUA7kjGp/VqBbSIN2g/01q0eb2VyxhSLRxewfrR2lDpQMIHXiiaXWElnsvT6khnoE+Z+XZoCra2MP8mw3ODiNn5rgmmZJOB+Wnu2aec619EvOtc4v3ICEPSjosNC6Jpb+Uwg1iMz77cvR6P+j1T1r5e/eaTFd4vId/rfv1XWIy1+IYB/T0R+iX5EgbPa0YAWxbiOxGf6RxyK1QR73hOpWHXRfTIpBUk4vTPm5xlOTuwAMEWB5gfiiuDSGmaX2C1zHIdl7Vwuls3igfN+OXAOy/iZc2KeLgxa7qZQ96Zy/2aEYGYeQU84yt+BhNg2JNrYlg7HeWhhKxIqt5hDp8eo8yuiCxOvGVFZhZHf997Rj7IFzO/pRznaonWgGYMySWEhTONYHBePi4ofTSEt3kUHj0g67Ciwxc+bcT2ViCjsURM60yshNoet7N9UjR1TUhiYtn1rh0PlhLXB4J5a2IrvBALPSjLh1ppB7yQ919rVrvUlDebW+3nubF8J9P12AN/qv38vrOjZb0U50gLAj4oIj7T44y89qHp9GfxX14Y8vWu4DeJcZlpNBEAPAkctElaIObyElLCa4Q57lHsTFeiiZcKBgQbpClFzKLRpDqferQ6PyACOAfQn9CcvUzCvaNcTl8vFd6J4Hi1O90xbf7sSAUw05vM2Kx3SgpEaJOryEnb5lnEBuKPmkIv3OQvDzQlcPrBAP5olpLfuhMmYrRzgGTpmQ5aTy+D2licx0GNL4j08hLJsF3Sibc3gv4jFlac7m8xbmkxoENfhYBenBoRQhdu9dNqpJtMolBEPn5ekj6n0VThT23BhyQ6eIEMU4HZlo5lVYWjR2Eaf7uPo6YicBcqqE1K37SMheLQoijRdVjNlN8lqe1NGVQD/rpgK+H+oVbl/a0dafPbrvz4kOBntcOz+1OxskaFrCUapA+aC+kSg/izJAbXWL5CIO6bHn9FaHvNgXsjchjSmLShcM16Oi2+e9r2XYwDzgtY9H/gceLp43VZ4hQPfRTNce0KBphZ/M89jj7FF6ZXF02n2nooEc3LuyAiAOV4EDfLBB5GiKS0JzGxiCwdxw8NxeNkTElZx4NFRA/hOFE/5U1gCQD/SU9v97BjTcMAh4s6lapKIj9+YGV70W+Feb6GgZVH1ZO508aYmdbpaIaTSIcQldts1HGIr3QmQO2OKIDQ6McG+H37dZnPBR8EDK9Qt5QwaX09q5N5deZT1fl17U0b9lar6V50Z/4iI/IXXXHtPLNz0QpcjLf4+nWJhDbcWwXoyJtET01emKg+LlyzeXxRYwclulQhvidw8hOKTnMTSpQNdgOax3jah5jIyph0DjQ4ar2og09IPMYbblgOd26EUuLhGDyLy+/qTaXYu7JwTF0EUEQvJXeoYmT12LB5ScYETdmvYegIWWkuHz+qI4rjt+TPmsvXu28vcpj8u5hHvR2RwkRFFPAwVz82xAgm/cytyA5oGnAVqFpYuMXOPrdy1vfOsXE/d07JjhWEbFIYWe37zpAn4ockxty01L8LLXGz9g/13MK7qaIK3uA3bEJv7AYfgF9rRb4FRVfWv+s+fFJE/CIOyb+9IC2pE145TdfHu0kMKJyJ6yhbiQ3p46y6EmGhqXn+fwU8NyhFhjDAzTjihjAcevVupSZmmBajVpwLNGXVOSD8wjiOS+aXsCY2fc93iFUzpW8/GvJoDp2hTY9oRfRWHecF4M21HEkgmhK9ogr9X722YCeXnnBOd68C5F+vPcXHnTzvQumUPSbNMH8YvQcira672IiCbBLOkY4ie2NszRSlIDJZS48p2Df+VQ7oASNGGKbjS3qxoJm12cx5Rw1o/TTOTwaj5bRTWvdgrDOY2q2X78l0hCDO99KX2JodE/Sx7pv60//4/BvB/Qh5p8d24PdLi94jIb4M5kz76SAsnXLas6i5hS0rO/JLrWuujLsPkgtfUrCKNjbkQ8FbBsikai9tgJ6ZZGmMKBNZtleG5xh1o03NYfc9mm/bufrlYyMW1qo0393eSKFTVNpQ7czzhgwKVrG9Ta4Bf7fS2Ep+rif4A0oFTYo5WSc+eQG9t1kQ2GDyLt7mDApT50+pJIYAN3FMBD9YvEhwiUCIixlXRHea6fwCJBNZ+a0nkaJGhRWEbtIFVcwazkUs0mbTt8yJcybyXKMf+TmHBd+S+55roIWitoBy3oUmXNq56Ol46+gC1UjlIofG682feRKP+3QD+oHf8APB7VPXfEZE/ibd1pIVk+IA2Du1NBbJwMpnSXsQZdkkpyYhObDwCAeUepxW3g1i9T4Ox0+T1hXDiAeixhDlyvIJDm/b87mUjLNIp6MVh0NAwrieYrmg200TvWemvBs35zzKF1PuI+J6ERjsXKOl7FCKSTqMKOfmZKtMVLS7KdwjMSRQ7cAriAMyhW5/dj77EBNm43zKZQ1wYpJeVe1q5fkzqj/E5arDT6xBZRyvDUbgVZ0zQezrGuA1QZD3UqWqx9JYLaJsrGT4EPplNnE5YknZl9FQormm9OkQKF1MCualgnb+9fSSjqupfBvBNdz5/u0dakJoIO5qdNp2VA3P+gyndnc4V03Jt2KLOeWmPcBcOt5YRPqfE5KTpnFDaqkhiMoJz71+Dw3ZnZPQoE2NQlXaMBfflsIR3IoVxmpMJhVH5rtgDi5KeFgF9s+2kJTwU0RBwzYtyCUoIC/T6ulOuMZxRwwZuC/ohVc01XIASt8HCs+tHIZqti+iXqiMVakN/NnxnjaKF95XrZXkImRIoC2G7sAxUtKjOEHVdGE7SMCkBhoBo27fYjqYAiigP7VpVc4Z+ihOTVenC64QCr7mDKc+HVb9W3YxS1xb0hEPSB3GvPUxmElvYk+r7JYsEJoEw5qQFjq5qQ5bqB2RongViz1gdG9XuoASFSEA0wCWuJ4GTjOE6XkOD0OOqaEe3kh2a5TUb/N3Tj/5rtmOHdkyW7/AsFtrmzqgHBQj7RaYRwcVTDBmeMePX0+R8DrnBngnnKaCqoMtUyAZY8epKrLSB+UzXLtyyV4kWQKAbYwqzWc/iGKNJAz9Th3B1+pY8noIAxlklbdSyeibcQlC71gtrQ11ATUsRlAS/9bydEDZeXG7SM4+Ex6Qf5opXugvF4H8vGlxqyiNCUFnFyjUnfW8Pw6gkEkLcWGhiUXACU8NUbcmdGLxHgYRSzdzkwwkghCVlt881FyW1rOtcX9Soo4NKYKRtDYIFBOpxNnP+dEhAqVlCLgO9xwOMGQ5HBwA6EPWLUJ0qhJSSMUdui4ubqwZEwnmUORVnGmqQ6YcxcaN3Q1aBzJAYQpEBJgThSfd8dNUOVn0xha9FgIv3VnJdiXSYXaTIWkUvpdcVlAumU4YTC46efI1NezHBpcW8CereZ9+MH3Oa2XHT+1+THch4gYj8ITkmny9XJITStgfVhUosyTuQlM/BkYh2jcFBU3tW5wKArO5WPutkJj5raWQq/gUnFE9fJFF6f9JW8+4iv8tjFo1IIlcZnHyPaToB2cIBR7f9i3a0outmbkPzRO5WIHx1imWfsx+0HTneGgbgPQeycqARd24hpBaV0NoaMVV+7mmykcFUvZXUQtELQVDhjLVr8XsVNkbIruGDuYhybg8IQ9zLDpV3OqykEBDUfG4JyBnzhLRPFY7UGEp1JpQmlpTCbWspX11YM0SW/QqzjZCeuxn4omXNXtamtm6P0CoTUfKgeGPdqUL4EHYoVg1D72d9VnXOMMTR21HQyirNOdncd2ndo5fZiZsLLoB9kHZGhU4p0VMjxH2s3URY7Uf2SbeKA+pe3JtzOKlNS985X5yTmEcY44eXWjNWTOeOSM8xoaO13L6XYRrmFSPpyQkQqmVNPDRUqkiE4N3WmH4Ig4Atn4N0REE9Sw3eB8PXKdBRBFKhmbruojkXZHA6tGYZHw/fYokXMjnmBGTm+oQtS5jPd+eJBHyn2GAASAi+UCRMdWVfanTiTnsMRvUWjpRihwGr5F1Kb/A72k8lXjq1lMYsBI1ik8VRgYpgNNWJsdBUqAUAHjpQBTS1Jg9JslfYJnS49BVBlIMFSN9rvBJAhKcoeAg1F6JjjJT913QeUUPV7wj7i5GVkj9sfbFNCKGlGRKhFpoBHaE558bnFESE+Rk2626TSoQniiYsTAZxIp5ZPYKfCUqozkUQt4zF/Dmzm5jAMl8LgwaRIeB+9AsSY5rTq4rAbOXDHZo6BQhQniaH4TCuQyufpsY1ILUm3YdTkGjjNR5fcGyfeCsQFUiGTfuDpUZcYmqlfIOUY0yvmOeFt8HwijFihXd5nxvzmuGReGUJZFdYIsJFKAvkEt9q9Gjck5AwHxGM73KDRMgntfAyS14L7i5p8Xe1G7kTpc4ZGTfCU2T03kKI1eM2KqzOZIQ6D+tOlaiLHGPjdQZsY5O8SL5r16qOlAAEw8XfkqG4YKqYT85WzmnC55joeMZUL2FanqcFpXGOxkwqCBoUW5s5ffeSO+i5xnZZKoKdvsLxVOY5kUrC+UAML7SH0ahaVL8Ci6QEMv6WZoGFRIAkdhI2N/yKCNCkMIzvAwVTydL+yOcgoGpKXSxaqe4wGb6FaoXdq00Z9qBrKu9eOCpUyfIahzYHg80BMifvF7AYVvaLDNcrIQAGd8vchdfbRVMKm9TIfL6hjA5gBOGhXDu9FMpUS0IHvF6RDDDDi9kLEWrzsIg/pEBHG04rTrMouF40IzUswytwJuTaVGYVEWjj/SyMrTG/UeeZoRoisJk27XRIrlGCxjUjBX2pAmnrRcHj9v42t0nwjsUcSTRCrxfaYzCqS3v7HeGOmNWFT3vAm5lHGSwWtynyjBcAsWZVIyZ4YRZSUElIBbuiphEaLHIO02SG3lt5V2qc2ugtjgHaINN+JaxT2sm1MmFfGC/0b2sAZjgs4r3W0RuioL3VULy3N+uQP1PyA1CJ+sghFhz6riEN9tNMD8Y009FGNKSp8Vw7VggfqKBAZvVx3GtS3lPtvqrhqtmUQNprQEEWZpNO7/bK9HWNua70JFc4T9u4CsiA47XfgG99pDe546X2GNAXAqtk7Aw0XTI7rHBRZpfuzFDpH0V7gZOTiQJ2mcZ9ZNbwKKaStT9pdyK1YUJOf1rYQqmVDR5J/AtW9IfRxkofrd1+9FJHyTWAutbkwVIBiyHG1EhCDfssHxnzxsJci/eYbYHlZU69TdXVK+rrEtoFGW9M+7UgoyXhAcGkC3GX96cntIzNr+O9vKYyXsyryA2MVJTcW3+OOdmaw3AuNOVIPp++COoKnTPSLDnFS4ZbS891hcRJJTk/obUJx19oj6FRkXZHEEGFnwBo01AKD4excbShKmQY1J3q7vaAxvVFSPEkwBw2kZGBFIyvSLrNbBcoQGsnQjD829PJajNCSocFT0MPLQQXHtOS/ZlXS+IlzAqhwOthsJhESqKYXhmQhMl7WPMHAKYA6nEWIXKAlOfmhKkzadikAfXt+xqeMeccUQGKcFLzcsK1mhYvvBaHUIHAu8Owrn2E61TTZ1EYc/f+hzYujjiiuHGeuQcaWBgqk12KgAFir7TZlhwnyl5dMWG9mF1pZwca9Gda9YyvPNf3q9JIyEB1VZewi0qWwlQJ+8aM/RnEYQSKWBAhLPaUtVorCFh3jVQ7okpqErAdkuyE5xwd8Va/ktlEigwHEV4b1Easu8KTDHwcLMod0r0sKsRtLH+fZe2QSF3jKXcPEXJJPAcuuTcAEp7SbTEg6jtlRhEOSueQ1RfKxsR3hHAUIGAoKzdUBxiZsjKUbOuwOBULkcf6cEzFYcbEj8JeC5yuTE2Gphc8YuWc88JMvD56JBTMqYGztrL7Hvw8GjJzKiMvc0o7XucS677XHoZRgZK1sUgh+5ZEzFjnBCVaLibDDXpv0ALw9LNeJR8lnRZYDA8DRAUAW+o4NdyZRX1LGiQJR6Q+Iwua8X0FJbv0phZjVlQztFDQPjX9jIVOYWLPLD0P7UvNlA6uCjcDIhK2InOC2d+o8UTvrCQBz0mhk/PIgc1hc7fahzaeRXiVsSwOmbKui7YsjCTiJ/wVLVlhcoVRwbRMx6zvLe/T7R5/KGrZ1aQqSaZEKXxW+yk5LwoEQtxRENHiV5SU/9VuOVm6EBmAsME4SSRI+yoJZffYsqh3KheNPMsUBA6laf944H5qwtSFsb1PNbUthQS/zoT2ZCWPtfmf3JNKZ8WSdB/ENAvicIHl72rhhWRdIYUlLsA1L3NVNbVSJWikIEDJi+Xn4jm3yZzFvqzwUrlmtgyyzJUJE0g9jiQ3c9s8ZiJDhb2hRflZ+BpuaaZqTjKMISwp6y/xeaAMSXQUdnoREMwu8xwz22C+rVMILY5f3O5lr1y4L2PznwJYqZwiBvb2IM4khKYkU1BjMX46/Z+2PDZRkMW4AB9wKI0CNyP8kotMTWj0lo6dZBD7vrXmtXfze3tWEqH9TWjLMi/p8OBLyVS8dhAqOaPopMMIZX/tqmkARFnU7EcSego32nNFhfv1teYsJ25BBi5ICHO5BiYYi/Nt04YUjLTV0mlViX6F4SkUZ1Tm5xpVrylhKBM4qq3qD3aYLSEEKjOXCVjmYmGNF6AnHY50LPFEBq7TnKM8p6yV03PdJM85qI6ziipeag/DqKplu1dhVlbOC/xCHhKJ7UIRFuDvfKhkAkE6GMQ1nF/ixBhcvrW6dgvsRGHouM46GnBGsxA3PYFh1JSnqtulAcH4KGp3Ch3X+pl4kEkOdFJxYIz7UcNTcof95s8Y6qeOt7STyay0qUn81asKqfnPGfCnNl08sCWZYWHqotnDO1w+Y6LE4q0tP8P0KVp+J/UiKvL3wvTVg8yFpL0Yq+u/GJ009ONS1s6UjBKVOG2Yx997UN/BrrMaiAuqSlv32oMwqlpFuFX4F4LxqzwzhPmyNOarK16DVkMHhLT1KUzm9xsS2uZ7/beEstDoG4mjlfBEhcNMpAdQoCnKs/h07xoXsyWqgMPWiAsXxsxdMjY7I7RcTqBtY2M4JysjTKbrKQ+FGh4Zy5km2aozv+K23yx2VuFfMGSZR25XW6Bn0Sa7XUqGlvL9QsCq61oVr3MKxxTWtZBd2IclBXGxbzUzlnL9tn77d5nyJ0knVZuCiyuZSFEzvjbl8jKL+jg+4vuvTqPEpjStkpNijRqmZPnYrXXRGN9MpaUzY6lB2IM5wKVola7PCg2BWnhqg78B79IRxjMy7epcwGoLVYaPRfL3Re1iFw4ViiexJXwG0pNKO1fK8wlHa97zXjfpFnZmOCklmuS7ohICludACE05iQ6pkaGUG0dRYQiOr3rcY8yV+EN7Z/hu+nbA8PoWGuIca3leZeYUsUV4lP6gXAdkODC+CcGyPqPeW7dliniROPaf73xhKx/wKIzqE1e3BdmiF+hUNE29L3/NxUyohNzW5NeJG6bBwJKTKMLso2QoZXJnfhp4iEywO5diD2MQda31I/EMXuusDoVre6+8kJrNrld/PlsU9SpzIMhjFRkjTTh9J/ShBXFQM0om/y9ZVbFGcNi53IrcfdS8v4kwRNY4IWesEjW/zxI0MdAbO6+KzqiLhGSiGCtSW94IAKQXOhi+CE6+I3bkRHeKliyogHOUAjJ/D5RVhBOfXfvzUnsjRhWRnysi3ycif0FEPi8i/5CIfFpE/oiI/EX/+fPK9d8lIj8iIj8sIv/4m7wjBu27WaDwQ6rXkiYBz5wJSOSpNeoxFevQjb4MStYE99Zb8RQjGNnYusDJzTET3lIU7akIu5QLksyU0Ghqal7eHwTrhMM6vhWa5zEL3s/YLpfPCMZnyY+iyHcksms3cWcdiXjZd0qYKYKIAcaTJeYgqkgKLLLVkmGqViIziHUotuPVp0a//Zrc74mFITmmVn6vjYwj+YEx5szdPRXZLHNT1nJ55jKfTCVNFGJn6qzCIwQmUrMvSuqF9qYa9f8G4N9R1f8OrH7S55FHWnwOwB/1vyHrkRbfBuC3y7Jj9oVWOiqqOMIuQxKaT4Y5gEoGkI+PtqdJPMJGpIZG8cChBP8DVWVoJspGFkaJZIm4r9gZxV3RpJ52Tu1Ftaiu4a1TIX29I8k3GoKF6X+ZusjH8GJnEpBxGdKAVRnUzOrJ6c5xxDGNgDmUfL64pSwJjENs0Xct6CI1oMVNh7+DwiU0TIwlhUVloPrOWeZhIeTt/tCCZF6sjcKA45zVbizPiTh5eR77kPtkNZmxaFtVhOee74y+R7cTmnMedOYB0y+1j2RUEfk5AP5hAL/TB/Osqn8DdnTF9/pl3wvg1/rv3w4/0kJVfxQAj7R4bQtD3wc+NB0lGsw5TErDk9VnTuK6aBpadoWrqWMX23DTvITZGfpZJXam3O1zlb8nhK2wyB4VRxNKIoZ4BlIA5NH2XmWhngUaY5aiVXmwUZ5RY3MmqSW2jtJ2XLECBVnRhP4u1l+CZsEwxhlRIF8DrF7UnLYDSFMDsg/VfgytwvEv701UUm3t5VpOr7+f8LkuTgpmXZioMnelBimOqNDqRYuHwlBNNOAMV2kjkERh/BiP/x7b/F5ob6JRfzGAvwbgXxGRPyMiv0Osvu9ypAWAeqTFj5X7XzzSQkR+UER+8Ke++EUIOkSbe3V9zyTFuMM8wivTVnR+2L8agA7m8pFXTUiXeGOJFE1bkwpvYRzxJfKZj2MbZaH3OjDXRqt9R+9pEqcGOuBzd5swNFZFYEiBviAzFu8KYphZ1qUwaWVILZqUfY/+MWR1I+Q1xr9m0hTCrXMBR0JgCGkt1cl/QeiVyImw6lpsDBc/yTibwH+xvQCR64Z1uXdtFXL8rjJnEVaJseprud6rloVu1Um29iaMegD4FQD+ZVX95QD+NhzmvtDuzc7tcqt+j6p+i6p+y2c+/Rl+llDDRznB6KpJweHB5brIYaxT3cbgUVfZfpT6RrRXyTD0DS4ayj3EfE5rDbXiOx1EKTPUmcafyaLM4oB0g9nhRHLt1I+eWlu8HpP3n1qyTqv47E6dbhsTZmIheBJR9ViyeuGNdsH+nuxzhepjjjALuMhBzEdfElRim1xxqlAbLWYCElXU59F5Uz3HsbTVUcNryvrUkdzzCkuZAwqreraplv7FO+/MkdTxIQXh/uz4riCHFxM02O/XfMf2BQBfUNUf8L+/D8a4PyF2lAXkKz3Soja1nfYg1IL4flojama9UCtlPq1DPiA8uoRvrUAza5lIYDRNW1SCEW2RWvGy0rEE1CkNd1NdQVAgwIVA/WI7RNihK6v0U1DFljZdbVODT35gr6rByjmBYcdDDp6a7dfMrRo/CmFW7RXSdSpaETQAvE4v789NDDzb1Cbds5goXMew9SHUQ0JJwt+qoXahEgdK+b2M0YbGLNfHWmxQOT4rz+W/evIBt6wt99TNAi1PIQjnJvtVIHhFLhzn3j/C39rXuhngpfaRjKqq/xWAHxORX+of/SpYFfzvhx1lAdweafEdIvKBiHwj3uRIizKu0BJErhWaOK+RYbgIY2ah6r0t2TPlPcMTtHtLxwgIq0EtWVzwwAIjbW5WmyaJ7rYCvin4