Image processing/Video Summarization/Video Summarization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a2dc73b1-6f1a-4829-9f3e-68835f0adfc8",
   "metadata": {},
   "source": [
    "# Video Summarization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "79ad9a99-0a05-4881-8d8b-59fbeadc7581",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2 as cv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3dda4622-f0d4-4026-8800-e8e61bb551d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "video = cv.VideoCapture(\"Sample.mkv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e4ebdadd-fabd-42d2-8cc6-f67cf38ba78f",
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 1\n",
    "while True:\n",
    "    ret,frame = video.read()\n",
    "\n",
    "    if ret == False:\n",
    "        break\n",
    "\n",
    "    gframe = cv.cvtColor(frame,cv.COLOR_BGR2GRAY)\n",
    "    cv.imwrite(\"./vout1/picture\" + str(i) + \".png\", frame)\n",
    "    cv.imwrite(\"./grayvout1/picture\" + str(i) + \".png\", gframe)\n",
    "    i = i+1\n",
    "    \n",
    "    cv.imshow(\" Video is playing\", frame)\n",
    "    key = cv.waitKey(1)\n",
    "    if key == ord('q'):\n",
    "        break\n",
    "    if key == ord('m'):\n",
    "        cv.waitKey(-1)\n",
    "    \n",
    "video.release()\n",
    "cv.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3392be9e-8647-43da-bd5d-6e7f7f6ac1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calEntropy(image):\n",
    "    row, col = image.shape\n",
    "    n = row*col\n",
    "    pixel1 = list(image.ravel())\n",
    "    pixel2 = set(pixel1)\n",
    "\n",
    "    fre = {}\n",
    "    for key in pixel2:\n",
    "        fre[key] = pixel1.count(key)/n\n",
    "        \n",
    "    l = list(fre.keys())\n",
    "\n",
    "\n",
    "    entropy = 0\n",
    "    for x in l:\n",
    "        entropy = entropy + fre[x]*math.log(fre[x],2)\n",
    "    entropy = (-1)*entropy\n",
    "    return entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "608c8d9c-c408-404d-81d5-647714ce3e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getKey(fname):\n",
    "    fin = 7\n",
    "    lin = fname.index('.')\n",
    "    result = fname[fin:lin]\n",
    "    return int(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1d149dd4-cdcb-44f0-86b6-87c57068234b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sortFile(files):\n",
    "    files.sort(key = getKey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77057fc6-d60a-4e2e-8e97-9ba2d73a5bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = os.listdir('./grayvout1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04b8ae7b-23ba-46f7-8d8f-1969bf75067e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['picture1.png', 'picture10.png', 'picture100.png', 'picture1000.png', 'picture1001.png', 'picture1002.png', 'picture1003.png', 'picture1004.png', 'picture1005.png', 'picture1006.png', 'picture1007.png', 'picture1008.png', 'picture1009.png', 'picture101.png', 'picture1010.png', 'picture1011.png', 'picture1012.png', 'picture1013.png', 'picture1014.png', 'picture1015.png']\n"
     ]
    }
   ],
   "source": [
    "print(files[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "578afc1f-2c14-47ed-bb14-8585c64e6628",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['picture1.png', 'picture2.png', 'picture3.png', 'picture4.png', 'picture5.png', 'picture6.png', 'picture7.png', 'picture8.png', 'picture9.png', 'picture10.png', 'picture11.png', 'picture12.png', 'picture13.png', 'picture14.png', 'picture15.png', 'picture16.png', 'picture17.png', 'picture18.png', 'picture19.png', 'picture20.png']\n"
