DRONE CLASSIFIER .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "import sys\n",
    "import cv2 \n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "import random\n",
    "import pandas as pd\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense,Dropout,Activation, Flatten,Conv2D,MaxPooling2D\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_out = open('x_data_np','wb')\n",
    "pickle.dump(x_data_np,pickle_out)\n",
    "pickle_out.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_Temp = open('x_data_np','rb')\n",
    "x_data_np = pickle.load(X_Temp)\n",
    "\n",
    "Y_Temp = open('y_data_np','rb')\n",
    "y_data_np = pickle.load(Y_Temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data_np = x_data_np.reshape(-1, 50, 50, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(x_data_np, y_data_np, test_size=0.3,random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(150, (3, 3), input_shape=x_data_np.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(75, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(64))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "5/5 [==============================] - 1s 270ms/step - loss: 0.1919 - accuracy: 0.9538 - val_loss: 0.5197 - val_accuracy: 0.7679\n",
      "Epoch 2/4\n",
      "5/5 [==============================] - 1s 288ms/step - loss: 0.1682 - accuracy: 0.9692 - val_loss: 0.5734 - val_accuracy: 0.7857\n",
      "Epoch 3/4\n",
      "5/5 [==============================] - 1s 288ms/step - loss: 0.1244 - accuracy: 0.9692 - val_loss: 1.1735 - val_accuracy: 0.6250\n",
      "Epoch 4/4\n",
      "5/5 [==============================] - 1s 279ms/step - loss: 0.1969 - accuracy: 0.9077 - val_loss: 0.7360 - val_accuracy: 0.6964\n",
      "INFO:tensorflow:Assets written to: 64x3CNN.model\\assets\n"
     ]
    }
   ],
   "source": [
    "model.fit(X_Train, Y_Train, batch_size=30, epochs=4, validation_split=0.3)\n",
    "model.save('64x3CNN.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 53ms/step - loss: 0.3917 - accuracy: 0.8889\n",
      "Loss = 0.3916577100753784\n",
      "Test Accuracy = 0.8888888955116272\n"
     ]
    }
   ],
   "source": [
    "preds = model.evaluate(X_Test, Y_Test)\n",
    "print (\"Loss = \" + str(preds[0]))\n",
    "print (\"Test Accuracy = \" + str(preds[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare(filepath):\n",
    "    training_date = []\n",
    "    \n",
    "    img_array = cv2.imread(filepath,cv2.IMREAD_GRAYSCALE)\n",
    "    new_array = cv2.resize(img_array,(IMAGE_SIZE,IMAGE_SIZE))\n",
    "    new_image =  new_array.reshape(-1,IMAGE_SIZE,IMAGE_SIZE,1)\n",
    "    return new_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model('64x3CNN.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LET SEE WE CAN UPLOAD FROM USER \n"
     ]
    }
   ],
   "source": [
    "print(\"LET SEE WE CAN UPLOAD FROM USER \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2ad03551d68>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xWiiJBIESgGUfe/9UskK3TAUs+EiMlfix1H8qMGQwFGRAQVjKUlEg09IQ2rWBE0kQ7C/xZTY/p7am9f1qa6jaJjmhOT0zSE5YnvO7AdgRdvNso59f7FhBdV0v3c9pEIKOYZ9GX5YfjJ+DsU+w2KkyYgo0bYDEcHfjNNZkt7Imo2laQUnmKEHLfFBopJWthd+qFzhB/ra/KsecEIjU8Mg+C6wlANqt9+TSjf5ckYDolKJVvx4Auk1IVMzks6Ww5NAoMVmTvkuXokIWI/GEjneoBjkhUAhKMnvCCoKjRaJlQaTKl0SWTldSNR4PNQt8a+hC7tx4GqV1BfKDBilh3mhAtd9h4jU1Llm2kT+Y8xiLnRG2h108F2RQWIZtiS5V5/WlFwEwqJaDcNw06JT5VrKROeBt5MTnmBMCCSZ2/mkLfU7kHEoiAIkDx8RaQsWa1sKf9BtEu/9cFUTHLQQIAisJrIweh6BLTnaw9azCs6rlVAQ4L05XTQXB4SNx3gVWUzEhJSfyyH+9spxPPHw1C/4jw6KGARswcK6LXlPFH82BG/CRNT8nJwP6nVFyQnNudoA7G0vpVROcnRmOowaTZb1V46GxFERmWktztV9t6GThmBACok0lS3CFpCwExlrqJtjLMyNyQrR5di1aTHp5m9Mcvq4wuMKgbewXSBY2oqVxtO8SP6jPISci27RLenTJkE6pKJGNssns1HNL/AoJSRmtxuKipozsTi7CoyFQkvOSyFZdfMX4dMpcq5AmsIacUK2wWaRuywMO5e4PVyh61GRRgMKQfzKP120Zvsryu6+4kx6nytm5rbzrlt/DZKOmJBfkttAjNXc2FrPFn8v5hY28KueRFXuGDfcV8ksFecQxkSdw/tqc/eWPFwOTTkFtbSs7bERrlIhMAAW4saou45/1WMXTbZ8lySsL4kPtMr/doRhMK1xpv8/EgkKKpMjEIluZcNHPlW4RbU2r7VVSjTadJNqQ0C4ojoWLsW78VtiuagPmtTXJGDcNmtbE6bR796D/KsNDHm76rMlE31US3we4YdvFXNf9OAbJKneAx5sLKcgmg2EHO4MufjGwkn847ZvcXl/FA+OncG7HS3yoezMAA7o25bxTpnJc5QnsWXo69f7IV2BbEQFXiNhpmKjrtmVzmuS5U2Rdu7DYhxAUUBYhBvCtJEDGJoODjh2MFeOx1An26CzTfmHDVDu49d5HWR0d0rXWTp8VDhvCBgVhWeREO2qiDeyta06ianfGCVnbwgaela3R43ujaQPOy0bq+ZCu4Vnb0pA+2X87gTUMasF9jeXUTZazc1vJuJqP3f3rFDe43KB/i5rvckn/Rl5Xeoqkxn96YljKgXFMCgGYWnq6ILYZk6yuutV41hLYaOfuFXHIIEG0hxvj6IKY1Aai0F/0eyapXkvyAbAtU0FiGY8di7rNbFBYFBotDGMmz3CziBKGLtmgV/n0qWxLxZ4pHBWd19G3RwvCbZk+T/oBH9n4VtY/vojcoGTO5Tu5YdldvK002Oqwkyyy7brOEqdESeZaMXpgxiy+maibgKyKvtdEwIybBrtDw0q3yDpPI4WhrBpcUtjASrfIgB5BFkIaZ4X4WnH9kqf5yJxHKchSLEwazFP7Fj4pM3PMmAP337Zwr/dPX0TR6KeAABtPgFEtJyFM3d0jp+CeZkJC0PbxZzIZBuNCFgApTOv35KcrDGMmS81kMUhyIqAsPQoiZLEj4/Fazh7pqYE1dMT26tEaqqGEnJIuWzc+f7zzUm57YC3Lbom+qYklLsMvD3nlWS/y2p6nuLywmSXO5GJPnHzJZ6uaZssjvzfqxqdpw9bj2rWmb1c7WeYO4VmXLumxKywzZgqcndnJLZW1PDqxmLfN+yWvL1ZbnyNx2jZteFy3/p5t9mYOHBNC4Ly1WbvutkWt27/qokjUUmCKMIA9BQLsKRSmLP5pX0fFTipLiY+g3W+gsPSogLKQBFiGtWCHLjOmi/Q6E9HkHRHSKXUrZTnRDo6mYzChaQO2hU3mKtVS6+/1DA82lvMPP7qGhXcajCvwSxLHswR5QW2h4F1v/ynv63psnwk4B0K7H+HSJ9/AxA8WUL+4xl+eeysX5F7i7vqpAFRMjs3eXAB+b+6dLHUyrXDfiZzvfzg55oXAfbctnJK40U7igd+bZ7pdJd2bMJhyX/uxOBSZMF1JTzSF6aHHdqdhe1gxJ8K4RNbwWLMfJUysHTTokk16pKYsHfJiqr/gSBNFBsSUSMVOXScjBPPihT1ufD499Eq+e8dFzHlcIEOLzgiEgfywptanGFljuOzlz3Dj4jtwheJpv8GZmf3b5lXj4cVdejcFVa74yYdZ/ZkxGBlHL+2j+7Pbubz7eTzrck7uJb43eh67vA5+Z/5dvCJXmeKLgD19MCl7ckwLgaSKcG9CYH/dZJIaA5jZ0XcwpkIiDDw7cyppYg6MmQwaETelUGgrW7UNi52xliPRs26rhVZOBigMF8XRsaOpCWhrmDAeWeG0FtGoriPj8Gyisn9udBmfv+cqup5wcWsWo0DnBSK0IKB6WY3/uOifDkgAtL/36rvey/JPhsiXdiI6OzCFHJVVnVzzl3eyOreDYV1igzePHqfG9eUnON3NTvm+mjaYMs04LQzbO8e0EGgPER5uZtISYFIwjBmDKyAjBG4cdjRx9MFvizIkAmK6prA1LBBYh7L06JI+Etit82wPuzk9s5tabE50SZ+ysHgWdugCg7qDmslSlg3mqQp9qkFPm0p+oCQh1b1pSQf6GtFnm/x+ksYaBjul7PfW2in8ZPhMnv7OasI85IYtfocgP2gZvEiz6XU3th7bHlVoj0S8FFb54KY3MfR/ltHx/UeR+RzmlEVsvaaTs69/jlWl3ZySHaRL1ZEYTnWHW8JlZ1hlgXPidfw9EhySEBBCfAW4Hhiw1p4VH+shmjuwjKh5yFustaMiajf8eeBaoA68x1r7yL5efzaFQDI3fkrvgbaLPUklHTce9TjiAJCNBcNMmkK7IKgZyXwFNWtYH3QAcJo7Qa/Kcr8XbfWRQ3HyWQqLFIbAKgLr4FuFQeKKkLL0KIuAojRTHHDttC/apIHH3rSoA2FvQqD9/vbXHtA1tLU82JzHz8bP5M5tpyF/0k3Pc02kr9lxSYHv3vC3KGEZ1HnWZvyW3f6/Bs/g+/90OfO/+jgil0WUS+y6eiHnv+8x3j7nAS7PT1aPejbEWDul7j/l4DlUIXApUAW+1iYEPgmMWGs/LoT4M6DbWvsRIcS1wB8QCYELgc9bay/c1+vPphBI/Anti3n6yOvkvqRqMalZCCyUpdjj+e1mQ90KXCxFKRgzUaUjwPPBHC7MjhJgqRhLxbh4sVaQ9D1o76pjrMS3ioBIMGgrWZEZwMWQE4aiFJSEO8XunW7XR+c5dfc+EPYnBGZquDGka9StZYlT4gtji7n5fVcgQ4MuRJ/R3T7Gsm/sJDCKxwYXknFCBh/t45Q/vx+kQq1aTmV1D6PvqPJP5/wrq9xGqw/AXFVs5SiUZYascNNU7cPAISULWWvvEkIsm3b4dcDl8e9fBe4APhIf/5qNpMs6IUSXEGKBtXbnwZ36oZG0uYryBGJvPJMLf1A3yQhBQSgKIgNictx0IOxkk6wZEpESylLgCkndRH3z5itY5Q5TsVAximGTpx6r/j3xyK2KdeMxXCE5EeLK6NwiH4KLZ1w2Bz2tXggdpkmn9CnboNVscyYBkIzJPtTwY1JwA8RpxB6eruHGrzdXFakbn6rxGA8LiHVPELz6XNxRD7lpG3qiSlPn+cyin/KyBz6EU5V0bIL6Gy7EaRi2vdrhr9/wdd5SGifKBS1yr2d4ZS4yIUoii5KT556kaB+K2ZMyM4eSLNSXLGxr7U4hxLz4+EJga9vjtsXHpgiBIzV3oL1pZDTzZjJ5CKBP5VuLvm6jBZoVkpLM4grFqK6j2VMYJChrqRjICU2n1LgCahYGTZZnmgtZ5g7Rp6oY2YgSjUSUjKSw9KnqZBaiiXb4nAjplXUyyrBLR045jWTCZhkL8/Ht6BzWZiYotGkH0eDPDK7duyp/oFSMpiAmw3ft9n3TBmhrJqf16CwymyUsKoTNoi9cgdCWO+5zeeu/LsX9swb0wbxPbWX9n56OXuTTea/Dn9/6dr534Qu8s+8+rit4vDInWz6cROtoD6OmTr/ZYTZW30zu9D1sDmvtjcCNEJkDs3AeLdoXQXujkOQ+Vyiy1mkV1QRxjbvGRjvfXlKODbBAZQjiFGeMz83VVXx54ysZ3tJNaaOidk6Dt5/9EL/W8Tg90qMgItNBCs22cDJLEaEJrKJmM9RsvKgxrfTkxIRI/AcA6wOfsvTplFW64rBj8nkSUwEOThD0yMmeCtPNjvbFOKRrfPOuV7BSP0Lp6QF0d5FM3ce6iu6nu1n/Jy7938jQ6FEsvM3nuws/T4Cmfpnmusfex3PfPJ2PDa/kg1eGPHn1P7S6DrX/fZo2QKWzc2eNQxECuxM1P24zPhAf3wa0G/iLgB2H8D6HnfYF0d5dVhHtNkmM3LOWTgFSJJ2ILKqVPhxpB8lueEutwI3bL+OZJ5aQ36nI5C3hxRO89dQn+P2e+1nglBg3gnEDO7Rg2OQpCh9XGAoiRAlLYCUVk2HMFKiZLHPU5HhuN27NXW7zZwybIhMmx7AM6BBNyrJOQUAu1mQOpfy5XeVOMhwT7Sh5rZ1hlVf87EOs/sIg4cvPAF8jN+/CjI3zwpfOwn0J5t+UZeC/1Lnp5Tey1BFRIZIqMKKrPHL+txg/t8E3Jk7jE3dex3X/9b+x9TWSr1x/I72qxmmuM2VmYOoXmB0ORQjcArwb+Hj88+a24x8UQnyTyDE4frT8AQdCsmsmHunE5kyq0dqzEZPwIdASBgO6xicGLuF7D51HbodLwYN6v+Hcc9fzPxffymJl0AjWeRrPFlnsTNCvLL2qhh9HIzwrqZuoQaorNPPVBNKx5IRuJSgFVu7RFUlho668YZ4RYWL/QjTR5+Js0jzj4ATBtrBBn8pQkNEUHrfNNFAi6sG/wCmhRh30ixtx1GmYUo7aRacw+r4Kc27OUtwd8tG/+5d4km9kytSNz6iuc4pbYtw0GDead3Rs4AOvu5Ft11X57OCl3PDNG/B7NKtWbeejy37AxVlN3UZRnk6RFgkdbg5ICAghvkHkBJwrhNgG/CXR4v+2EOJ9wEvAm+OH/5AoMrCeKET43kM9yXHTQCFiZ1gyvNK06vQLbdl3B+M0mlR7k575k19LYps+3PQ5L5vhXs+QEyHnZKLHXPn0G9m1bgGFhkBY8NY0+OJFX+PKvGZIa8biJqlLnQaebWCAwTha0Kt86lZRsw4ZDD3Sx40LnTwr6JIwrGHY5DFWUpBNCjJAG8GwLnFmZoBBnacgA17059Gr6mwOegms4qF6KeqBoOrMd8ZZ6kzQ72RbanV7um4STUi+ubrxOcUt8YTvsSZDS0vaEESaSVkKskJyU7WDnicF6oyVsGOAne9ZTePlNRb/XQFhm4QFxanuKFDihaDGSjca7JFoT50yT2ebTFrklPj0gkf4+Hse3ONvlPyNf5Vy5ZQD44RKFkri/Qb2W/s+/XkwKQySSTPTa9OTfvZV47Fbh/zz6MW8q3sdD3pLeKCynLwK+KO591IQii2h5cxMnp1hlYoVFIQlIwRe/H27QN1GrdFyQlG3mpqxKBEJo5p16JFhfH4wblwmbBaFoUd6dEnDk343D9WXU1BN7hhaxXO756E3lZABSF8QFi1hp8bp8CmXGizqHGdVeTfLckP8TudGnvJtq2Nv0wZsDAJWZ6LPN2ZCFjmJIMjxWLPJsCmQEwGBddjgz+Mzz1zJsj+uontKvPjOMsoTLLuljjNco7mwE+f2h9n4yYu59+1/yzxVnDIP4ED+HnsjFQIHxwmRMZhMkgVaM+Zhat54kkF3MJ7kcdOgbjQ9KruHHZpULiYX8etfvJpn7l1OMD9g1bKd3Lzq+63768anboNWHB2iTDfPQo9S8XtpakbSFYcGR4xCI+iRIYpIQCxQGYaMz5hxKIqQshRUjOXFYA4b/Hn83ZOvxmwq4lYFMoBGn8FdVGNN/w5Wlga4uPQiReHzd9uv5PkfrWDJj8YQW3ZCxqV60TK2vTHkN2vokrEAACAASURBVM56gv/Vd3cUkhPRbn+qW2JTUOUb4+dxWelZflI5m7rO8M6e+9kVltkRdkf1EKrBB3/+TkRTMv9eQdfDu2FohMoVp1P8wWM0XruW//HZf+a1hYCXwupek5+mkwqB2eG4FgLtF8VMmXF7q8Y7UNVxpvz5cdPgcT/PuRlvSpLMvZ7hQ8+8jeEt3eTnVwme7wAJ77n2dt7U+QinOvmW02zMSPodQd1oxoxkjrJ0yxxNG+BZTU4oKiakEicclWOtoGJCxoxkty5Rlh6LnYCCUDzuZ/jHXVdw77OnkdnhIkOB1x9w9ulbeVPfQ1yU34LC4ltJ3Tq88+Hfpus7Jbof3BV9zs4iwlpEvYloNAm3bkP19jJ03Wnc8JH/4IL8ZurGZXsYdfu9ILeDB71+7q6s5Lfn3MOPq2fSo2rMcarcPbGSs4vbuH/iVLa+ox/9wgbUaadgukv43VkqC10WvGcTt6y4rfV9dsr8AfkljmYn5BOZ41oIVI23x6CQ9lTf9h0mimHb1hyBAxEC04VF1XitcBtE8+vOci1NG/KF0XO5/9pTGbxyCc03jKFu64pKbS+b4FPnfJdbRl7G0twIH537PBBd0C8EHr3KMlcVW8MwFzngohiJ05p7YuGzU/sM6jwawXKnTgBsDDr496GL+dnTq8m9lME4Fn+u5p+v+hJrM1VGTLRongvm8qmN17Br3QKW3lpF1gOEtZiMAxJkIwA/ACEQno8t5RGeD6EGa/G/Kvnp6v/k4WY0C3CDP497xldwdfdT3Fc5jW63jraS7V4X7513NzePnsstz6+hu7PG3EKNj5/yPQZ1kccbS/lA13MUZIZtYZU+lccViqrxqFu93xZgqRCYHY5rIVA3/h6NOXbqBlvDAsO6xOmZQVa6U5NZDrS+vWq8GevRk/folJNFPUO6xgc2v47aVVXEKYsJ5pYYX55D+ZYwL/DLggX3VRg+u8TQJT5vOedhPtBzD0ucAgO6Tlk6SCRDxqcgoizDJNqg4/Riz0rK0rBARc/5xODl3PzEWrJbM1gBwZImb17zCH8y916e8sv0qhqbw27+6oXrCW7uxa1aKksk3S9q3IrGqYUIbVF1H1lpYJXEFrIgBPbp9cilC2G8inAUzVX9bHi3YOnCYeYVKlzR8xxSWD75yNXIl3JkxgWdGzQ7L4F3XnYPD48u4VPLbmKxIynJHOOmwZDWnOqW+EndjaMC0TzAJU4+VeOPMsdVj8HpTK8Td4VirsygVZ2y9FnXWMqgHmCpU6e3zZ4/EEoyR2CjEFRSPfj1iTP5/N2vZcEvJDuv0Lzl5Q9yZn4bSszh4aeXs/wVmsxIA/eZLTgLV6IzArdmyVQMzTk5nIZlxZc033n/uZx38SZ61W6eDzrIiYBVbpO++By1NSgZdffZFjZwBZziRkM0NoQN3vrY7zCxsYv8iKSxOOA1a5/hfb13cVYmoCSL9DsVvjj8Km775sXMfSIg62gmljlkKiAMIARh0cFkJNJ3yQFyrIpoNKP7XrYKsXUQC1jfJ7t+N2f8lcvQKxew9MNbeLiyjLt+sobTbqlSX6ipLlAMvtHD2VTg6z++lPdeczt161CKh4COGMOpbolRXee1hUkH4DInmv03qutpMdAxyHEhBKrGwxVqyu6eFQ79jiCwmq1OlZ9VziInAy4sbGBtpkpWOIwYv9U0c18M6UarPPWN669i1/85lTPu2YqZ00HHTS/yOA5Prng19eVdLMoIsrurhF15nPm9aFeQqRh0RmCVQHma8VNd3voXd/DezmdjOzjDae4ERSHZGDpUTJbL81ETjwWqQN36jBuXfieKBnx1Yimfe/oKgg1lbN6QPX+ED628izeX1qOxrA8cJizc8LU/YemtE/SVPQZeliM/ZCns1lgpsBKMKzCOJMwLHE9gM21/bmtRI1WQEuvVkHN7IAih6TPnl4P87CsXc8l7HuR/vuXbfOz0a2nuFuTmV8g+0MGcZwKaHYrz37iR87KZVvivWzElAjBuopkOCkFJ5ChIlxCdZv8dYxwXQmAmdb09w++6gsd1hWcYNw2e9TN8r7qcnUEXdZ3hb/qeYFtYbQmDx5pNzslm+X6txKc2vJbsZ3rI3vUU1lrkacsYXduNCiy1tf2I0OIWzmTD70kW3Jyh4471BGcuYfXXXuQ/f3IhvY9kOeP3n+Lq7qdYkdnNandSa9kWVimJQsu+7VORSXFWxgBNmha2hXmGdMBcpVnkGB5q9vD1wQvZXe/g4kWbOfeMLby3YwObQk1OaLpViZ1hle26kzPcIZy1Ywzu6MTvEJS2Gwq7g2jhZySqGb2vlRLHgzAnqCwrUAbkaBVRa2BzUamz8AOso6CQg2bko5CBZaBZ5qLeLXz2Zd/GFSHzVZXn1vTxvLeAuW6Fjf48vqnr9LsANfqUnBICLIksTRtiMAfc8CPNCjzyHBdCYH8kF06nzHNRDpa7G7jP6+OpxiLesflyrp/zOG8rjzKgaxQkQJa/+PK7WPrVjdTX9BBesJrMiztgeIziziIiMOicQoaWsOhQfDRLbR6E16ygY2OD3c0OVFOw40rLXYvvjC/aqSZLb9tQDYiiGlH2nmxVM16QFTStoGklFWu4v3Ya97xwGgi4aO0mFrvD1G3AUkdRMZFgGTEO52aG0MCTF36dbedVub2+jL965Hq6f5KntCMgv6uBNzeHVQKdlRgHsmNRhKXen6dU9/Hnd+I8vgHRUSY8dQFqpEbQU2DDm7t48yUP8P5S5NXXCC7OjfF84LA57GahM8qKjgE+u+sqHh/oZ3RXB/ktLtkxqM+3sLLGf170j625jknoEWhlZrabd+3zGCarPFMhcCQ5IYRA0jU3CR/OU0V+vTDBdYWnkXOe4cM7L+QX4w5v6HmEpc4oT/sNmj