QnSS0JwhGj4sEhqoddUKdQOKKNjsEN2Y074bc+A8T5yn/RxjYkz7VxmWxD7L3xyj/UsPrsFKdqfAviWtMDVTDKPMVZRsLYxXtXzNYqp24SyEz2dWz66iJJdQ81ahxH7Eeula/gUrbL7XQriRDmQNFWUozJ93xzaufaLACTt5oaeXdeqbJuX/rwH8bhF5AvCXAfwvYEz+do608MYKB4Rv1IgiSJupDCpslmJL8XMeV1HxRGx+Vs3wjQh0zAz1KJKJvEozbUVONImz9kvVt9c50AkHiz+zng5e6zElRC8piWUsDIJLQGeN99thUsOOiCwQkhXwyf29H5F9JIKsglAkfBBcpVeaBFwLSQYGEPMFZByTn9OWJINSu9e0vaUP2AQBnw8YYkBqZApdnvTNSaZjLCAr/640s9mBAXsLrGVfIkmlCnvuwBGeT5T3LEco1udSq0sWduee4YD25f332pue5vZnAXzLna/eypEWCmDANMugpAG1EICAg8m0JHBqwiBkp544NcsZDTPhaGXs4Uzq/U6C02QYY7rchZLSOsYLOrkKRs0tXUBuxvZ+KuG8cgbEn31rLxFesqqhZT8NnINMOktebPPdXOlcAwZU7GDh1nLz9qJJlCe3pUCB93t6ZUWzKc2UmOVAJGBiDgDdN+2X5wbEL+/RjTi5tlUA1FnJfbgaBA8g4Ki0dgNf2bmAySI3zpoUQissJUMdx+Fo5izrjHj+bpvnfulkbMG6IwrO7DWNEQvd3G8PtM1t3XIUUAMmEXsv1ROENXzyfJomDecYdl2yehZNI3yDMZ56icxalT0uWoiLEDg1LviXuANqauz6JxyjhuNLGUqKdD8QQSDeS6YlI9Xzc5iQP4YdkHyOYRp1jMjZRRFa/bClbTzEqHefm9RU1Ej8O+ooL1Z4LA/CNBAKymQYG3s9rQyhVfLPW8KsSfdLCmkR2PvB0xH3LfBxgd2knSr0U6r6uq2NApX9mABOZ9BY1x3a+xjhf1dtHjDYryHsZk1f3lOF4uuY9YEYFbnJWjWoh97MJnmokEmn5gyroQ1ay03Q5phZzw0JDRsLzVABS7coi7XbPdAoUqX8W9IBRa277kk1jQehlM53N6RWJ0kRalf7xOywZiLKtdd0pgzGHGaLzjlxnmfRTqZZP3h6AmBa5HCmfYadKA4XAgHrCBObHeQrPjfN5zHQAhy2Nzp3Vs2LTlMgha2WMVGTkFnrodL3tJsUAgcQYTvO5XI915iTSEEpWb1BcDvXvNY7mf3x94XnVraEBPHyKeV8JBUptJJoUIQVEFMwcRwfVXib7aEYlXCXWpS2UUAWXQld/HpQa5SNugFzCYeBYMhFAjtdGaOzVOUsDCjIRyRkJQR3XIWa/cOTvtM7k5qd755DY7wJg9kHDe1o6ZO0QQemTlzPK8Z5eh3j4YxKCW6MdVWFtI7jODAFENihw2NoeFSrRN9tMTGMHMKtaVs2uasqlCd++7SHXegMWB14AFb70OeER2qGc8qFCFeSmwbuJbZwAcNm3bTTXQbYtLzqgqXyGWTsEm7ZBRv7Wp+19K9A6UpsIXyKJ1mAZSP+3h6GUel8mZMOJIFK2U9atBn8YBBqXjpPspyn/RBB1Felo0Q1tURAFGSYxPoii0DItW8QKUkJKb6LbZT2JRlWGoKBBRJah8vCMjORoK8ppEh4UwfGHLherzjHiev1GTKB6/WKcY6obTvGQG8NbXQcvXtm0AFc7B1HO6BzYoAlaZL4FykPOLHm3JsPwbzHvZWs0GpU+n2088j0u91G+Fmr/Tcg7NeYdDi0LoIuIC+fQwYoGpsabowRx4IY6cxlzDsz1yLjkUIJLO++aVWIsO8FGvN56UW33TNMrqgQ+qX2MIwaqg3FXnSIWS2m0D6oh9SaY8MgBSHGhKikpxOMv67FmkMKNoMxdhQDvbzMspF8phjDUutSk4SXV9h/JTIO+Bx1hsQKZeeuHVe9mqNlYejMWDIpPMaJ8/kZ8zwxzumMajYr1KDpPA70OQDl8ipEqOkFA4LLZd15AzihMUe6bkIvHnf6DWiOVPhJwUJtUu23XYvtVQJvnECF+Srx22ogfhJiTofsvC4rCOb7q61bQz+1VSalaRXMpDm+RXMKY+m34SE6uqq3e8kIK+PU1zDrwzAqq5UbI6SLe/f05a9pWzKsoUh3P+EzvOKbK0d/AG1MhN0LIKow6NTMCAKKdOTNur0bYcNVYKXxUtOucGiuyhxmEl85MZtaXJppBDVGGeeJeT0hUy254Xri1fOzxU2vJ8Y4zWt7HJjXgTFa2vfHpzB1eHJER+uWh9sUVtmhjFE07fIpGUqiZqVtSgESDOoasWqVe2QXULCMP6By0bxAatuqgem0uYn99h60wuugGqhB1Wz8VlDEooEDXSWDBy05/BZZY6jhUJoz3k9BcQPz78wHHaGLx/mF9jCMmiESAKjHFrptiaz0lsXK1nNmAJeWfKb/33ghGar56dj2Pl3ezWMIasFu2rnVOWSpgVmBjpNcTsow5xar6rOP/p0rY/B4CdvEYdpqjGGVCF1DncMYccyB5+dng7pj4ny+4nq94np9Dlt2joHup4AD9szzPNM2R6IWamrrd4kISiFWh+9L7q+UyhVws6XYiaGxuCaFOQkFK1RkiCW0qUPCJcTjCyQompJrVOzRBZ4WWNw2IRCMNvN4y7oPNjSkv3fZSUQBRQbfmLd6h+tzzjGWcqkzCf4G7u/tcRiVk0rGkvSIusz2a7wmqt0V/1c4Y7C4NRK2ABo5vf3o+S4i7FJwOhQ35w/FQREzjyB461058axcp0H0MCKTfFbWnnXNTk3rGw7Yf8ZIx5iebTRwjhPn9Rnj+sog8PUa3kdVxSkneu8YvUPOE8d5QD54AsST/8t4m2Y4iafOxbGJhMAoAiuGmAjAEHMy0dzjla6RKqHHdDmhC/wcn8K8+7avvXYShQOwxSpXwso+k+GRQoLajCVqpHxWi3PzXfQ2TyATHqh5dwZFamhh/4tCqYwZEP+F9iCMqhsBpx0gQGB3+5HbgWwiKK19QsgYstdSItSS1HpVu8lqY5Ao4zgHMh/75XblVJ7UnZqKf8dznANmcVbVGrp8LiF8CgfaphrQjeGY63mFjoFxvULPq+fWOgQE0FQx2hWHF+KaJc2SsBWKW8L2cQo8rIGJpplfnXOmcQSHS7tlb+culCYhZyFowkQSfo2jmjOxMHX5nfey9kat1i+V+Mu7oiK/pG9gHXdqx10L85zaMUYIoYDB1O4kqY3mKgOHCVfeHde/hkmBB2FUEmb1JHKSUpMqeJq3ScI6XoFIZe6wbIKp6fwAjMiaO5dAja0JoRdPJ31PKPDVOXsGREMQ/qJ5FFbpAISaKWVDsAQki29CC6tL+nOM0KaWwD9wzhPX+YxzPEPH1dIgBZgyoWiYs6EF4wPQBiYoRPmRnlqT47XQkWmW4dk+rOMU2l8E3Q+vgo+fCSCxIoWxqgNmd8bw2iDiMDlww6TBCJo2MZ1G1c6rdmdSCJK5qfnm7VGHkau7CRxq1RvGklisFQoX7c33VyEEkaVO1Ucx6kPUTHIWMUkP3cpeEjpUiVqIXgFmDlUvZNTFJSzjMQNASFWgCDiHpyTaatMsAoAEi3Q4UTCQ0OlwyaQCBES015QSojwoOEFZhJn2KhHs9xgn5vAUwpnnxoaNFdoZIVz28EIIQv+7CkleX++pm9bDN00UUmx0MhQD+91P6k7H2Qb7itaJoxR37bk3SadOhbtcxvpcvm/ZGOD9hLxQVaFq/MJAS2ZSYTJq9YD2m8amSQApRc0q1A2aebk9BKMCCPgHILyvQBY247Brxb1icYDnvRjDSnpwHQ4TUgKctFtJbQtdJb8vAm3iCpfvwRdv9LYG3Ay1i/Vd0wRI2MvsG2wfLCsaUrNG8sPUyEbiGMcY0e+aa5vVJEwLMi5NaMtKFNXmzH5sUF9uoV2dS5S5pNaogiTnvsxX6WvVbiTcfW6p8ZYT2pOECvpKZh6aZ6hyLuMd+/PLM9OXQbpcPbn+wBtYvgiZqoVRCulxF035/CsqxfLVaK6PMsslxSJqJQdVbs3iorRM1C/PSw3szxfxe9fKcXkYExkUwcxAlaBlMRSZuOD9mOqlOJoEExAixj0+UFnCSbowW9iQrtVjG5pvUSOTclPzOM9g2tDk0qLmU5ydg8JoTYI5K0pZnEYUVP4Z33ePGCsUDEiHlcgXe9VrAUNyphWlqkZlvM0ppfyMz9tQgo2jho08AiCWwscaTux3jEu89E/p++JsciFEh1P6MaqwKmGugoCI8PZSK4ylxra3WXwWd9pD2KgkjiinAoSdaESGJHx4fZ0w3xWi3FcK8JtgDr8snTvW0pcMdGlAw7JlDbTH2Eel9vNdI4UYLAyTEiagM4m+2oBICWsLWmCwe6xb81Q/TGAOS54fqUnHaZvFGwQnUxddwvH+fhzo/UDv3f7u3X737X/0ki5oYRGSuc937a/NixVNy7/tGekxDUIN2+KjiKDYdEU7797c0Dr+N1MQ4zOH9IxTV8Zkfwn7CcsrarlBR57mCPVNFSUuS2i8VJ1wIUmtv++X5ZGTIWToc3lN+iDwIIwKJLOSs+htLcjDF7M6l8hw/FqDoQ2OGWNZGt9KkLSx4Jo25DsNX0hUxExN50RDQVFgYk1GVwUPgPOxIbQX7XBqOa5hdUaF5Pa46HRH0pwjN4AHUzdo97qAPh/H0cFK9kfvEPHfjwOtt6jbu2hU/nQTYc6Z29lg7yNzLmYKBEw0WcITHpIJIqxQOpch1hWxpuvG8E4IHXSCtGfrui+05O2OkNhTAqstHs8qf+/IoJY5heriJV682XxW0ZSBOFTTO+2KqSqRe+1BoK/DkGVQ+b1pMT9z1PNhc+O3XbtsKYN9ZYf3cAKNOVTrIUfFLnJYPSeZvWgQ/z69k/4/SSZcJLZI9JfaXMgYOaolnumfuGDynxNRWmV6ssP1vFrywzgXbUfdf3icuB8HjuOAtIanp0uGGlyImfTPYtk9NiSkMAyholjuFzENzevD5i7zVVP/dnhK7VIZEq6tQtP4Z/E97hWdW1vt3/797mXm9ftnAXUreqP9WDX3Bm1j7vzvZV5iddZCe3V88d4X2kMw6tpRLnpdeIl/lZB49oz9ns4EIO9bikZRU1baLr/zGSRkm8eErtSIKc1TL9DG5dEO9m2BjAXWs9XtcatDybINr+cVgGKMK6YOTB2AKMY8DeJ7jJX1jOkcenp6Cqh7HEdAXjIXUCoI+hyYTbxCP9r6edR9EWqjZG5RK8X8p1BbNmsXTQVwI3oSf4W1S+XIAqUJVfmOKqxC04os1+zX7RA3stCInDTL1ShgtquYHXtOK22zVGgAbkM9FEZ0hnIMoEnmMJmhnHdBowKgC8Yby4lkXDNPLhM06cHI1KYSXOZzVI9jJOMVfUa4y/+MaJDOHiLjsmc1IQo9yrPAx9TclXADQvG5wpG6JS5Efrng0ys3mN0+IltJVXFer5jj9E3f+R+dJtIa0Dra0SG9oTW3TXsLolmOhwjeWImKXuJapbEKtRRmpu2rk6VCzyDOLdyFep19uKTXLVk8FHCF2WhT5u2rANgPOL53Hf/mfNw7HrEKt4C3BeLWc3sqw7PPHGumpGINExUbfO3Z2j6SUUXkl4rIny3//qaI/GZ5q0daKNVhaCruiHEl6+zUnGGLY4hab4EgVRtTM9p11Lqt9YXYltDNApHJcDO8xozLVo8n7zWzZAZx99YXSQ9oPI+avwpihnbOccWYZ+xBPU+LnY7riabmXApYLwLpDdI65OjOrObU6EcPAiMBWmWMvoQnQgAVSBfzgwJfybSBZGhnZ19qJfjYNYLCoDOr+HHB+M6IuRYm9UKwO12umqqMj/N9Y3+Xn5WpqkOpPmuB8pXB/fPmTNzoQEJFdQnpAcTxFlKepdtYoS+z6ptUIfxhVf1mVf1mAH8/gC8B+IN4q0dacGJY4pNDzqT7G3uh3u2MjApX/XnCCn8gkyQDR8VBn8EM38DDGKnlgyn81UuMNOYKMdnq14wxQpjENdDF5quChYXKzMtr2hXUspTcoZF0FQq9W1imdRxHT09v2WJVoTmnpTJJZVyD7MacQbTOvBAsNnZq12SQWq40Sq7wn6738nMSer5/FgGctYB1+1e3DO7a9p5GrUy527OViXfIHASCUhIGiBROMmdl7DzpoNjQZU3A9fxKGHVrvwrAX1LV/xxv9UgLdjA1VMX7t46BcmfALYGqx0vLiWxamC+hHTV0GGgAEI4V2w2TBEnVXjWHUNUDhegoCFIzmwBI2weFUBJm2hSEh9U/m+fA9NKfUXLFoRbE9s8aZNLw8jax0