     ]
    }
   ],
   "source": [
    "sortFile(files)\n",
    "print(files[:20])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e413fb0-bc07-405d-9363-6daa216499dc",
   "metadata": {},
   "source": [
    "# Plot the Histogram of All Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b942763d-3a2f-4e35-aa2a-ae5196d21c28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# listed = []\n",
    "# for f in files:\n",
    "#     image = cv.imread('./grayvout/' + f, 0)\n",
    "#     error = calEntropy(image)\n",
    "#     listed.append(error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a2db63f-35ad-4af2-a820-99d3f0f54cf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x = [i for i in range(len(listed))]\n",
    "# plt.plot(x,listed,color = 'r', label = 'Entropy')\n",
    "# plt.legend()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e2e1ed-51db-4b3a-ab2b-9e75a0f3b045",
   "metadata": {},
   "source": [
    "# Reduce the Number of Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "548a29b0-1293-431e-9fb2-441f1873ec30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_frame_changes(files):\n",
    "    prev_hist = None\n",
    "    changes = []\n",
    "    for f in files:\n",
    "        image = cv.imread('./grayvout1/' + f, 0)\n",
    "        hist = cv.calcHist([image], [0], None, [256], [0, 256])\n",
    "\n",
    "        if prev_hist is not None:\n",
    "            diff = cv.compareHist(prev_hist, hist, cv.HISTCMP_CHISQR)\n",
    "            changes.append((f, diff))\n",
    "        prev_hist = hist\n",
    "    return changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d70cacd-3af1-4619-ad83-6b92b821e598",
   "metadata": {},
   "outputs": [],
   "source": [
    "changes = calculate_frame_changes(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba2f600e-b37f-453e-859e-6220e4c20095",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for item in changes:\n",
    "    #filename, difference = item\n",
    "    #print(\"Filename:\", filename)\n",
    "    #print(\"Histogram Difference:\", difference)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8ebe3363-7b32-4fe4-8b08-affa331880da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_frames(changes, num_frames=100):\n",
    "    changes.sort(key=lambda x: x[1], reverse=True)\n",
    "    selected_frames = [x[0] for x in changes[:num_frames]]\n",
    "    return selected_frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "83a3e491-aa61-4e48-9fa0-f9d6b2ca61d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate frame changes based on histogram differences\n",
    "frame_changes = calculate_frame_changes(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "70ed29d8-0e20-4bfb-8d97-d9141c5f79df",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_frames = select_frames(frame_changes, num_frames=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2fd4a85-666a-4363-9fee-652aebb1d379",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the selected frames\n",
    "listed = []\n",
    "for f in selected_frames:\n",
    "    image = cv.imread('./grayvout1/' + f, 0)\n",
    "    final_imgs = calEntropy(image)\n",