0Wf+UCavNd5j5UwXaWMYUs2S0j6J4SJqtwH3ye8evPJsxDc46lulTQ875BejI1CjstZ79mAw3rt8qSjY0KjqI+/pO73mQV46QgSM67IDJgoCAFb+l8iA0rehlpFri4uJ5rCk3GjWTMhBRF1LW4KEIqFr4w/Cp+uPkMOvIefYUKP3jlF+i/VPEH267i7rvPovdRS9czE0ys7GBiqaRvIMDvchhZrShuEGQ27ML2zYVag+acLIN/ovnrNd+h3xmlVzY5xS3xrF9nzGSoGEsQVyZ+efBSfvbM6XQ8lsU64PRZGksCvBWG7jkVzu/byrrGUrpUnYXOGP2qTo/K4iSpztOrMIWEVjQgXfxHg+MiOrA/2lXIJKcgEQhJxtsttQKf2HANK7sGWZIfIStCvvTIq1jxnoejtFchECPj2J5OzAubEK7Dc58+i7dftI4zC9t5ttHPpaXnCFB85Eu/Te00n03XfumAIxHtYa9k5xs3XqsuP5lcNG4aPNosstydYFBnKMuAunE4J5ulaQMufvgdyFt66NjsowKDCC1B2aXe63DqB57jq8t+xm7d4AGvn5sGz+PFL51O56YmYyuy5IcNxU1VZDNAeD5+fxfr3+XypSu/Qr+qsFuX6FVRtuSYybLYqbMjzPNvw6/gpxtXoV8qIkMIy4bc/Bqn9Q5xbe+TnJ3bimddaiZLRmhqJhs3Y5W4QjNHVel3KvQr1XLEJvUMqep/5DiuQ4T7Y29JQYkdmiSq/N+xhfzTP7yOBXcMsfpf1zM/O86dQyvZfNspLLi3QWb7KAOXLeCU973Amo7tnJIdpCwbbA3moDD8bHg1T92+kqBsePwtn2uVvU4YDykEORF3DooHp073ZUyPf1dtsxV+bHeobQqqVKxDv9Ktlt4PNRXvuO0DLLhTkh3XhHmJ8g3ueIDJKjK7a4yu7eLf/+ZvyQpaUZJRXedblRV84t5rmbPOQRgwLoyuNfz2JXfymx2PMGgKLHeq9KosO8Jma+LShze+mZd+sZTsCDS7AAHNuYYVa7by0WU/YJlTxbOCrWEHw7pETgR0qTqecQmIeiRoBBmh427LDQoipEua1ii5rHCmhH9TZo8TWgjsy5mUCIhtYZXLv/3HnPrdOqx7ghe+eAF//MrbWOwOs9AZ46ax8/nWL15BeZMkuHyctfN3kFUhL471suvpeTg1QXNByO9eeBcfnfs8A7qGi5gxFz5JZJppfNf0WvmZPkPd+ARodmvDmMnQK5tsDDv5o8/dgFuzeHMExZ2G3IhGZyU6E7UDz1Q0f/V/b6RHejTjLscXZCON6KWw2qpJqBuX87JRpWCvcijJHJuCKiMmw7Au8mhjGV/95lUIA24VTAaqazzeec4DvKvrAU51S2wIqjztz8OzLnNUlV5Vw1hBzbqMmQIu0fDQDLrVC6FmMwTWod8ZRWEpiJCiNJTbmrikzB7HdZ7A/mhfTEnvu4IQzFXFVv17WSr6z97NW667n8driyk3NvFScw7GSjJCszK/ix/95qe5r3EK3955Pg9tXUwx77O4a4zPvPHvWe74rUU9oGt0xr3vZsIVisJe7msfOgJ7Osu2hVUqRsbj0QTLHZ+6hYuzDSZWaMobFdkRi18SGOVQGAohbnyy6Q0ShWXcZDkvG4U+lSjxtN9g2JQ4N+NRMSFlB14ILD0SRkzIoK6SE/Ci38fHnr4W80gnJgMyhPFzm7z9nAd5S9eD9KmAgCiDsigU52QHyImonmG7LlE32XjYit9qrpp0QXJFSFkYjJWt2oOCbBLgIWWItH5r0nPKkeWEEALtTN99kwVXEBl+ftZ3ATAdk93P/mTnK5DC0OtM8NPa6VR1jnf1388Fy7eyzCm0FSO5rem8+xIACQWZ2euu3/57UiffTk4YepWDZzV1a5krM4wYnxVnbmfXhiUUhjX1OVH0wqlp3ImQ7Zfm+cNLfwjAmoxm1AQoIRjQNXoV9Kkano0ajgTW0Kcs20KX1ZkozPqhnefzo9suwDigXPC7DGes3cKfLvkRp7s1KsYyZiRKWIoixBUGzwpqRgCCXlnHyCgvwGtvbxb7BqIZjgYEaCvxcNFGEFhFxUQ9EDIESGFZk5lsLRbGbV3b/S7Tw41Jb8UAvc+W7Wn4cWZOCHNgX8zkkGvvNrwl9Hms2c8d46t5YrifWjPDvFKV1/Q9yxXFZzkrI/a44NrV9n1NvdmbEJiJAV3j7sYCnvcWsCq3k6sLUaOmrWHUbfgUN9rR/2jjm9jwyyWUtgmMC7VFhjPO38zr5j3GebktLHU0L4Yui1UzOodp75OcUVlIpBB0yjyvfvp1bHu0P8oytBDMC/iDC2/nsuJzrHZhxPjs1pmWPa/i3b81pHXagJZmLATah7gmgmDK95P0hoiHsiT3RxObfHpkOKVt2vRworZ2j1Hw+ys2O5k5aHNgLzMHPgX8OuADG4D3WmvH4o7EzwLPx09fZ639wGH5BIeBmWL1K90iLtsJOhwa2uWBbUt5YfN8to52cf+85fzGvMe5ILelNfyiaptgo4YYv8rYq5mKYhrWJ7AGGc83qJkM940s52sDL+cjgcPvrb2T68tP0iUl6zxNQQpuO/0HDKyo8fWJMzk3v5nN/lzmO+O8thDV+Y+akF7ZJNfub5hhHiNEn+F9L72KHev6sTlAgFpe5S/X/JjXFDbSp/LRQsKPhqdIgURQt0nj1fh1p3WVdGMzQGJb49oQUWdEYyU5EWVGtjolCaZMb0oiDWMyMi3KskFZWMrSaSshV0S9oKZuYokQiEKRUfgxFQT7Zr+awF5mDrwWuN1aGwohPgEQzxxYBtyaPO5AmU1NIGEmjQCgapqtfPZtYZWvjZ3HD3ecydBEESktly9dz8vLG7ksv5FFzmRnYjfufrs/Z9a+NJHAajaGkfq8wpnsRfCo7/CiP5+/efTXCH3Fgr4x/vvyn3F9cZiK8albS5/KMqibLHKiXP1dGpY6Dk8Fgpdn3VYXoOTyV2JqFH5Iu9xaWcuX112CaChUr4e1go+d/33eVh7lsWaTHhVQEILAWlwx2fxkUEvKUsefL/lsk/g26f7UrglM/t6uEUzXAiDqp5gMZklGwRdkk6IIyQnNIid7UObAyc6hDh9Zxl4WtxDiDcCbrLW/dSwLAdj7gmyfZefZkB2h5b7Gch6uLot6/FlJX3aCV5Ve4NJcZcrMvgPplJOosYkKmwiBkswxpGsM6qgVucLGnvRoYSxy4PnA4V+GLuH2TSuwVvCqpRt5Z+99nJWp8FCzhy5Z56Kc4i4P1maiNmway7jRkwIA4tLq5Lbg5toy/vd3fpOgw2I7AhYuGOWLp/8bqzMFft5QrM1M4Mb+g7q1aAs5AW4sFPY2xFXbtunPbeaCaVv8yedj2uNarxfnF7jxKHiNoG5cajYTRSNkjU7ZZI6ylIR7wCHGk90nMJtC4D+Bb1lr/y1+3NPAC8AE8BfW2rv39/pHSgjAnmp5u7NpRDfplJkpnvp7Got5qrGIzfU55FXAmaXtXJDfyFmZ5gHvOomDS1tLgMZYi8byfJBv2b5lqeLzMK0ZiBUbCYReZcgKyY/r8/mnly5j+0gnmUzIEy//Bus8Tc1mWOGOkxPRlKIR49MlHTSWwJo47m/jHorRorv6Rx/GHVMEc0K65lV492nruLb0NKc6eRrWx7O6JVA8azFELdFyQkaDVqze7zDX6QIh8R1khGndDpAEVkbmAAKNpCj81sBXKeweWoRnI6GtMBRFQKcMWg1Z0jHle2dWhIAQ4n8A5wNvtNZaIUQWKFlrh4UQ5wHfB8601k7M8Jrtw0fO2/TQsl/5Qx0s+4rVN22IEmKKN3pA13jS7+BJbzEbG71kZciVHc9wVb5xwDtQ+0yDAIu2lrkqT9U0qVizx07rxX0FO2VAgMCzin6l6ZZ57vYc/n3oYnoyNT4272Ge8DXnZTM84XvsCsssc5NpyIqKyTGsS4yE0f+qztI0DreuPwujJblHCtTWeGx8zVeAqGJzYwhFESIBV0AuNgU0tiWkckLsMeK99Xvb8Wjy81Rh0K4JJFpA+0IviLCV5+CjUETNXcsyICcsNRN1TqrZDMbKVjjSFZrFqomKh8u6yUyJdGgJMAtCQAjxbuADwJXW2vpenncH8MfW2of29fpHUhNIaG9JNqRrqGmJP3XjYzDUrZ4yA3BA19gROsxVwQGVKSfvFaLxbNiadaiZOglZIVo1BU0bUrOGnjg0mBGCeaqItoaXwjoBgl4pePUn/5gvfPgfKEufQR21M68karNxqdssu4JOdgZdbGt0s9srM+bl8bXCVZrdIx2c3r+biWaOjqzHa3qf5eriM/Q7UdQg6ZWo4gUVWEPFGmpG0qtmdjbubYBruyDYGnYghSGDJidCskKTEYasiEyXLWEeKQw5oVsaQSI8AqtapgJEwsNrS0Qqywa52HfQKfUeRUwnM4c1WUgIcQ3R3MHL2gWAEKKXaEipFkIsB1YAGw/ynGeV9otipsy+ZNFPX+bzVJF5CiCqEtyfnamtoWF96lbjt2YW0NpJAyy0Fs7kHpoRgoDoIjbWMqRrGCJtIYelZi3dv7Gdd/7gv/Kd6/+eovB5srmAomwyrEsUZZMJnaNusoyHeYabRUa9PIFWKGkYqRRZNHeMqp9l644epGt4Q9+jrHRz3OG5dMkGKp7A5AoTLyxBQQg6HcG4ibz9ubiDcsUoylKzQOV5KWzg2yjhKWkyIqXgzsYcPr/lNbz0yEJyQwK/w3Ll1Y/yyQV3UDGGSqwNdMpm63swUxyGZsrinzyu6RJRjsL2sCv+/jSDIqBLNpir6vS0mXntfxvYcxjNvjgRsxoPJEQ408yBPydaBT+NJpG3QoGXAv+fECIkuqI/YK0dmaVzP+ZJsgGTaUWZuILuV9mTEv9BtJtGGkSyo76ydyPbH+7nYW8Zl+TXs8G65PDJiYCayeLZDCNhkR2NTka8An7oYCyE2mF+1wTaSLbt6iZTCPjTNT/h10sb2K4tl+cKDGh/jxyDRIh5VtMXl0onE5skTQILo/EswcTBOGYanJctsM7TfOi2d1LYrpBli/Sj7kd56cdFRXXq1uAK8JjMPZBYTBwhgEnzod1XkDgPAZY4o1MiED6S3ToT92H06JKJMNvTf9BuJp5MOQb7FQLW2rfPcPjLe3nsTcBNh3pSxztJNGDUeLFdLcmJJIHGtvoKTo9xT2e6AAiI1OogvsCv73iM7zYv4bHqEi4vvEgtTttVwlAJc3jWpaqzjDcjE8BR0bL2fJeM1GwY7KZQbvLbK+/nTeVNdMpJjSjptJSMJfesxmtzLo7oKBfBkZHN3auSmYWSrNLcVO3givwuCtLlWb/Ob93y3ylulwQlyA4LaosM/Wfv5r/0rAMy7NIAkmwcEZgSMGj7mhKhAETDXZMHxo/JimTyUmQ+eNahbl086+JbRVE2KQqfTtmkS1ZbuQdJ7sjJ6D844dKGjzQz7RZJJKBmLK4gVqtV7LCCgMhBuD+mC4DARjH4AImxgrL0CQuWiSDHLl1EYaiZDDkZJQ5F76txlSajNFknJNCKpnDYNDCHznKdP1xxO+/qGGJImxnbgicj0CUhimhcu7ZRCnFRGnTcQgwgJ1TUtl1kuLowQEkW+NzoMm78+rXkDPidlsyEoDHfctmrnuJ/L/wx81SRbWGVus3Qp3zqcft1GX/26DtuW+zEO/+0LEUlIikwYnIoLDkRIoXFjQuYkv8KGw191RkGjaYofLpkk04ponqPmcbpMinYT0pzIOVXIzEBDmSR74/pAiDx+CfhtOGwhJ4T0JOpsdGfR05GZoAxkqwMUBg6nQYdrkcjdGmGDo3ApRk4ZHMBf736Zq4pRPb3XFVk3DRmHBKihCSL0za2zNApo7bu48ZnWEcrpyxDtLTsCCw5YfhWZT5f/udrsUVIzHydgTdedT+/P+ce5sXt0nbrDL3Kp1dledY39KioS3Gy3LRlilCIzsm2FnX0mOgcPOviCo3EkCNxIIYtgajieZOBVXjGxQgZT4MOyImAvtgIShy1iRCM2tmdmKRC4DCirZkSBizKKEFHCoHBYOxUc2D/rxf5AHSsAXhx2CzJpNsRdKOymg7Ho6Lz9DqTkdgO2cC3iqwMKLsew16RiSCHNoJCNuDjq2/i4myD9rZo7bMBpzf+mLoIVGvH7JQQjUgVuHE/haLU9EiHM7Pb+dQHvsy/DVzMfY+uIjug+Pi7/4WLcoOURKaV1dirfCRQNwFSSBKFvOWTEK13jb+X6OdM2kKvqrUEQ/QaiQ/BoGxU0uyKMC5zNnHegcOgLuJZl0FdxxVRT8eyiFKlyzJzQrc8S4XAYSTJCgyw+NbSp6I04wCNZ3Xrgv2VHIPx/yDevQLr4Fk3GlnmVNEVl4Z26clXmTB5csInI6Ja/okwSmbqcht05+oIYVlQmGBteStX5jUDOqDAntmPSaejZKLxTBGQpKOSg2pFV+rGZ1Oo+cyua7j79rMJOwxLV+3iL5bfyt//xg8ZMaaVUhylXkNPnNQ0pDUZYaMxbEkLsrZoyhQn5V78BRA93zBpOkWTnk2c6KSpGzslG1Ei0OhIOKCn9EIIhI9nQ+q2SU5EgmqmSNLxTioEDjNJsY5h0p421tCEKSFCV+zF+JzxNaPc+3bbNrAOp7qDOOOKsaDAQmeUx7wldLk1ciLAIGkaF2Mlc90KJi+Yn5vgqs6nuK7gMaRrrdyDhvXpVoU9mqG0e8vbK/gAJJIQ3eodCPBsAD+rruHOe8+ivE3g9Sq21fv5b7+4gevffB8f7b2fksjSsFFEAF2nYqJi4U4pWq3WqjY2B+LCquQ70633pvUdK9GuGUSPS7Iik6hCJARsnPikY4ehomKjy98Vmi7ZwBWGMTPZINazDp51GGmzQ+aegDZBKgQOEwO6xqCWFISmRylKQjGqJ3OooqSg6Pdkd2/fW2fyIOzWbmvRR6W4sbMOjSs0/8/m19P9DFzx+mcZ0GVcofGMy4gp0etUKMgm4zqPp3OcU3yJS/JbWKgKNK2esqPl47Hfrpjc1ZOOTEO61jqWXP+TU5sUTRswoT26VYHNwVxu/PmVmILB65V4i30yu1ykD7esP5v3dN/HSjeqmRjVdWqxkIlCdm5LayrFHvoAjcG2NKsk6Sg3zTz4/9s79yDJrrOw/75z+3b39MzszO7MvrWSVk9LQliWhZAtbBJsR8gQBIGAXQGbR0wCpjAOTsVAUnGKopJQtis4UA4iNthgY152LAcLjIyNgvyUQZZlvVayXqtd7Xtme6anu2/f8+WPc8/t2709M72PUc/snl/V1HSfvt19um+f737ne8anZDE6FyK5p6D7HafqshtrWUAR9KY8+wxHf7+YAp1iuLdJltmY5JmNPtV5tdqJy/XMHDVBCJwlfrFsi8apyBIHOoYDHWXStBlfJp23n+Uj7qTnti2E21oM++enqArcfex6bpp6mknTpJlFzv3dyavZXZnjisohdsUnuLRUMPjp4B9kMdGpbl0lpdloPHcTer+6/+97Nm6O3KL99f/547zkLw8wf+N2Dt2kmEpK+WQZBJpzVRpa4ru/8SPsGD/Jm3Z8gdtq8xhcx2eL5VDaZt5GXFTKFm/exl2IxeUxIJAMYVM5ZQvhx0VXjEE45bZYKBRFaWpMkxKRVTAdUtuhIenAUnP+O/YalBcQ6y2RKQiBsySWiERTTtgmR1IDCFsjy7ZoItcElsvnL+KNgP3H9GsBPuc+RVh4fhMVgbaNmDRNHm7sYsmWuXXTPvZUS1xf3c+NleNsNlUMtZ4fZUsTZ+3XriCIxGCzxKDZaJzD2ZZh3jbzcmnFysiH0g6Xx6568NdasONLdexElakv7ad6dDsnL6nSnAFR2HZviTcefRsTz8ChQ1v592/YReOGTzEdNTBYdpTq7IiE68pjeVNZK7YnCSod0qDq8YLAFL7TVWMQRFashQC4eolA2xoaWEyWAWrEZXG61zK5bcMMsAL1b69gdNGIQQicA9zeH6ZMys7I9d1bsM1Trv7d7DqfISenRAL6GPvebUAhL5/u/dLMEou