iu9dVwul7Bb8zyUonkKx9IW5WeEgRRei9ZSZCy2aNLFPl0Ew+oRrWaB+uIM7qcta8pn0QveRXC4hm220NFXQtTKsDGGO9qyfnaPMXfNuo+LB0BHSiI1fE2B9O+mdbKYTpWmbo9lude+XEb9DgC/139/a0dafPGLXwxYaNpwLjG8HYLU4DwkKzjAbVmzOROqRRK6z2BvzPMFlqLd/kwR3Nhc+XtKv8gWcgYkLHcU7/31l2qmHBJmVrg95sjk+3PYZvDr6VrW/q4OFiOY5locaM0g7+VyMe16HLhcLqlRPZYatrvANbLPCwU7EgIHHAarG87YYhjOGmfwaGW+wiRgqEk1SquAYyg/qUEDIu65sIX5A2qKRB50PNP/7U6nXdO+1Kpm25m00iLbrr0D4jtFDzrAKlrxuaJDidT/UntjRhWr6ftrAPzrH3Xpnc9u+qDlSItPf/rToeXghBKSzxPRBcDqQROoNqgKDFkXiV60Syhp15xT55JORnriljISPgk6bC27IYlZ6D32KRZB2J3+xkablNKTFR4gYYuyH+CdYwI6bVeMsiyo2ac6DPYJvFKgw0npT2j9gn75GrTjA7TjCUe/hKf3OI50lDQBeot/2u30tgFAPRjLERhDtIDLFCy137GBHFiLl8X8JqHX0i9MVCD0q84Uzlfd54kCg8lw1UYsdHVjY96DwRXy1j7yZ3VGVRuW31fYXLX3zsRVS9rSb9C/oKyX9emXp1H/CQB/WlV/wv9+q0daRNwRKJPCxXIbdWaNGcJDzgMPGVogc3zGfOHqjYNrIvcyFhhHU5N9iHhiaH1uMBc/y8QkosUiPYbqjEh4HRAcfE8VOsXGo1ZxOMgdM4M2a9keR0Y6jgPHcUE/Dkjz+KkIznMsxFTPN61HQ5hga8XxU6Fq/ksUoqt9SjvS76nHE/LzQCW8VtdkAX4G5FxEn8t44/cCM2vjnO55vJVx63VV+46Sqlmft9uxO2Pv15L27mnfcnEKIj7zLUHfX4eEvcDHcKRFEADhg1OIQCGiAAYUA1Zcml7TCRELUOg8reSInlAdYEgjoJ6mw0Qcn9Z53B0Mdv1IptW0fVsjfEwow9q6JHbaqEa3zPJJIktHRbFboy/uYaZtqjPgHRsZvnv+rDjj9n6YQ+lyRAZSk5VwF82CTRMEbFda3cscAQmRqzOM31dnH5nTXxaPr++jVg1taDcm00g6ExWITdu1cv6uIe+ZTfe03r7mZNJ7EHlnzPpsxlinWMy11qjm2CmEudNpjJG1nYLm77c3SiEUka8F8I8B+F+Vj78bb/FIixAo4lKeR1LAmWGysFj1wJGZWabFrk6icWcE1A+a8hIi8VAN+8sHuhCMwWksxb3geihDGbDjIpzxWtVIIXiQsNpEiu0JLRu449Ex5215XxJeEpHBc8t37Z7Y0D0kI0fDcbmkBveDoVm2ROI99x0nvpbRJ6IRfk7hByCrICD3ulKb9sIcWQxdgmB5n5Un9Tq87F+BiNKaxZPLe5Z++s97n+9/y8YUdQ7q/XWXzUuamNdV4R71kYswBHy6fBwiLDowwbrDrzt28U2PtPgSgM9sn/0U3tKRFgBtAt+g7MfK1wHaRLUstlU1EZnBr23dvLeEtErYhtSsNC8bWnzmDwa8H1HVUAj7cjEG0xPDU7rtf0Xav2k/m3CZE+g8/nFWLWGM21vDoKaangWkE9ATghnS12xQg7mHMyudSZd+xH7Q8M62ZjYqcr/okruKwlCwe8+Z1eL5fRD0hkaqvVlLbXINA+pSGzrD1rzXnZkpitn/Co/vtRSy6zU1HEYmqky3X3dvP/JLzF03Buxad9n3y3tQ4O7W95fagyTlV6gCW0hIEHpIMMB352vYTDyeAkotwUVKezByflt5lpST25SOITKxhH0ZC+yMR8af08qDjnMgONSfscNZjpHNGMRjvD29qyLitYxz8ziZTXuHzI4x8lyd5nZl7x3H5WJhmePA4btmmIQPUJj5GMgIoRl9ziZPJTDHl8FOxO+t2NWhk7WO2eePqXH7KlMgkFh9bggJIekFJtStu1V2O/Eeo1UoW+e/2qi7CXDPlrz32f7MnZnZuOOm2rvB6Jre4CocP6o9CKOys5UJAYDhAbhYtS9EBGiAaHooAyLCi3bLFiODQrTaMKzCrvFa4bEWqI4Bm9KAOSMzjKhRw2qLHTR14lfNb30vEBvUwEzZU5zniPlgyEWnaVMVQGjrHgf65cDxdAF6M2eSiKcOpk0acUY3A6JnzhAVBvfWbMO6JpNyzqOcjWRpG37Op0wA3cfGGKpPegrJkt+6v5+zFY7DhCWhCe85inZNdi80U8e9mlC3TFhh9J4bvFfHWOZzEwzp69gqK27jrrR6rz0Mo9LksXXpIfEV1HZrSlixsvze7gxmzyjry6uyUJk3xhCpEBNWA9AKddbsod4EgoRhfL7QwVS0aGjLJu66Yzph8jS1PpPibf9osy1quLhJ8IRTLG3tej4DIrhcnvD0wQehTeXo0CZolyNSCclMtImcWpZDkwhJp6Y3l/HT2rKA9eq8iWcj4W0wWdHatDtrHJXaFY5wunt2l501yDndteg9O/GlpP1d++0adoe3+++7PV+vr7/XrXRLxtT2TLZ7+2D39jCMStuUyevdC1DnpArg1ejFr6cdq5peNmpK0N7rPTduQyMRorWssQSkM+rGFikEa5+X0ASJVdWroQvylKl0SK3jpA3HYtD2vC4d0hXTj0g8jov18xQo7FiM3huu54mhisvlguNyweXpUzguT+geK2XMNCBtNzidJ5pnH8CeUgOKWDw1+siUxbL5oJgIGs8sISAuTZkbLe+J733+KhwUONFuDqHJdbwDa1+n2XZtWb/bP+fzKuNXhtvv3a//KOi8p2dWQfcm7WEYlVKZXR91YNOkvrmybTOxEiIrC1clxMrqBYcRpggQqYTUxhIOIps4xOe0IQOaucoVYcKD95kL7v8jI4tJEGfGDnp8xfttTJGbC7IcaQO6QPrE5enioaGJJ/kA0gRn60A7IZ1ZR+ZMspTBZNSLe4CpPeFzJ9Ly4N0y1ySqEHZACKfqdFF188HkpaEBmiZ2cTLltOrxcS9iIu0zz1YKUuW6OSwWAJPC5g4s3LemhZNvrLHjfLzGfS9p2Gpz0nt9T3veExj3vq/v3oXFzvR850