    "    listed.append(final_imgs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ade5864d-ac21-4703-832a-99dbd1d3a860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ICawAl/cyTKnDzogZFiPSsBixEpUz8tJL+sG4//7Avvs6P06FGNm0yfh91y7gu9+N5kQQE1gBQ5RwzgjDlCYsRsxwaa80LEasROWMfPKJfjtmjPvjVIqRYcN0gfDKK8CcOebHdHUB994L/PKXwfNK7MI04v0Mw5QWnMBqhp0RaViMWInKGdmyRb9tanJ/HImRHTvs/69peg7Ij3/s/BokRsaNA37zG/33n/4UePNN/fmPPab/77zzgB/9SF9lMwgsRhiGERGdEXYCWIz4gMWIlaicERIjtBieE17OyJo1wH//N/CLXzgLJhIjzc3AOecAZ55pJLUedRRw+unA8uXG4zdskP8cIlYx4jdn5Ec/Ar73vWgrfhiGiQ92RsywGJGGxYiVqBbK8+uMOIkReh0A2LjR/jF0/+DBesnt3Ln6qrp/+xvw6qtAbS1w7bW6MAGATz+V+wxWrAmsfnJGurv1ENHddwd/f4Zh0gXnjJhhMSINixEr5IyoDtPQhOsnTGPXVl2cuJ0cDXJGaCXdfv2ABx/Uk2cvukgXJT/7GbDPPvr/RYHjB6cEVhlnRByoolwhmWGY+GBnxAyLEWm4z4iVtDgj+bw+qTc22r8OIC9GAOArXzGHZgAjZBRWjATJGRHFXlTrADEMEy+cM2KGxYg07IxYicoZkRUjNTXGNtiFakRnhCp0rNiJETtoW4KGScKIEfGqicUIwxQH7IyYYTEiDYsRK1E4I93dwGef6b97JbAC7nkjfpyR5mb39yExEtQZCdP0LCthmtdfB158MZ73Wr0auP1289Ulw2QJzhkxw31GpOEwjZUonJGtW42KkQEDvB/ft68uKLycETsxsnu3/gN4OyNhwjQdHcZgE6TpWRbCNN3dwHHH6dv32WdAfX207/eTnwAPPKAfI1OmRPteDBMF7IyYYWdEGnZGrEThjJCA6N8fqJDQf2GcEXJFevXynjzDOCOi+1GsYZo9e3Rh1d4eT8UP9ZbJworNDGMH54yYYTEiDYsRK1E0PZPNFyHcxIiXMyKbLyJuz6ef+u/1QYKjpsYQWMWWwLpnj/H7zp3Rvx8NXHxFWZosWRLdOlJx0Nlpnnz5OGYx4gMWI1aiaHqmUoxYnRGriPAjRihM097uv2uqNXkVCJ4zwmJEhwYrvqIsPdauBSZO1MOCWcWa68RihMWID1iMWInSGZFJXgWcxYimmZ2RvXsLHyObvAoAdXV6OEfcRlmsyavi762t3k4LOyOF0MCVlBjp7AzejZcJx7PP6pPV6tXZ7UjMYqQQFiPSsBixkmZnpLXVOMFJRFjLe8XuqzIELe+1NjwDDDHS3e29/9gZKYQGq6QG8XPOAVpaCvvRMNHzwgv6bXe3OQk0S1i3mx0+FiM+YDFiJQpnRLb7KuEkRuh16uuN7qnWK1k/YRpxm/w6I3ZhGjFh1itUw2KkkKSdkQ8/1K/KP/ggmfcvVTTNXD7utEhm2mFnpBAWI9KwGLGSJmfEOiiJ4Z6hQ/Xf0yRGysoMx8ZLjHCYppCkc0Zo8sjqZJhVPvoIWL/e+Dur+9/qjLAY4T4jPmAxYiXN1TSiw6JKjATtNWInRgD5JNY0OCNPPw0sXuz8f1GMxDFBsBgpTaxN9bK6/9kZKYSdEWm46ZkVckY6O/X1YcoU6DVVCazi6wwZov/uJEZkEliB4Dkjdgms9PfGjd6Nz5J2Rj79FDjxRH1f0j6zUmqlvSxGksEqRrLaZ4ZzRgoR90F3tx6Sy+WS254Uw86IFXJGAHWTZFzOSD4PbN6s/x5XmEZMYAXke40k7Yxs3GjsL7vVkQEO0zDxQGKExp6s7n92Rgqxnsvd3fFvww9/qK/Yvm1b/O/tAxYjVsgZAdTkjXR0GBNZEDEilvnZ5YyI1TSffaYf7LmcvAujOkyTFTEiigun77nUEljpe4jjszI669frjc7Ky4FjjtHvy6oY4ZyRQqznchKhmgceAFauBJ57Lv739oEvMTJy5EjkcrmCn8suu8zxOQ8//DDGjBmDmpoajBs3Dk888UTojY6UigojNKNCjJCbUVEB9Okj9xx6XHe3sc6M+FpOzgiFGwYMACor5d4rbGlv0JyRpMM04oTrtDBdqZX2sjMSP+SKHHKIXlYNZHf/03lUW6vfshhJXox0dxsXmn/9a7zv7RNfYuTNN9/Exo0be36eeeYZAMAZZ5xh+/hXX30VZ511Fi644AK88847OO2003Daaadh6dKl4bc8KnI5tYvl0YEwYIB8/kldnSEmxFCNnTOyZYtxwPtNXgXUVtOIf3vljCTtjIjbJ4oOkVJzRliMxA+JkaOPdq6iywrkjNDFFOeMJC9GtmwxwtDFJEYGDhyIwYMH9/wsWrQI++67L44++mjbx99+++04/vjjcfXVV+OAAw7ATTfdhEMOOQR33HGHko2PDJWL5fnNFwF0QWSXNyI6I+R+aJrR6Mxv8qq4XZ9+6pw7YYdbAivAzkgQkswZ6e42vv+sToZZRBQjNIlndf/TeURjFzsjhedy3Oe2GMZ/771439sngXNGOjo6cP/99+P8889HziE7+LXXXsPkyZNN9x133HF47bXXXF+7vb0dra2tpp9YicIZ8SNGAHsxIjojZWWG6KBQTRBnZMAA/ba7218Wf9YTWP2KkTgmiCSracT3zOpkSGgasHVr0lvhzSefAKtW6RcfRx2VfTFidUZYjCTvjNCFKqCvf5TifLDAYuSxxx7Djh07cO655zo+ZtOmTRg0aJDpvkGDBmGTUynlP5g9ezYaGxt7floolhoXKp0Rv91XCasYEdelodey5o34bQUPAFVVxuDhJ2+kmBJYncSIeH9bW/RXNUk6I8UkRmbO1AX7q68mvSXukCty8MH6OUjnYVZLe+l8YTFikLQYsS4X8v778b6/DwKLkbvvvhsnnHAChlC/C4XMnDkTO3fu7PlZL3YnjIM0OiM7dhgHMlXAWMVIEGdE3DY/eSNeCaxp7zMiihGZnBHA+zOFRVXOyLvvAl/6ErBokfxzxIlj165kShBV8d57unhP8cALwBAjVEVTbM4I54wkL0ZEZwRIdd5IIDGydu1aPPvss/je977n+rjBgwdjM/W9+AebN2/GYI/Jsrq6Gg0NDaafWIkiZ0S21JawihFyLRoaDLFkLe9NgxgpJmfEKkaitDg1zRAAYa8o//AHfSJ+6CH551jfM+7QqEros6R1mQFCzBcBsi9GOGekkLSIESqISHHeSCAxMn/+fDQ1NeGkk05yfdzEiRPxnKW2+ZlnnsHEiRODvG18pNEZsRM1Ts6InwRW8TVlwzT5vFFyXCoJrNbn+GXvXuDvf3f+v+hEhL2ipOZGXt+BiPU7yOqECBj7L81iZPNmfXXkXA74ylf0+4pFjCQdpvn88/Q4e0mLEbpQPeoo/baYnJF8Po/58+dj2rRpqKgwd5OfOnUqZs6c2fP3jBkz8NRTT+GWW27B8uXL8dOf/hRLlizB5ZdfHn7LoyTpahrA2RkRXyepMI3Y+0RFAqvKRQlliVuMfPe7wH77AU5l7SoX1AoiRqwTR1YnRCAbYuQvf9Fvx40D+vXTf6dzfudOf5VtaSENCaytrcDw4cAJJ8T/3nYkLUbIGTn+eP32/fdTe2z5FiPPPvss1q1bh/PPP7/gf+vWrcNGIUZ15JFHYsGCBZg3bx4OOuggPPLII3jssccwduzYcFsdNSqdkbAJrDQp2Dkj4vo0e/YYj1UpRpYuBebONR/ANMmVl5vb5wPZbHommzMSRoysWqXfrl5t/39xkAorRqiShMVIstvhhjVEAxiTuOg8ZgmrM5JEzsjq1boYf+ut+N/bjrSIkX/6J31e+/xz5zEoYXwvlPeNb3wDmtiiXOCFF14ouO+MM85wbIqWWrLojJArUl0NNDb6ey+3lvAXXgi8/jowaBDwrW/p94n5Itay7qw0PYvbGaHP6DRAi/eHvaJkZ0S/TbMYoZDdwQcb99XU6Odve7u+/+POlQsLOSM0dnV36z/l5fFtAx3HaUmetZ5XcW5Xd7cxLwwfDnzxi8Dbb+uhmv32i287JOG1aewgMRJ2MPv8c2OiC5vA6pYz0tYGrFih/z54sP9VIZ1awnd1Ae+8o//+0kvG/U7Jq+J9xZTASr1YwkzQXmJEpTOiQoz4EV5PP60PcmkhCwmstI1WZzHL5b10HokXQ3GLAvrO05I8K+uMfPwxcNttahPHxbXKmpr0CjsgtXkjLEbsoDBNWGeEBERNDVBf7++5Ms5IXZ0xeNFk4DdEI76m1RlZscI4uV9/3bifThi7KzcSI3v3uluSSYZpNM180nuJEdqncTkjaRAjssJr82bgxBOBU0+Vf6+oyYIzQttoXUMqy0ms1pwRIH5RkDZnRFaM3HwzcOWVwP33q3tvSl5tatLXRmMxkkG8nJE//Ql4+GHv1xEFhF+3wnqF5FQiTO4IxUj9VtLQ9onvQbz7rvH7228b+0PGGREfZ0eSzsju3ebVkL1yRlSIEa9BUhykwgzg+bxxzFg/p8z2EX7ESD6vhwrTVsGQZjFC33cxiRFrzggQvyig4zifT0eipqwYoXNWpSNG+SKUW3jQQfptSst7WYzY4eaMaBrw7W8D3/mO94ETNF8EMDsjdt1XCasYCeKMkMDZts18sogHbUeHEbJxEyNVVcb+c7Mck3RGrKKimJwRsRJD0wqXdffaPkJ2MhQFZ1p6k2RBjBSbM5LPG+dR795Gnkjczoj4nafBHZEVI1EskklihC5Qx43Tb1ev9ueaxgSLETvcnJG9e/UvUibjXYUY6ezUT3InZ4RU79q1+m0QMdK/v+7cWNf0IDFCqw1TqMZNjIj3p9UZSVKMOA1GqnJGKERDyA46QZ0R8RxIywRKnyWJknFZik2MiPu6Vy/jcyUVpgFYjFCYhuaIgQMNYeLUYiBBWIzY4eaMiFeaXgdO0O6rgJ5jQlcX27bpyUiAszNCBBEj5eVGkqYYqqEwzSmn6LckRpxW7CX8ipGODvlwggpkxIimGd9/WDGiad5hGlXVNNYF4kpRjGTJGbH0aioo6fdixw498dHa9jtuxHGxtlZ3SIFknZE0JLHS90wXuE5iJIpcF6szAhihmhTmjbAYscOttFc86bxqxsM4I7mcMTCtXm3E40k0ECrECFBYUbNpk779ZWUAtf2n1ZadVuwlZMSIdaKIc+Cwigq7nBHxuw8rRmSu1sRjqbs7uDizOiOy/SqCVtOwGCnkV78CjjjCfR+qckbuuUdPfLz5Zr9bqRYS9DU1+sUNiZGkckaSeG87aBtqa/XbJMM0gJHEmsK8ERYjdrg1PRMHX68DJ2jDM4LECJXt9uljnOSEVYwESWAFCnuN0ME6erTemKmsDFi3Trf+vMI0Mo3PrJNfnBOHjDMiCpSwYkQmjm0dpIIOSmHDNHTsB8kZYTGic/fdwOLFwBtvOD/GS4zIJjKSE/bRR362UD10kVZXp98m5YykXYx4OaNRhmmAVFfUsBixIw3OCGCIkZUr9Vu7cI9qZ4S2mUI0Bx2kiw7qmvvGG/I5I7IJrHZ/RwmJChow7cQI3VdRoefUAMEnW5kB0np/0EE8rBih4yBImCbKhQRl0TTjvExKjNB+cJtYVDkj9BmpuVVS0PnSq5d+m1TOSFoTWNPmjPz1r/GGxiVgMWKHmzOShBghZ8TudaxiZNCgYO9lFSPkjFCHyC9/Wb99/XX1CaxAMmKEhJubM1JbazRxyoIzEjZnJIwYSYMzIu63UhAjdMGUFjHCzoiZpMRIPm8vRsaM0Y+5XbuSd9MssBixQ9YZiTKBFZBzRgYONBJd+/Y1hJRfrCv3khihhCcSI6+9pj6BFUhGjNBJapczYidG9uwJNliIn81rMHL6WxZVzojsYm0sRgrfnybmuJ2RJK90aVwkZySpnJE0OSOalpwY2brVeC/RLa+sBA48UP89ZaEaFiN2yOaMuDkjbr1BZBETWJ1ep7zcmFSDhmjE196yRZ90ly/X/7aKkSVLjHi2ygTWJMWIlzMifs4g7kgWckZoG0mUaprcc9OWM5K0GBFDk3GKkY6OZFvIp9EZSbqaRmwCGLcYIVdk4MDCYyyleSMsRuxQ4Yzs3Gn8P6wzQgew0+tQqCZo8ipgFiMffKBfFffvbyQ/7b+/PlDu2WO4Jl4JrE45I93dxlW3qnWA/EDbJRumqagwrviiEiOqc0Zov/p1RhoaDDEu81nT5oykac0jt/HBqQOr39Je8TMmGaqxOiOcM2J+fxJpcZX22iWvEikt72UxYoeKnBEK0TQ0FC6GJQsNTISTw0JiRIUz8umn5nwRamNfVqaXKwLGCRM0TCPuV3ps0mEaq8UtihEgXN5IEjkjI0bot37FSFWVv6vztImRYnFGWlvlwmRpESNpdEbSJEaSckbsLlC/8AX9ds0aNe+lCBYjdrg5I7KlvWHzRYBCMeL0WjTxWJNZ/SCW9oqVNCIUqiGCihFxwCAXJUkxks8XDpokRmhwDSNGglTThA3TBBUj1dUsRsIg44xommHhW8UIHWfWxRydED9jko3PnEp7S7nPSFrFCH1HKetQXOH9kBJEpTMSNF8EkHdGLr9c35ZLLw3+XmLi4uLF+u9WMTJxovlvFc4IrWacZDUNoF/Zicm/VmeEJuiwzohMO3ggfJhm5Ej9NmpnJM05I0kMtuLx4fQditto7cBaXa0fc3v26Dkg4qJzdoifMQ3OiDWBlcM0urtMY0tcfUbcwjRuF9sJws6IHSr6jKgQI9