7J7hm8gUSjTjU2sRce4yy2cuN40+zJVqgQEjNaQAAIABJREFUVqi849XVWKJ8obkfcO9n8Y/H2fvUJGZBnRHRh97+4L7b+K+XfIJEU+5rxtz5wnczf8U44wfbxMZQfugZZo/M8vzrZrAlOLlXmHnQklaE0s+/wH0v+SizmU3iYNrguC3xQGuaTabJRSXnT/Tz8mnQJltUwzhf+20JfhkaBnsVcrSgIYgzHEY+uUmUcbrl4KwaFolyQVJva+5ZqEmbceMiEytiBkcgDhAEoyAIgbPEpyAfsSWqkrKgLWKNqNtObvxbTt0vUrRo2z4BkGYVeFJ/P7si/fz19/K+5muYiJo825rhFdNPMt+pcdf+63lhahP3V/bybePPc0P1GS4rtdlsxvIfYjFkdtDWoCIxlcjVVYyJ2Jwd/2C7yduf+FEqbxvjLde8nU0Pn4BDR0kv3015m6V8eMH1cBgbozNZwcYwftDSqQjNGYO57Sj/+6qPMBtN5AlRe+MJLgJ2RHO0VdlfSCAalw41lIqJqIrr6uxDk1diOaNiNMDd2C8UoOBaVOnRDmrSyaMR3Vj3PB1OJ51nJiui2tBOZntwHo/+luzrJfYgCIGzxOUGwLRxqbd1mxKRuoKefVK+P8Ou6A/3sQA+v77rCTD5j8z6mnzZ/0vLRylVE7ZEi2wvzfP68WeIxbCQVnjo5C4eae7gsfnt/H3tCq6bOMhLx57l2vKxgcFAiaZ0Mqs8OCHQsG3mbcrFpTEeaTd4y6M/zuEHtnPxX7Ww5RabHplzDVtMRGeizNJMRFrdzKZ7HkV378DGEZLC0ozBJHDNGx/ho3s/h0/LammHSdO9CvsCrhELPd9TCszblHlSUqXnOcvhPAe9gqD4f7kYBI/JDJFeUzDq6x/azKirXWEgSgxsMk44GbEkGjGnEfW8LoKlIt0GrT5UeT3kKAQhcJZMmCoTBh5PFjmeVtlVWmJcDHPWMl74sS6Xb+9xHoNuuqwXAN3n92oBqRqeaG3nkq0neOXYt9gRwebsSvNftv0jd088wZcWruBrxy/m0ePb2Te3lftql/Ntmw7w7bXneGnlecaNZYtxfQdcaXbJC4ok4vIALi5N8HiyyOv/6u1c8ZGEK/cfAGtp75nBNFqkO2cx7Q6V/XNU97WgFKG7trNwxRRpxaAGFm9c4qe+/Yv8x9lH2d9ZoCzCTFajsUaZRFMWbCsv2bazIKR8OnNDlWZWZGVyiA2BtwMUBQGwegyCf750t2eguTDwW7XucZrbDyYzIVA04uaJTBqzv9PBoJQlpSLd1nIx0UiFQRAC54D7mpaf/urP0T5Z4Udefj//edsX2VWKmbdu/7iaAOg+Vqyg063E21OTD5MLgp3xCf7T3k9xTbmWW/B93cMfGG8wHT1IohEPnLiIw/UJnmxUObw4wcPjO3lh9jH2xMe5tvwCV8U2jwoco0yizXy7cCJtcNvfvI2rf3cR02zT2T6FRgbTTmnumSJqpoi1EMXYySrRoTme+s1NwBK3XfYI79j6eerWcE25RktdDYZE3bbJWs2bjnjL+oJt9iTx+A5Fm7P7vv7D6dAvCPrHerYLhfNkwGkKmZ0mQnvqJw4qiEJ2XJTVQvBGQ4AX0onMZpAwLh2sSUlJqGahzDUZvnDtueS8Lzn+YtCwbe547Id54pntyFLEpsciGjuVN33f57h98kFuKJc4Zpd4uuNOchnLosZ5CmyaRQOmakjoVt2tSYuGVjiZVkm0RNUkbI1OsqNUZ9p02JlVA/YUryTPdhaYMhE1KTNvm3y5NcNdx1/GFw9cSv3YODt2neCV255iJl7kmeYWLhs7yj+deJibK3HuwnrF13+Yzp9uY9tnnoFKGY0MstSCcszSZTOUjzUhEp793k2YDsR1SCbga7/4W/k8vGV8uXmeCYm6OsM+TbvoPSgu5qiQuu2t9sUt2iDhvNoevd+DkxaWT0K3hJo7ptfFmxS0g6Km4AV+olFPUdUpk/aUXC9+/kRTIjm1HL5vBW8wp9RePK87EI2SE2mDTy1ezM7aPE9GW6kcKaMRbH4EPnvT1WwuLTJpHuV4WuWFdIqt0UmqJgWFdvaTK/r/PQbLnK0RocyUFpg0TWbMEltMypaoQkWqK/Y12B5VaNiEBs7f/321Jt9RuYc/ql3PPVMvwapwsjPG9eP7qUUt/vboS/jW0iyPbtrH99Se5u+WLmHT7U+SvHYa3TROc88UaoTa40fQahnTUZo7a8SLHVozlrEXDPPXpvzh7e8vfIZTBcC5wHk7uum/cRbbgLiOSjnqkpzct+R99Q6/VQAnDIY10PW7HaPCR7M6ZAo0Suq9EmqIskzHcdMm0YiGrVCnypHUCYRJaVIVmwWhVU7ptdDKbDnFIraRDL+9GKbk+KC+A+8C3gIcyQ77VVX9dPbYrwA/k31Xv6iqf73aJDayJvCB+R3E0uGOCdfV6LHEpfDes3AdO+MTXFo+yri0eb4zTaIlprOuvy90ppmOFnPVvqjmF5twTEcNdkcL7Ix6K+MM29l3wTadqpk9d8E2OZJ2+PP6S/ns4ZfQSMpcOeVO4+W1IxxOJrlx/GnuOXEtX73726i9oGz/2xdYuG4rYweXiOpNNIpIZmukFUPy9uO8+6o/42MnvpP/sfP+fG6ec6kBLEdRGPqORctpCT6Aa5jYjcHvtXItxf7XK27lkr7zWwwFB9gRtWiqULcxdVvNS6b538Xu0gl2lZbYHlWWrXuw0oXhjGsMLtN34F3Agqq+u+/Ya4E/Bm4GdgH3AFeprlyzaSMLgQfbTa4odRdZoikNbVOVUl5151Dqqu4Ur0JtNXmnnuW4KvaLvBt0UjQgDRt55hdl8epxIm3wTCfikydfxpH2JCWTcuP40zze3MnO8hz3n7yUS8eOcaJT477f+Q62fPCLHPrFV7L748+gUxMsXTTJ8Z9b5PMv/0C+rz+cLjJjxk6JkHOf4cU3ehWrPFts78LvWcznRiD0P7dfKPR3bOq6gt19v0WMC+HMiUY0NXbbQUnySMZps8T2KOkxog6eY7cOwhlvB1T13qzQ6DDcAXxMVVvAUyLyBE4gfHHI5284fN+8p5IFdmVNMWLtVrhNNGW2UN/OZ9+d7qLwhj+DISbKipQs/xrFq3F/JFpLExbVckOlxiVb7ufhpMqfnbiZD+1/JUnqjl38+A6+vE2YuOUIr/j5+9n3D9ey+1PPU3/5bipzCe/4nT/iu6tzTBhn0Luvabm1mvnBlynr/mJQjHlwfnjTo6Iv+BgDn8ehmtsLHKdhI+vbTqzmfjTeEJlvBbpRigCJLRFJQoRSkZRINEt26tDMisQsapm2RhyzNZraZs42qIrlotLYwIhDHwa+krXjbGwCvyAibwLuB35ZVU8Au4EvFY7Zn42d9+yNuxLZYnkqWWBvPNETlJNoSj3zGFSzUtjDqPRAzz6w6AlY6XhvQEu1qz0k6qoeX1RywTqxGG6tGm7Z8RU+Px2zr7WDd9/9z7n608/Ref4g+953E18R5dgbprjygy0qxxPm/sMi31drAlUeaLW4oVLh1qrhRNpgU1/dvrUQAH4PPMjPvlL6c8+8/AotCINh8M/zdSOLtoWc1VKgs8VfFAYA26NufIR3F3stIZYUq4YZ08DkwqHEc50pl8+gR6mKZTKrkOSNgu73tfI5OFMh8H7g193H4deB9wA/zeDiTAO/3b6+A2c4jfXB/s4C26MxDqVLtBQujyeYMF11PVXrIgkLufctTXr65lnVU37Qfj/fbwjyhrGV8Mk98QBBEWfZf0AuSJ7tNLipEnFl/Bh/9MXvp3nldrhqOzLdZstYg/GbnufRXbNce/FBvnrV3YDLnLyhMs5TyQLTxuRuvrXOlvO1DF0H1d5+g8XvadB2IPbG2D7l3RSu6sPgG7MUt3j5Y6ukQCM+UhHoy2Xwac6JRllZdptFHVqO2AptjHNZZnaCqkkoa8q+ZJZNpsmWqMFW02bKQIXSUFGJZ7T6VPVQ/nlEfg/4v9nd/UBxc38RcGCZ17gTuBOcTeBM5rFe8P0H+vsQFBNzpqS3W1FlhXp2nrPpprPccwdpFOA0mVQt9zSnufTfPcYPzP4jF5eOM2VaPNPZzJcXL+fqqcNsL5/kI/UZXld7llr2OnvjCRasq1FQk5iDaZtLS12BcNI2qZn4nBXxHPZ7GbQdWI1iDMKgcO/co1CoG+GJC29UDGrsX4Sum3J2G/DXyZZClOUe9GSQqtBUk6c+W5U8SjFFXJamxtRMC6vCM50xaiZhT+S+94ZNVmyXd6Z9B3aq6sHs7g8BD2W37wI+KiLvxRkGrwS+cibvEVh7XPWgXmvyD0+c5FVjf8lm47IDK1LlmnKL7609zP7OAp+oX8cnj9zA5bsOc0s1YsE2SdSyOarlPRouz+wfvoFLJSs75tOu1zO+05InT2fuU/lX0gSKjw9iqGIpxZTmLJ/BVZ8WVytBu+nOKcK0aeQdnn1NhOPWkuJyRlbiTPsO/BMRuQEnwp4G/g2Aqn5TRP4UeBjoAG9dzTMQGC3eg1G0GxQTXY7ZJSpimDJjXFSa4Genn+COyW/yjmfvYHO5wU/N/j23VN3Cbth2bgD1iUf+qt0q5CWsZ4raio89KG4RkuIuv7DGIwpZjn1bimG3DFHBZjDoTXqEivQeNx01mbO+OIwlUcMxrVCR1qrbshAxGAC6Ngr/Q6tJOb9q+xJkidpcrb9rscZHD9/Cc/Vprt18iJ/Yeh+vqKTLxsCvt0Iap4u3c/iIvHx8mav9ci5IWL1t26nHuP++30R/l2dwvSOdLcHkrkUjlj1RK3cjhojBQA/9sQMVcYvbh54W1fZIDBPSzSU4YFv8wDhcvvsv+d2jr+arRy7mNxa+n1dtfYJXjO/jNWPdn3PDtnvCW4vawkYit+8gRHjh6D0EvZuBFHW1IvIrfa8doT9q0QuC8nLbhFxDODWz0UchLmqJ8WwbcNKOYdWwJzrJlqhbM3E5giZwgbOSq9EbDgd19vWNSsDFCPzWgdfx1NwMm6pNvmvrk9w++WC+TdgItoBhKVYE6l/8xdiM3HA4YH0tF5w0qOZk71bEj3ULz7j7Ql3jXBuYs2NMmiZXx52eZjJBEwgMxIXTuh+JN+TFEtGwCZtMtccS7x83mFwAHE0XuTYW/uDSu7m3OclHj3wnXzh6GQudCienvsH15RNMmfK6yJs/U04Jxc3uG6K+ykBdIZAbDgfYCYpGw97SZwOClqToJfBj/uHufWsNR6zrnTBpltgRtZiQWnfOK7Axz0rgnODrBRZ97P6qvzkrk+aPm7dLWCxj4qIf/diUqbLJVLFYXjPW4MOX3MtPXvQFTnbGeN/+13LniZs5mLbzkmuPJ6eXBjwKUrX5nwu40vwPehdVMTR6UC3B/LEB3oWVjunWKuwu0qIuFUlxXDFiOZxOciydoCodZk15aKEbNIELmP69+XJBSIP28P1j3mYA8K8mj/G62mf4s/pL+Osj1/HN+k5+bNtXua3W5Kp4fN2W3vb0pN+exjGrbXmeTBYwuBLpZZGsirJvdWZYsN2W7L4PJfTGKKSq+Ka0rgCNoY3hSDpJVRK2RXW2Rm1qphuzUty6DSIIgcCaMGPGeOv0c/zrqW/x+/OX8vsHbuX/TR7hbbOf5+LS8oEr5zOzkU8d9zEASqrd7lS+34LbcnUzIIuMiwt+SlDqNs0rUcWkTEcNdkQNpkyvMFqtWEkQAoE1pSIxb5l6jhvHnuYLjSv57aOv5jd33D/qaY0EfzX2HpiGJj3t3mPT6Tk+9tpGnogkubbRsC5iYVFj5myNGbPIrtISO6OxrFSce4+KlFbVUIIQCJxzfPIUkHcwurlieGl5H59Y2Dbi2Y2eSLJMUFyCl8VFAM5Zm+/1yyIYVWIxPR2Sncemw5y11G2ZRVsh0RK7SkvMmvIpC96yesGUIAQC55y98UQeIVhsa1aRmDdMnugpyXUhU5USMUota7LSsF3vgu9b2Sy4Hasi1K3S0IhE3dZhW7SAkXquAXgicTULU+3fUJxKEAKBNcFgOJQusTNy3gd/FTtf4gXOhOKCdE1gepPIJsRpBa4wjdsqtBSaWfXpGEsj60hdlZQtJslqVVR63qPHaJmVk1/pew9CIHDO8VsAn1VZbF12IbOaN8Sn/bqYCqGqKYnabNMAi1Ypi6tHURPXp6HopfGp051M6/KsVuMxCIHAOadYM6EicU99w+O2w+4V0lovZIr1I3wYd5GW6SZh+YIqxZoVxcAlX4PSV1haiSAEAmtG/4/YN2oJDGa1Ogkr1WPoFg85fW0rnJJA4AInCIFA4AJnVSEgIh8UkcMi8lBh7E9E5IHs72kReSAbv1RElgqP/a+1nHwgEDh7hrEJ/AHw28CH/YCq/pi/LSLvAeYLxz+pqjecqwkGAoG15az6DoiIAD8KfM+5nVYgEHixOFubwKuAQ6q6rzC2V0T+UUT+TkRedZavHwgE1pizdRG+Edd2zHMQuFhVj4nIy4H/IyLXqerJ/ieeT30HAoGNzBlrAiJSAv4F8Cd+TFVbqnosu/014EngqkHPV9U7VfUmVb1p68yFHUkWCIySs9kOvBZ4VFX3+wER2SriYkNF5DJc34Fvnd0UA4HAWjKMi/CPcQ1FrxaR/SLyM9lDb6B3KwDwauBBEfk68OfAv1XV4+dywoFA4NwyjHfgjcuM/+SAsb8A/uLspxUIBF4sQsRgIHCBE4RAIHCBE4RAIHCBE4RAIFDANWdduSRXsdNQw7Y3TLPV5QhROoFAAYPJq/OUiHoKcvgCHgZhwTZJUapSWjHPfyMQNIFAoIBBaGqHedtmQVs9j3WyNmMt7XAgTTmU2rwxyEbWBoImEAj00VJL3SptSZmQbuHOVJWGtjlu2zSsu/ovmIRYI0pnUNFnvRA0gUCgQEs7NFVZ1BINFZa0nT8WiXDUtjluS8RiqUhK3SoLtrVuW6oNw8adeSCwBrS0Q6KQqKGpEXXbyQ2BJSKaKlh1Jb/HjTMgpmzsRgpBCAQCBepqaatbFlaFhsKCbeVGwRilZjp5t+BJI9SCYTAQOH9Isot6hJIitNWQFK70VYFp43oHWrq1/xu2PfD1NgJBCAQCBS6PJ9hVEiZNQoTS0oi6VU7aJi1NOJTGPNdxXYCmTJS7ByuycW3sQQgEAn043z/E4vf8QpK1B4vFMmkSJkWJ6e39t1HZuOIrEFgjKhIzLgktSZ0AUENTU2IsVbFsMYYJUz1v2qptXPEVCKwhE6ZCTSAm0wYUEpSKwKbzSADAcEVF9ojI50TkERH5poi8LRvfIiJ/IyL7sv+bs3ERkfeJyBMi8qCI3LjWHyIQONfEElEzEWUpdhJ2Tb42suo/iGE+TQf4ZVW9BrgFeKuIXAu8E/isql4JfDa7D3A7rqzYlbhCou8/57MOBF4EqlIizmwDVYGaxEQiPSHCqdqh/9YrqwoBVT2oqv+Q3a4DjwC7gTuAD2WHfQj4wez2HcCH1fElYFpEdp7zmQcCa0DR1VcioipCjFIVQ82USVWZt23m7VIuDCxKSzujmvJZc1p6TdaE5GXAl4HtqnoQnKAAtmWH7QaeKzxtfzYWCKx7iovZZQySaQPdpbJolbpNSTTFolgsCemAV9sYDO0dEJEJXP3AX1LVk6750OBDB4ydElcZ+g4E1gLnxusa7VK1eTZghNDQNL/yRQhGhCj7yRoMFSlxNF2kbpWmGsqiTBlhTMoA1ExErJamKofSDm21JBisGoysHDC0xXSYNBFTZqxnPFVLSzvUTPkcfQunx1CrT0RinAD4iKp+PBs+JCI7VfVgpu4fzsb3A3sKT78IOND/mqp6J3AnwE0vrW7s4OvAusFiIfPfp2o5ZpeYsy4XwGBJssciFIMSZYa/aED8/7ixVEUyW4ATHTFRdpmzgGKweXRhsopiPW8jLCkRTSZMNR934chr621YySYxjHdAgA8Aj6jqewsP3QW8Obv9ZuCThfE3ZV6CW4B5v20IBNaaYoEPt7jcVd4v0gg9ZcH7+waYs2WaGhELTIrJw4K7r1+iKiWqEjEuhnEjTIoyaSxVSVf8a2rEojU0ND2l/sAoXY7DaAK3Aj8BfMO3IAd+FfhvwJ9mfQieBf5l9tingdcDTwAN4KfO6YwDgRUobgdStSQoaZb5Z8RdsXtQChtYZWvUJsap/TUpn7I4IzFEGAyGWCyJpqTihEh5FQ9ARJuKQEXcKxRZa+/BSm7NYfoO/D2D9/kArxlwvAJvHXZygcC5pKUJBnGLVQwVMUyalHEsBvBLrWsXcHUC3H1hNhof6n1iiUhVMNnisjg34kpsNiafW5FULRbFLLvM1pZgkQucV1QK+3eACakQmTYpLtbfW/GLxkAnBM4sAMjkrxOdURCRFwBA/n8tWGmzEYRA4LyioW2quOKfLU2Yt23mLKQquLg3iDL13RkHISpcgLdHlUzV7102zh1oc2Ex6KpdrEI8iH6vhc1tEe611lIIrJTqHIRA4LxiQir5FblERKpKohFNdQvQ7eN9vYCuMPDGwSeSDpMmZbbPINjShKamTGZjFk7RHhq6soswUm+kdO8VExFLV4OwqwiRs2FhhUKoQQgEziuKKnmH1Pn1vQEPaK1ysd0aWZoKB9M2sW1TE2HSlJkwVSayY06kDRbVOSOrIkxITM2UmU9TqiJUxGBVaaolzY6pSsSRtENdS0yblJoI85rmr1+3bRL1kYnOw9HUDnWb0lCXyejZHlnizK2YkLK/A7siZcJUOJQucTSN2R4lTJkyFktFYqZWiEEIQiBw3lIioiYG6+sCqK4akOPV9JYm1G2HhirWtpkxvXv+RMmEQFfN32LcNsTf99sDf3/OLjBOhykTMSEV4iyIyVUxVmZNmUikUKgkBpaod5Q5W2HauOMnTTk/Zt4ukaghoYNBGBdDXbzA6zhhIVFeMWnw9xQInKc4dx7kZrEhjO/+ObFExJJwKG2xaJWatJkQF+BjsmjZthqOpbBol4gFtkdjPbaCfruC90xUpeRiGNRdyRuaMGdLXFwqk2hKw7ZzYVCTMlujJmVpsKilPLU5yWINErXUjOt/4IVUSyMSa5jPgpm2Rosrej2CEAgEChTjDNwibDOvSt12iCXJkopKbI2EpqY0VGmq0LCGaZPtu5UejcCzaA0WIdE0v5K7bYPmfQw8RQNkhGBQrApWhIZNqKulbiOmjLLVGPyO34iLh/BEorknZDmCEAgECrQ0OWXx+vJiDZuwKasrWJGYCdz2YUnbNNX1IPCmvaqkPWp7mucYyClehLIIsaS01LotTGHLsqRtjlvLMVshQvGiYtEa5myFmjSoRTH1gvW/KmkWxVjCEGfZj8sHIwUhELgg8ItgNTdc0eLfsG0aqqRqwLsVB8QCeKHRyHoWuKhEpaYK4t67Q+o8EIWLciTChFSwWJpRi7p1eQqVbFm2tJMnPE2bNnUbY3AGwBmaeehxoinVbA4ttaQqxMZpI5bVIxGDEAict3gjn18IwxgGDZJf3Y/bDnUbZePdisKJpjS0TUstbdV8maWZAEhVsD0quaGTFSk1KnlacjfPIWKLSZmzHSwJC5rQVCVRMluDc1d+rdUmEiWWiG3ROG1dwAINTdmcJSQ1VZm3VaqyBDSpWwV6eyr2E4RA4LzlTA2DqVpS3CJ0yUSWskiuVieaYlVpq9JSst4Ehi2mQxWlaoSK9CcexVxSWG3FLYHBpSrX6WQGPsncik74NDShkSbsyZ5/OF0E3DbC82xnCXBCaEvUJEE4kspQHzoIgUCggLO4p9lV3l2xfWQhOCFRkzKRCrGmJHmSUoc4q00QiwtF9l2LVqLoTZg0pWys+5yosNB9B+RBbIt7NZz+7c9KWYpBCAQCBY6nrTyoKCFyxjixPZWFgNw4WKSYHuwX8iBBsFzocbHGwEBOI7/Iv6df+sEwGAgMSV3dnt5b8mOx1PoajSxHUSgU7REdTUlVe67GRQFwLqoX++1F0RBY1ByCJhAIDEmqgqVbf6AmKeNieqIAB6UD9xOJyXMBUvXGSW9kPLcCAIqLvFtLodTjiTiLegKBwIXEuLE01QXnlMW6rsOmNxQ4EsGqrioM3GI3IJaI6BT1v/+5/cbC/uNWUun7X2tQzYLlCEIgECgwKYYYSxPNew0UA35Op/Fov3eiuBBXFx6rH7ccxfcJhsFA4DSpmRhsAliqYnoWvUVXXEx+8Q2zVRjEanUGhxUIRc3BC5R1rwkoOpQ7JRBYayoSY4yhmS6xOaoBXQv/auW/znTxrxXF91tJCIiu4Ht8sRCRI8AicHTUczkLZgnzHzUb/TOs9fwvUdWt/YPrQggAiMj9qnrTqOdxpoT5j56N/hlGNf+gfwcCFzhBCAQCFzjrSQjcOeoJnCVh/qNno3+Gkcx/3dgEAoHAaFhPmkAgEBgBIxcCIvK9IvKYiDwhIu8c9XyGRUSeFpFviMgDInJ/NrZFRP5GRPZl/zePep4eEfmgiBwWkYcKYwPnmzWTfV92Th4UkRtHN/N8roPm/y4ReT47Bw+IyOsLj/1KNv/HROS20cy6i4jsEZHPicgjIvJNEXlbNj76c6CqI/vDxVM+CVwGlIGvA9eOck6nMfengdm+sd8E3pndfifw30c9z8LcXg3cCDy02nxxDWXvxiWv3gJ8eZ3O/13AOwYce232W6oAe7PfWDTi+e8EbsxuTwKPZ/Mc+TkYtSZwM/CEqn5LVdvAx4A7Rjyns+EO4EPZ7Q8BPzjCufSgqvcCx/uGl5vvHcCH1fElYFpEdr44Mx3MMvNfjjuAj6lqS1WfwnXIvnnNJjcEqnpQVf8hu10HHgF2sw7OwaiFwG7gucL9/dnYRkCBz4jI10TkZ7Ox7ap6ENxJB7aNbHbDsdx8N9J5+YVMXf5gYfu1rucvIpcCLwO+zDo4B6MWAoOCsTeKu+JWVb0RuB14q4i8etQTOodslPPyfuBy4AbgIPCebHzdzl9EJoC/AH5JVU+udOiAsTX5DKMWAvuBPYX7FwEHRjSX00JVD2T/DwOfwKm3evL/AAABMklEQVSbh7zKlv0/PLoZDsVy890Q50VVD6lqqqoW+D26Kv+6nL+IxDgB8BFV/Xg2PPJzMGoh8FXgShHZKyJl4A3AXSOe06qIyLiITPrbwD8DHsLN/c3ZYW8GPjmaGQ7NcvO9C3hTZqG+BZj3Kut6om+P/EO4cwBu/m8QkYqI7AWuBL7yYs+viIgI8AHgEVV9b+Gh0Z+DUVpMC1bQx3EW3F8b9XyGnPNlOOvz14Fv+nkDM8BngX3Z/y2jnmthzn+MU5kT3FXmZ5abL04V/Z3snHwDuGmdzv8Ps/k9iFs0OwvH/1o2/8eA29fB/L8Lp84/CDyQ/b1+PZyDEDEYCFzgjHo7EAgERkwQAoHABU4QAoHABU4QAoHABU4QAoHABU4QAoHABU4QAoHABU4QAoHABc7/BzhpiIVEptRfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "filepath = 'E:/Image/Fixed wing/1.jpg'\n",
    "img_array = cv2.imread(filepath,cv2.IMREAD_GRAYSCALE)\n",
    "plt.imshow(img_array)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = model.predict([prepare(filepath='E:/Image/Fixed wing/1.jpg')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fixed wing\n"
     ]
    }
   ],
   "source": [
    "print(CATERGORIES[int(test[0][0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "THANK U\n"
     ]
    }
   ],
   "source": [
    "print(\"THANK U\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}