vtYRhVkXDJ4FMpg8FBlQHGwElg5Vn37I+qGaOwlDJG6JomfYwAMpRj77PP6UyKqwm5KlR0lR8TX8xvK3nKdLcsGk7WUJ2x08I8uMA5eIaO7TXlfPXe3Tt7sSR8z+2Nh3OshJ3eFcLdXbOiEL19Vn5Kfh5IwLUtYpyFeH1uVGRJgOB1nBIKBt5bS5dURiAtRBiseHF5DYAbD+w9u2+Hmjtjv2Qr1ufd2xIXtuhrNO/+8x4iuNcehlGBIsW1MNsYURCrMuQ9xwTh1L5ACdNyn2Ls5giiWKGZJl7zp9g3uzQNx0rr4LGHfGc4XqDgIVWiXvEhGNsKgtMP1fuBJhMTiv50AY4GORv65RIFuI0QpntXfVM4IS8dOKoQ7qU0e2DxtHLcbCySDR1hL6oLm6HDNKnb8WSUKOYmlqReibBudYv3+fpA87yZQC3u3Z3l+jrXEZ/FyiSLnVxzg8v3lRmq1uR9bNWevEufqoum3YXGPRv4Ja3Lz6qH+HUM+1CMGlJJJxqsaoHC0++2zJq2LUAtQLXDIsLLAoQMjiLP4aQ2VVbSRxEG4fk1BL0v5D1bh9BalCGe1cahoCAkFJTYXRN0bQAsBtqbFX7ro5cNALKk/DVpkG5e2yZZCtQ7GDFJEclTw9R2gNSzUskoJMiYw+LlzjXwQml+WlmM3d8XBFxhKN9TBav/RJmb+/MrYcfy83va8VbTpiDWWR2R5ptocTxmatSEp/nMymwveos9ilCvq4x8z069x8R7exhGNbuFMAJQPwPVSGSVoIp034fHskjb+7maAHB7sJE7YgHXGLQb2Q+BaTsetZHaN3Ss9y0hjxUF84QH82u5dqmwJ32qHAfHCHZnArbxoKPNCe2ZlE6iJkzm701kcSgRcUzOMYdbiIZlSeGx0omsMBEEVmAwk/R37aNEKVwXOOon81ZN5v1hCc+q7SsBVw1m7yAaMdrY7b39p3XUfRJzZLVHHqIFrg9guSSeeOP+Ckv+bzfP3rX5ONlHe75KIoG9T/eFCfD4jOp0pygGtSZhASnpq+SlHcTcyX1RazM7kE4mSeKk9PeUvSYSO1MElIQTKj0FR0GPEIJlxE9CxpS6RbIvzGr31pSzINCeO3/GGOiA2YLimTX+4Jq8cJRN4ouNh0Qg9biH2CdLOI2yIYIwd9YUS66JRkZXzAP8GEHPNOJ7AvnQcSS2j5aJDLuttv9d4SU9e/Sw23hWhr4h9rJmYhog/AXhAFSAB14n6UhoX8J98bi7SNrsqqyzTAujOZZyTe00wHxsJv5X4bIz7L32GIxqnoywW6LKfSH8YM7CiAGZkBI8tMX+Ckkn0HSiZpJ73VTN4H1oUNheT4Tkd0jLE9L1Ns8zjVw/MEqshEzAbKU+pR1m/Vo0lEPYMSfacdj799xTyUwe/uPRDXye/5KwVzPJYv8euoY7wjbkoc3Rb1iap1KwJlNJ6RMqIZa/fQoAdzYRJezEW+HhGMNrZaXmSgH4eiIPZnVNSueY3ZK2d6EW1IOqqxxmf6anr1PIlhfF2o6RIR6ezVrHtnt5X7KNgUdhVJjGc3r0Fm6juGbXEGnTFomK4mzYvHecBxHgHGd5HqW5b8RGekUZBiLhxZpphnSoLe1zgDtfaMPRKUXnEkuAcmtd4Gh/LplDZC3LqdvC7hL5cBicU+h9JuJAmgpkptiFourhFIfJ8MJqOhY7q6Fltf1gppzccA450rlx5gBRlLsKlN3Js0PLdSGLt5y2Z2Hq/e8xp821J6TwDFwKXRMwzcGRhwJ5Il8pnr7YKt7qlkzrdzJ4RXi7PXqvPb5GRdUSJWOoaM4JGLEXb6F9ZNJsIOHevuD1WZxF2pFAzVbKuj65O6cw5aoUgiCYimjw6FYb0MYzqK6FyLkLhiEbY7M5Fa3LQsA1nS37lAse0JWaCbSyEJq2amDOp/jc1/NNocBQq/VEYVMJleOmUyp2z5R3x/mvZcIW29gZCMicXj5rj4XmbqH0ylvp1DL2jTmDnpTr64kjzAlXWBoqLHXTzo116nANya4LbOyz0Eqde1v/TJpZQo3VBCna/x6CeCc0atVWNw6Cclk4jZz4Qvo7hKxOiRellyKOQxTJGrWkbHHokxCLoBeA8PzTYutNzYOpqH0xEYcG0x6a1KKu7V27NmfumjywnLG6MWQlzD2euHgRS74sGZdMsSA6Tc9tEwGaQGYmfUyd7mhx2B8css3rpgGjX3v/9uuRmUBVA9Ux83m7Hbv/vjMDhXL2BTR1CzkknUWcXMu6mZVhYUPw1LnVO2xmgBQTwbRr9fguu4lEsHuEX9e+3JpJH1/btBUASDh4cmf/rbZ05walL2AbeO8wq6rAhtwQhRFD9dg1NZZGOGT354boRpiENU2OmpeaknCanQ/JrrZNjgzPZ9qYSbwkGOrFtBn5e9UelbDDXmwNoLYrGrXWoSXzShl/FNdWOkXyxLn6M/6xh5p2801lefat9I/vq+Pef1/eU/7ZfOZc2O8S86W0P70bTB2ltotnzxpyGbE+kGTg5ZBqXftdnW91jIAGM7KPOwzeUxRf1x5Ho6Ksp9JuNH3pW61jLqqtCtqX0Djavtph8WwAiPNTFU1TKzSxHSM144b30A5KrchJpyDwz0uJFELdGEw4PhAMDmSWkwhTJgGLSlXpr9DBcIJ9vlfci3feWfBRg/gVZqFoX8A8yppb6Ljfls+2Z/lmaE8h1LDnLJke1QbLwYbnN9c512+3Q3cNuWtT/l6THlbzhrqnrkfxVTiNYKbTcg512FqyxqpXe9IPUtakrEWMTXNMIsii63h5S9wO219qj6NRQwBLaiBqNAANiibmMIFDNcC0bpO+7oTRtAN5bUxGvM+gstkW5tldjr8AYquXXe4MF2e3GgFUgkpiuzetgjgvB7mhHUDYrT4N24I5oc4ZmsFq//qcTdwlAD4XRbPYuToa5/zQg077ktvtIKkBqkYI2K7pZINm4gaZP3pO7brvyax9vAPbaxy8Ipy9esN9Zh7Ffk2GWs6MdQRFYVjHFnN+wzPGzGOyqIDnak8Fw65mTtGfsTIgx7GX09nH8FJ7I0YVkf+NiPx5EflPROT3isin5K1Wyo/7ls6nM0IhmGbBqUYIwibCqjBUJqzagxk61S6iM8Oq5duE0zu/OJEm7V7xoHcG/+OUc0Iu0H6x/pqnsYddGrCtOMooVMzzWBaQDMyxi8F12j9zKs7rQNQvLtcvTOG9YtG15lX7W2GMm9o+SEhI2GvvZAnXPFC5blyIagz+Xf0dko4u+OfLOqve9D36cUcIvY5hJYRJFsu7mXvHCmMMpNeXReoomXMdqtMqPfXln68joE5OutxTx7bbqXX8r2sfyagi8vUA/nkA36Kqfy+ADqvv+xYr5VetULKKyne7NF6kUGFs/zKfW/6uBFBrHHEP5D5XDB+MyXq21HqEtnmocO1XOhbc1lGNcRW9H9e3OEmtvNd/b9zaJ2RujnuHfGmrLZCWc6uIuOzSX6xaa9naJpZal27SpF2UZwu4tTA9umWAjl5ccNZ12tqOTOrnu13Lfzux7wTP9bC+h7EUCMl2HPFwZbvUKjJKWTu455fYFhExGNOPFmlJW7Wv9xxJde7uJmncaW8KfQ8AXyMiB4CvhZX//Ha8tUr5qQVtkUo2SCxCDsyk+7RK5apZqZzPsgcutlgtsoXy3Kn6QtEsCoZkEnuAf0dIFfV/EZDR4CvDP1VIFMs1lOEKx+uRjVU4xVoWeE1bKMa1zaeEhjAEUgtu0dlEqK1AVN63+bJMLGl5DlAVpiEcirOKSij6UmxRatTTNTA1+e4cu0cXu/1WmfKNCD2EhyGIrBRhnMm4djqW7LPJLCRSh9udAJYidnY20Ljbj3vo4KVrXtc+klFV9b8E8H+FVRr8cQD/X1X9d/EVVspfX4KK4KLxvFKRXojkPhGkRqXxlh5JMiSAhfgJA3VasiefSA1ImMf7aZvx3Bnba0oUsDqhksGRdmK8n0XOTDNHdlORymTsaiuNYQ4ndVuToYIIuRQmpFb0GQjmStifKX6LJpNSlpPhpehTrhcJr9pcAuQJA24Dc8zByHUu72ge9j/uK99Vpn0d4VdGtmtmzLk9sqRLQst8pyC3wgKczwLBy3EnFLIUmCGwy6LVfr7EtPsmknvtTaDvz4NpyW8E8AsB/CwR+fWvu+XOZzc9kHKkxU998YtxIzWBZQ50TDQMtYSGIYrZNEIOdTHoNaUdGzYRvydRbDDkHCMkbtUGAdUi95gErwFBE17WuK/G4tu1LZgF/m0SXMKx1OBVwmpoRCBjthRqETq5ycCSIB6zl/ivCCfOH1bCUQBoEgkP8aFDAFW1ufKx07nHIyvsdsntdrjd37nv5QwiKTBxYZzyPYAwN+pa7c+K3UOiN7CUgjxiooIQeCEESolYESILztc6HlVFQ4doSztX175UIVRRwJtoXODNoO8/CuBHVfWvqeoVwB8A8D/AV1gpX8uRFp/59KcD6s6ZMGRfYBLfrOlpZbJMM6XWDKLyybrnNKmfRZ0eXSeQnlCetBYaYtFYiEOmaHNa56q0di/rvPX00YtdHSBMcasHXaUASWkfe2yL3baPkx1S1UxsyAsSkoMCDmvO9WYLcp6GO4viOSUj6H4fsBDpPbt0t0FrI1yu2/jq/VVLA8nUFFK8PhCHjxW6wWxhhpwpjhxTOqW4XpGKyASR7ZS7QDg1VObjHJEF9Xr4Lh95gcg/COB3AfjvAfgZAP8qgB8E8N8E8FOq+t0i8p0APq2qv0VE/h4Avwdml/5CmKPpc/qaItwi8tMAfvi1HXk3288H8Nc/6U58DO39uD6+9t9S1V+wf/iRCQ+q+gMi8n0A/jSs8v2fAfA9AH423l6l/B9W1W/5ckbzLjQR+cH343p32iOP6yM16lelEw88QV9Jez+ud6s98rgeJzPpfXvf3rcX26Mw6vd80h34mNr7cb1b7WHH9RDQ9317396317dH0ajv2/v2vr2mvWfU9+19ewfaJ86oIvJtYrtsfsTjse9EE5FfJCL/gYh8Xmxn0W/yz9/6rqJPoolIF5E/IyJ/yP9+58clIj9XRL5PRP6Cr9s/9M6Ma8+L/Gr+g+3E+UsAfjGAJwB/DsAv+yT79GX0/esA/Ar//e8C8J8B+GUA/i8AvtM//04A/2f//Zf5+D6ApWP+JQD9kx7Ha8b3v4Ulrvwh//udHxds88j/0n9/AvBz35VxfdIa9R8A8COq+pdV9RnA74PlFT98U9UfV9U/7b//NIDPwzYffDve4q6iT6KJyGcB/JMAfkf5+J0el4j8HAD/MIDfCQCq+qyqfwPvyLg+aUb98nfaPGATkW8A8MsB/ADe5q6iT679SwB+C2o9k3d/XL8YwF8D8K84pP8dIvKz8I6M65Nm1DfaafPITUR+NoB/A8BvVtW/+bpL73z2cGMVkX8KwE+q6p9601vufPZw44Kly/4KAP+yqv5yAH8bXuzghfZQ4/qkGfWNdto8ahORC4xJf7eq/gH/+CvaVfQA7VcC+DUi8ldgpsg/IiL/Gt79cX0BwBdU9Qf87++DMe47Ma5PmlH/JIDPicg3isgTrITL93/CfXqjJrZX6XcC+Lyq/rby1fcD+A3++28A8G+Vz79DRD4QkW8E8DkAf+Kr1d83bar6Xar6WVX9Bth6/Puq+uvx7o/rvwLwYyLyS/2jXwXbOPJujOsBPHG/GuYx/UsA/oVPuj9fRr//hzAo9EMA/qz/+9UAPgPb2vcX/eenyz3/go/zhwH8E5/0GN5gjN+K9Pq+8+MC8M2wLZo/BODfBPDz3pVxvU8hfN/et3egfdLQ93173963N2jvGfV9e9/egfaeUd+39+0daO8Z9X17396B9p5R37f37R1o7xn1fXvf3oH2nlHft/ftHWj/PzRfYc5ITYb5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(contrast_img)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b886177700>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im_gray_th_otsu, cmap='gray', vmin=0, vmax=255)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b8965205e0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(resized, cmap='gray', vmin=0, vmax=255)\n"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}