aByMkZ2Wcf4Ne/NiagoO9FA+OSJfotlfUShx9u/tspgdUrZ0Sc+NyEX1TQhDFggFGJZM0bcQrTBJlw4wrTiCv2hgnT+PmsaXNGxMmvuzv+lYRlnBHxfqszAvgTg2kJ01idkTSsTeP23suW6T9RIobiaGxNgzPCYiRDyDojbgdO2EoaQN4ZUUEuZ4idfF4/gcaMKdwe8T5yNazIhmmqq5MVI42NxuBpLe+NO2dERZhGLMdNImckDU3PrPstyUUYS0mMZMkZaW/X89/GjgV+8AP70n4VJClGyBlxEyMpW7uJxYgdsjkjcYdpqBNoVIjbeuCBha3nASNU06uXsZqvFdkwjeiMxKXSNc1ejMg6I3ElsAYZxClE06uXcayEESN+q2lke5NESSmLEc4ZkcsZ2bVLP241Dbj1Vt0BpnW3VJIGZ4TDNBlHhTOiOoG1b1/7gUslohix5osQlMTqlC8i/s/LGUkiTNPWZlj3jY2G2EhajKhwRkiM9Osn1+tFRBSIspOhppnFiGxvkihJWozIVNPQ/WVl9oKenZHgyIgRekwup0/WK1cCRx0FXH212gnajxhRWdqraRymKRroy+rsLLzSizNnpHdvY7CKMkRDiO9hzRchJk/WT64vftH5dSh88/nn9l0haQBNIkxDYqK8XB8443BG/K7a6/Y4N6ist39//2IkSJimrc34fqkEPOm8kSQ7+wL+nBGniws/vUbSIkbSkjMiI/xpm2pqgKVLgalT9XH+V78C5sxRty2yYiSfN+YZFWJk2zbjM9q1e6D5ravLe0mTGGExYofYF8Q6mPkt7Q0jIsrKjIkhjMMii/geTs7IPvvoHWH/+Efn16H9l8/b76MknRGaLBoa9Ak07pwR2XbwYcI0QZwRMY9HNoGVzgUx3yhpMZK0M6JCjPhxRsSr261bk+s6mkZnxOm9xfGnb1/gvvuAK6/U79uwQd22iN8zfdd25794nKgQB+SK9O9vvzyI2/yWICxG7BC/QKuVJeuMkF1LA3tQ6CopbmfESYwAwLBhxiRth/g/u+SwJBNYxXwRIL05I6rCNGJYyo0gzgiJkV699PeUeU7UZEGMOHVfJWj/y7R3t34+ugiKm7TkjPgJiYo5cRTOUHm82Dkjdtsk3qdif7mFaAD3+S1BWIzYUVFhhEesB6esGLE74INAYiQOZ4TEyLBh4ZJlvQ52uwTWpMSIbM6I3zVDROLOGRHDNIDZzXMiiBgh16W+Ptz+UUkWxIhKZ4Q+H41XSSWxptEZ8QrTiGNzFHkUsmEa1WLErccIoG8LbQ+LkZQjNqlxc0Zk2j1bmxr5JU5nZMIEPZfi+OPDvU4uZ5zcbs5IEmLE6lg5OSP0d5acEcoZ6ddP36802cmEaoJU05DISZMYSTpnxE8Ca1gxomnG56Puy0nljVjbwWchZ0Tc/1GMQ0mJES9nBEhlEit3YHWipkafSK1flmxpr5cVK0tLi34bpqGZLAcdBGzeXFhSHITaWn3f2YmRNCSwWsVI2vqMhM0ZAXR3ZNu24GJENkzTu3d6xEiWnBGnCxXZfSm+/ogRwPr1yYkR60J5aXZG7FzrYnRGvMTI7t2pEiPsjDhhN0nm8+ZJK8hCWH656Sbgt78Fzj473OvI0r+/c/8QP9AkbnewpyGBNc6ckbiqaezECCAnRpxKe+2qoQg7ZyTpxmfW/Rb3YBtnmEY8Z6jJXVqckTT0GZFJYCWSdEZUr6fj1mOEiLu/kwQsRpywU8rWCUsmZySsGBkyBLjgguAr/yYFTeJpTWCllvV++4zs3ev/ai/I2jRhc0YAf2LErh18d7c5LGlFFCNh2uWrJElnJJ837+uoS3vFsYnESFI5I2lxRjhMk9kwDYsRJ+wOTmsioNuqnHTQhc0ZySpuOSNpSmCVdUbEdXj8Xv3HFaYRc0aA4GKkrs44bt0mRE5gNbNrl9lJissZqaxMNmeks9MY75LOGUlrmEa2tDfOMA3Apb2ZwO7gtF4lOl3liqWUUXdNTSuyYZq4T4qgOSPl5UYztyjESNJhGtGtyuXkwi5pzBlJMoHVuq+8Vu31EiO7drm7r2LuFTW3SkKMiOOiNUyTZmckLWEacTu7u91Do16I3VfdwjTsjGQIu4NTVox4rT1RCriFadKYwOrkjND/xefE4Yz4FSPiir1hnRFATlyksZomSWfEukp1UGdE7E3kdqylRYzQuVNebhw/acgZ8ROmiSuB1avPiNNjZNm50/gMHKYpEmSckaALYZUCaS3tDdpnBAg+4caxUF5rq9FSmsWIQZLOSFAxUllp5F647U87MbJxY7gr6yCIDc9oWYAknJHubvPyHV7OVBqdEbu//UAhmr593fMMWYxkCJmcERlnpFRzRorFGRHFSFBnxE8cmyqZ/A5IlC/Sq5exT2XFiKYVihGZhFTOGTEjK0Zkyv5l9qedGNm7t9ChiRprwzMgmZwR63edxaZndn/7QSZ5FWAxkilUOSPl5Wq3KytkrbRXFE2aplaM+Kmmoe3xOyBZ80UAeTEixqmDOCOcM6KjyhkB5PYnnVvV1fpxQ0nWcYdqrK3ggWScEet7ZbGaxu5vP8gkrwIsRjJFmJwR8cqHbMtSQyZMk1ZnpKPDmJxVixGvCYq2x+8gHkaMiNsXNkyzc2f8YQKRNDgjXoJSRozIlPfSZ6NzLam8ETtnJImckaw6I7IiSgY6XryW82AxkiHsvizZ0l5VPUayjEyYJg3t4O1yRsRtVi1GnLLlwzojFKYRByFZMSIOhlYxIlNNI4qRfF5uLZyoSIMYGTDAflsIVc6IGKYBkhMjWXNG3HJGurrkFpaUIQlnxM7RtYPFSIZQUU3DYiRdYRqxYZmbM0IndFmZ+TtUIUYA94x62m9xhmnEQZw+r8xkKOaM1NQYg3uSoZo0VNOQIAzaDh6QW7nXSYzE3fgsLTkj1vfy04FVTPZUdcwE6TNi97cfrJ1wnWAxkiHcckYo9BJmsCl20pjASiIilzN6htjljIhXF2KYLUoxkmTOiDg40+eVSWAVc0aAdOSN0GehXK04B9uknRHKE0gqTJO0M+I3TGOXM2L3OkERHRh2RjxhMeKEmzNCiWIyOSOlShpLe2my6N3bqFpxc0asJ7SKahrA/rixipGgOSNhwjTilaLfnBHZ50QNDeT02TlMEz3WVvBAMjkjYcI0FRXGmKBqgk6iz4isGOG1aTKEW84IDRQcpnFGJkwjOiNxnBTW5FXAPWfEekKr6DMCuA9IYXNGwjojRNbFCG1TMYsRsZoGSD6BNWvOiHi853LqL4ySyBnxG6bhdvAZwM0ZoYGCE1idSWMCq50YoZO2q8v43uiEVuWMZClMQ3hNhmKiKosRnaScEWs1Tdw5I3bOSBpyRvyEaYDiECMcpilC3HJGZJ2RUs4ZCRKmibok1E2MAMa2qg7TBElgTaK0106MOH1W0UlKY85IkmEarwTWKJqeAeyMWL9rPx1YAfUTdBKlvSxGihC3MA1NSmEGm2LHLUxjl8AqrnQcFXZihBaGA4xBNQkxosoZscsZ6ehwnxS8nBE7kUjnQi5n7Kc0iJEknRGqplHhjPjpM2JNYP300+jPJRG30t405ozYHe9AcTgjTq6uFRYjGUImTMM5I864hWnsnBEg+onDToyIk2kUYqSry7xeBhBfzghNyIC7O2I3ONNn7eiwH7DEEA2JOZneJFFTbGEaP6W9AwboSZiapguSuHBreiY2EIyasGGaKJ2RuEp77Rb4tIPFSIaQCdNwzogzfjuwAsmIEaCwosZLjLS3y2+rOECK+SlWwlTT2K3YC+hXY/QZZMSI+F3U1xvVBXZX52KPEaKUnRFNKxQj1oXbiKiqacrLgaYm/fc480bsnBHxs8Xl0ljDjaXojHCYpghxc0ZoUuKcEWdkE1jFkrqkxYhXzgiFPcTX8kL8THTlqLrpmd2KvYRM3ojd4FxW5t5rxJq8Csj1JomapHJG2tqMzp0kRgD371pGjLS1OQtTazUNkEzeiJszAsSXN0LvQ8ekn9JeQP0ELTowaSvtZTGSIbi0NxyyHViB+Cpq/DojVquzvNyY5PyKETEcpDpnhPJFxBV7iaBiBHC/Orc2PPN6fFwk5YzQ8VBebuwHcXtEZC5WqJeR+NpWrNU0QDKNz9xyRoD48kZof9B376cDK1Aczgh3YC1CwpT2cgKrfAdW8TbqicO6Lg0hmzMC+J9wxc9Kx4NMzoifq0m7fBFChRixmwztnBEWI7qI8JqIZS5WKiqM785pf1rPIyA9zogotOJ2RtwcSPFxceaMcJjGExYjTqgo7S1lMSJb2is+Nm3OiN0J7TeJVVaMqHBGVIsRt7BL2nNG6HPHNdiS0G1oME/EQcUI4L0/0yJG7JyRXC7+XiNhwzRxOCN2C2VyaS8AFiPOWA/Mzs7CRdY4gdUZ2dJe8TYuMSJa4IB8zggQvRgJkjNiV9ZLyIgRuz4jgFyYJm1iJKmcEVHo5nJyOQJe44NXea+bGIkzgdXOGQHi7zVC+0PWGUmizwhQuCowh2kAsBhxxvpliSv2ctMzb8TmXdaTLys5IyrEiPhZZcr7gjgjUYdpguSMxFXOaSXpMA0dHzIhuWJ2RoD4e43IOiNJdmAFCs9/VWKku9v4bLw2TRFhPTDphKuo8FbenDNiTqqzHvDWUtKkxYifnJEwzojb1XKY0l5VYRpr8mtQZ6S72yze4yQLYkR2fPDqNWJXTZNEAmvanBH67ru67EVxEh1Y3UqdVYkRcbtlwzS8Nk0GsB6YNPj26uWejARwmAYwnwziSSI2ASsFZ8RvAmuYME2czohdzkhtrfEZk2p8ZhUjdk3noiBKZ8RJjNhV08TtjOTzztVncYsRawIrYL//k6ymAaITI+ISDX7ESFIupgUWI044OSO9ernb7QCLEUAvcaTPLyaxigNTnGJE7CQatM+I+NxNm+ROYhkxks8brxUmgTVozohXAqtsNU0ul3zeiDWBFYh3EUbKR1IhRugzOLlMbmGa3buN7yhKxHPb6ozEncBqdUYAdzHiVE0ThRgpL3feJlVihL6L6mqjd5MTooBNiTvCYsQJ+rI6O/XJggaE+np3u128v5RzRgD7ihrxwI8zTCNOqE4JrDLOCLkP//VfwPDhwHnnAQ88AHz2mf37yogR8W9RjMhesaQlZwRIvvGZNW8AiGewtZaNqxAj1uPSip0Yqa83jtvNm91fXwVuV+NJ5YyEdUaiCNOUlRkCwckZoWUVwooRL1cEcA+jJwSLESesbcpFZ0QM07jFJEvZGQHse42IV0m0H+MUI/X15qsUwF/OyFlnAccfr2/zxx8D994LnHMOsO++higQEfMxnBw18W/R6pZto51EmMbOGfF6ThxYE4GBeJ2RpMVILmeI7Tjydug9amoKr8bTGqZJorQXcA7v03a7LRchg+wiebRNJH6yKkY2bNiAc845B/3790dtbS3GjRuHJUuWuD7ngQcewEEHHYS6ujo0Nzfj/PPPx1a7gTtNWJWjmDMiDiLWShGAE1gJu/JecXKmkyFOMWIN0QCFg77bSd3SAjz5pB7H/9OfgB/9SH9+ayuwenXh48WyWT/OiN3jnAgrRpxKe6m01O5cTbsYERdhTKsY8XJOg4gRoFBcR4lT8iqQXAKrmCxu995JlPYCzmIkTCWdiOwieYA+9qasvNeXGNm+fTsmTZqEyspKPPnkk1i2bBluueUW9KVBy4ZXXnkFU6dOxQUXXIAPPvgADz/8MBYvXowLL7ww9MZHSkWFMVmKzogYpgHcyzRZjOi3dmEacSBIixiRyRkhamuBr38dmDMHGDFCv8/uSlSmmkY8hsT3lB3Eo8oZGTJEv92wodABtEtgBZIVI/m8cXFQWZl+MRLWGbGrpgHcux+rxqmsF0iu6ZlXsngSpb2AtxjxqtL0wk+YBkidGPGV1DBnzhy0tLRg/vz5PfeNGjXK9TmvvfYaRo4cienTp/c8/l//9V8xZ86cAJsbI6Qc9+zRvyy7BFZAP3BEF4XuA1iM2OWM2E18cUwaTq3gAX85I3a4TRp+ckbKyswTi8yg1NkZXZhm2DD9tq1Nd4LE13fKGUlSjIj7Ky1ixG4iVh2msY4/VnEdJTLOSNxr05ALuWePvzBN1M6IU5hWtTOSUTHiyxl5/PHHMWHCBJxxxhloamrC+PHjcdddd7k+Z+LEiVi/fj2eeOIJaJqGzZs345FHHsGJJ57o+Jz29na0traafhJBHMzsSnsBd2ek1BNY7cI0dtZyHA143JwRPzkjdoQVI3QM0QrGlNMiMyg99ZTuCDQ1GcvHi4TpM1Jba6xAu369+X9pDNM4iZE4BluxHTy9v3WbiChzRoB4wzRuzkhSOSNu55qmJVPaC0QfppHtvkpkWYysXr0ac+fOxejRo/H000/jkksuwfTp03Hfffc5PmfSpEl44IEH8C//8i+oqqrC4MGD0djYiN/85jeOz5k9ezYaGxt7flpaWvxspjrEL8sugRWwFyOcM6LjlsCaVJjGWkkDhHdG6KrQK0zjJUasV1Ayg/i99+q355xTmJgLhHNGAD1HBvAvRpLoMyLu16qqeBs7WcWumyugQozk88brJBmmSVPOiHgcO+1/cW2YOBfKA+LLGSkFZySfz+OQQw7BrFmzMH78eFx00UW48MILceeddzo+Z9myZZgxYwZ+8pOf4K233sJTTz2Fjz76CBdffLHjc2bOnImdO3f2/Ky3DoRxIV5ZiTkjuZz71SuHaXTcwjR2zkiWckbsnm83aci0g7c6aW5X1SKffQb88Y/67+eea/8YUYw4lQrLiJGPPzbus5a6i6TFGamoSEeYJkwHVpnjCigUI3GGadKUM2In/N0WoYvSGdE0ZzHiFKYtcTHiK47Q3NyMAw880HTfAQccgP/93/91fM7s2bMxadIkXH311QCAL33pS+jVqxe+8pWv4Gc/+xmaqX2xQHV1NaqtJ1gSiFdWojMC6AdWdzcnsLrhFqZJYwJrW5v+3VESpKzdKRumoU6gbmEaQF6MLFigP+bQQ4Fx4+wfQ2KEOmXafSY3MUJ5I+IFgegApSlnhD4HJZ/HJUba2433iCuB1a5fD5G2apq4+4y4Va7ZNV0kVE7OYpWl39LeuMI0cYp1CXw5I5MmTcKKFStM961cuRIjqJrAhra2NpRZ6s/L/+EqaClpQ+uIeHCKOSOAmtK9YieNYRqvnBFxW1WHabyujOiYkh3EKUTj5IrQtlFVmFOoxm+Yhs6FsrLC5Mkkm55Z92PcywwAhjiLWoyIE6b1e0tLNU1Spb2yYiTKahpr/hLAYRoPfImRK6+8Eq+//jpmzZqFv/3tb1iwYAHmzZuHyy67rOcxM2fOxNSpU3v+PuWUU/Doo49i7ty5WL16NV555RVMnz4dhx9+OIZQ6WBaEQ9Oqy3ttj4N54zouHVgTWOYRhQj4pW1FyoTWAE5e/u994B33tEfe9ZZzo/L5Yxj1kmMOPUZAdzFCIUsRdIQpklqzSOxoV5czojYr8f6vFLNGZGpXBNbNxAqJ2cWI77xdel+2GGHYeHChZg5cyZuvPFGjBo1CrfddhumTJnS85iNGzdi3bp1PX+fe+652LVrF+644w784Ac/QJ8+ffDVr341/aW9gHMCK6BmsCl23Jqexe2M0ORIk6WIOHjTCV1TUzhYORE2TONUAug2KJErcuqp9v1FRHr31oWIKmfEqccIkA4xErczYlc2HqcYsZJEmCYNOSMyCaxux3rSzkjYPiMZr6bxHUc4+eSTcfLJJzv+/14aJAW+//3v4/vf/77ft0oep9JewN0ZYTGi4xamidsZoZVP7Rr00ckrOmCyVxeAfJiGjhUvZ8QrTNPRAdx/v/77eed5b59XRY1TaS9gTmDVNF2gOfUYAcxihB4fF9ZmVkmuBu1WWmr9vp0Qr5Q7O83jiYwYiTNMkwZnRCaB1anhGaC2+kpcb8bqlrEzYguvTeOGnTNCV4OcM+JNmjqwuokR8eSl1ud+xIiqahrZ0t4nn9QraQYNAo47znv7ZMWI3dXi0KH6gNreDnz6qX6fU1kvYIiRzs54JkORpHNGZMSI+N3LOiNA4b5MmxhxyxlJYwKrmzOiMkwjfsccpnGFxYgbdjkj7IzII9uBNY5+ELJihLqZqhIjftrBy1bTkPv43e/KCd4wYqSqShc9gFHe6yZGevUyrgTjDtUknTMiI0bs7HsnxHwQ67HlJka8mqWpxKnEG0hnAqtT91UgmjBNnGLEz0J5AIuRTCGTM8IJrM747cAa1aSRzxsTo50YEatCgjgjYZue+amm2bIFWLRI/33aNLntCyNGgMK8EbeckVwuucZnSTsjYkM9FWIkl3MWFk7r0gDxOiNuwjSNa9O4HeviRVHYSk83MeK0TXEulAewGMkUMjkjnMDqTFpKe3ftMpJHnRZ1pBNYdZhGdTXNQw/pj58wARg7Vm77wooRa68Rt5wRILkkVqeckagH26icEcD52EpLmMZNmMbpjGiav9Jeu30v7suw2yzrjIgdYb3ESHc38Pbb9hfAAIdpihq3nBEO03iTltJeCtHU1BT2xSDoBP7sM/PfMkQlRuwGpQ8/1G9POEF++1Q7I25Xw0ByYsSPM6JpwKZNat7XTzUNfddiYqMbXmLE7niOM0zjdizEmTMiTurV1c5CyC1MI+7LsBO0rBgR942XGPntb/UGh7feav9/DtMUMTSY7dxpXFlz0zN50lLa65YvQtBAECRnRCZM4yeB1W0QpwHHyZWww0uMuPUZAfyLkaQan1knGrdcpNtvB5qbgf/5n/Dv6+aMOE2GshcqaXdGZMRIHM6ItbNqkDCNeF/YsSiIGPEq7V29Wr+1NB7tgcM0RYw1jwCQS2DlnBGdtJT2+hEjUVXTqArT+LVigXhzRoBsOCPvvqvfvv9++PcNEqYpBTESZ86I+B0HDdOIjQ6jcEbsLkb8OCO0TU7ncdAwTRbbwZccdGDSBFVTo7bDYrHjFqZJqzMSZc6IbDt4t2NLtRgRF/Ry6jhrXSxPNmeE9ntc+OkzQqEVFRN22sRIEmEau2MhKWeksjJYNQ2gbiwK4ozQeOkkRmibVImRuHKqJGEx4gYdHGTdi419OGfEmyyFaWhbk6im8dP0TLUYEd/DyxnZsEEPV3qFaej+OCZDET/OCIkRFQOxXTWN03eoSoykoZpG09KTMyJe5ORywcI0gLrQhV8x4iagCPqMdOxayXgHVhYjblidEVGMuJX2cs6IjlvTs7SGacL0GaFOmSJBSnvdBiW/Aw7gLkbcVjElmpv18ufOTmDzZm8xktQg56fPCO2LpJwR2bEhzWGaPXuMpNG05IzQ/nB6b7cwjfj8KJ0R8ZgIIkZUh2lYjGQA+rKowsLOGeEwjTNpKe0NEqbxM9nLdspMY86IjBipqNAFCaDnjXjljCQtRuIO0/ipplEdpkmymoZEqfieInHmjFjHlaw4I6I4CiNGNI3FSFEjVtMA5sGXE1i9cevAaueMdHUZVUsqITFit0geYU0e8zPZV1frzgFQGKoJUk0TZ84IfR9epaZiEquXMxJXszErSYdp0pIzQsdGd3e0IRKx91KZzVQSpzNi3R9ZyxkJG6bp6DDGThYjRYj1qsMuTMPOiDNizgjZuW4JrOL/VeLWfZWwnsB+Jnu3TplBqmnizBmxxtqdsBMjTgmsSQ1yfhJYVYVpuroMAZo2MQJEG6rxEqVx5oyodkbiFiNVVe5uu7hNduex+D1zzkgRYj3ROYHVHzQoapoxCLiFaYBoTgw/YRrCz2QvPl+cNMTPHaSaxu6KMqqcEafBmRArarKeM5LPqxMj4lWqn3bwsmMDjTl+xIh4ERVlqEZWjMTpjMiKEa+ckaQTWO3a0dNn7OwsPKbpOC4rkz+2WIxkCKszIp503PTMG3H/0cliN4iKJ08UzkgcYsSuokYchFV1YA3jjHz+eWEYzK8YyWLOiHU7xO8o7DZSiKamxrwPvb7rKKtpcrl4kli9xEgSOSNeCaxeYZoonRG3PiOiGAH0EJsVcZusoRqx+6qbwynCYiRDuIVpnJyRfN4Y8EvdGRGtfzrg7SY/sdlQVsWI3aQhfhYVYZogSWqAOZwiJh0C9jk8dpAY+egjYxuizhnJ54E//1m+X4lszog4kIedrO3yRcRtSCJMA6RDjKTZGfHKGUnSGRHvFxG3yepy+u2+CrAYyRRuYRqnZETx71IXI3ZXaE6TX9bFiJ2dLtsVUjZM09npP0kNMIeIrIOYX2dk+XLjvqhzRp55BvjqV4EZM+QeL5szolKM2FXSiNugSow4JUY7rbUUR0VNGnNGvBJY01DaG1SMiNvkJEb8jAssRjKEjDPiNNgALEaAwooap3VQohIjmuav6ZnT317YTRr0WSoqzLFcJwHrFaYRJxY/V0C5nHPeiF8xQpNvebnzVbmqQW7tWv12zRq5x8uuTSPuA3ZGgpMmZ0Q2gVU2TBOlMyJuk11pr/UxhJsY8btIHmD+rHY5KjHDYsQN64kuU9orHkSlnjMCFHZhdZr8ohIju3cb8de4wzSyV2vWgcvpijJIkhoRVow0NZmP5/p659i0KqubPq81tOREkDCNqpwRMXlV3IZSFiNJrE2jKkyThDMiltZ7iRFrzkiYMI2m2RdixAyLETeClPZymMaMdVB0GkSjEiPkilRVuQuMKMM09Nmc3DTZpmeiFSubpEaEFSPl5cDQocbfThMQoC4JkD6vU8dJK0nkjNBr+RUjUXZgdXueStLojFgTWP2GaZJqB+/Vxh5QH6aJuorRJyxG3AhS2ksHUVmZfSOgUsMpZyQuZ0QM0bhN4KqcEbswjV9nxOlxQQYcwkmMOIXN7KBQjfh6dqga0GkilRUjsjkj1jBNGIuaXsu6P9zyfsT/exGkmgZIhzOS1No0gPP+T7MzIt7GEaZhMZIhgpT2co8RM045I9ZBNKrlrGXyRQB1OSNuzggdE93d5glQtpomSI8Rgq7crfaurDMCmMWIjDOSVJjG2mdETPwFzPtAXLU4CF5ipJTDNPQ9dHfbl6qqxG9INK6cEbtybxkxYhc2UR2mKSszto/FSMoJ44xwvohO0jkjsmIkyjCN9WoNcF4sS7yNwxmRLe0F5MWI6pyRtja5ycwpTAOYr5CdBvIg0P60hmmiXrU3C9U0XgmZVsKEc1R1YE2rM5LPm5+nIkwDpKqihsWIGzI5I17rjJQ6aQrTuBFnmAYwHzdBckb8EjZnBPDvjKjKGQHk3BE3MSITbw8COyPezgjgLTSWLNErkmbNCrYtsiHRtOaMeIkRt1AjwGKk6KmoMOcZyJT28iJ5ZsQwjaYll8AalxiRqaYBzMeN9ZjxCtOkQYzI5Iy0t4fLxxD3pYwYsU404meSsbiD4FeMhOnAKu7LLIgRP87I66/rE+JLLwXbFutx7OVMRe2M2Iker1V7xVsvMeLWgdUPLEYyQi5ndkfsckbYGXFHDNOIuRJxOyNuK/YC4XNG/FTTAPZhGq8+I0HiwgRNlmFyRoYNM36XcUaAcN+nOJHKJLFaJ5pczj4mbt0HYQbiuJwRwLydWaimKS83ylW9nJGwqyirSmCNY6G8ICFa635R0YEVYDGSKcST3U/TMxYjOuIVmrU9ukianJHqav+VUDJhmvJyw2mzc0aiDNNQTkMcYRpVqzCHDdOI25LVMI34XYvCIgvVNIB8rxESI0G3V3UH1iRKe8XHc5iGKUC8yuMEVv+IYRpxQIrLGdmxQ7/1U00TZLKXqaYB7AebrJT2DhxofBavCci6JlEQgjojXmJEZZiGXstNjIjhFb9ipKLC+G68ji2RtIgR2V4j9P0GPV5Ud2BNWwIrh2kY08kuU9rLOSNmxDANDRi5XKFYS9oZEQf9IJO9TDUNYB/e81vam1TOSC5nhGrcckbE8GaYQU7clzJixO6qV8YZiTJMA5grgYI4p25CN83VNIB8rxFVYRpZZySJhfL8lPYGdUY4TFPE0JclLvoGeDsjLEZ07MI04mq+RNJiBDC2NYwz4hamAfw5I05hmqRyRgAjVCO6hHaoGORUhGnsrnTjTGAVt8tpG72wCguxb0exOCOqwjReCaxpXihPvI07TBPFAqU+YTHiBR2cdXXmCZSbnskhDopuE18axAgN+qrCNHY9POxyjWQXyosyZ0SmzwgATJsGjBkDTJ7s/jgV32fYBFan7bCutBtFnxEvMeInjGs9ttxyr4ioxUhHh/FZVOaMRJ3AGrbpWdClCQAO03jAYsQL+rKsV4KcMyKHeLC7xbmzLkZkqmkAOWfEa6G8pMI0AHDuucCHHwJf+IL749LijLiFaZqawm1je7vxnnE6IzJiJOowjfh9uLlkcTsjqsI0duPQq6/q48j113tvj6wY8VvaS/taVZhGVVhKASxGvHBK2OPSXjmSdEY0LX5nJEiYRraaJumcET+kNWekq8t43UGD9NugE6C4TdbxwalySoUYoX1ql3tFRO2MkBiprnb/LHHnjMh2YA3S9GzxYj089uqr3tsTlTMycKCxfeJjuJqmBPByRjiB1R07MRKXM9LWZnw/ceWMiM2p3BJYw1TThM0ZEas70ixGoqimEV+HnJGwYqS21l4UyHzXMjg5I9XVzos/xiVG3EI0gP9qms7OYOvYuDkjfo53t3Fo5079dutW7+1R3WeEtmfAAOM+8VjmME0JQAen3zANixEdsbTXrYw0CruQXJGKCu+ES0BNmKa72zgG3JyRINU0KnJGurvN+zgqMRJWXIr7EfAXpnHLGaEBvLra2CdhxYhTZZGdwxXkYsVJjDhV0tg9RzWyYsRvzggQbAxwKu0F7KuZguSMqBIjYfqM1Ncbx7R19WmAq2mKGvqynMI0nMDqjl1pb1xhGjFE43QFKaIiTAMYoZqwYRrrVV0YMSKKMXEQ89NnxA9hBzmrQFDljNCk19BQuIijX2TFSJTOiBNZckY6O83bGWSbrfvEaV0cFc7Itm3e26M6TCM2ubNLRucwTQnACazhsCvtjStM4ydfBAgnRiorje+cJg3ZahqnAUnTzFd1YXJGysqMiUMcxNIaprFe0YcVI7QdYpOysBO2U8MzgsWIXM6Iir4vbs6I+N5+ckas6yqRGNm929vpiarPSHW1fZk+i5ESwClMw03P5LDrwJqEMyJDmJwRoLCiJqgzIu4fuyS1IDkjgP0g5re0VxbVzkiQhfIA5zCN6IxEFaaxm4jjEiNpCdPIOCMq1gpySmAFjH2uad5hGtqnmlZ4oUliBPAO1USVwCqKEc4ZKTGCOiMsRnREK7yYnRHx+X7DNE4DkvVxYcI0gL29m9ackTBhGrecETFME3YgduoxQkTljHitSwMYx0h7O5DPy7+XLCpzRlSsFWQV1WVlxvpStM/dFuok3BZ5FEWTV6gmqtJeuzBNd7fxOpwzUsTQl+vUR4DFiDtJlvb6FSNjxui3++8f7P2crmC9qmmcckYA8yAeVozYXVGlNUzj1xnJ541JV8YZURGmyULOCBDNRJM2Z8TuOLbuf3EbvDqw2m1H2pwRu3LoDDsjnNjgxZQpwLJlwNSp5vu9Vu3lnBGdNIRp+vSRe/z06cAJJ3g39HLCT5jGbUAqL9ev6vJ58/EVJmcEyJYY8ZszIu4nvwmsSYgRFR1Y3appxGNkz57goT0nVOaMqGjPb3euVVXp99MxLm6D0/FeVqZ/b52dhWORKjESprTXLkwjnisZbgfPM6YXBx0ELFpUeL9TmIZzRszIhmmiOClkV+wlysqCuyJA8DCN1Rmhx4kdPoFoc0aiCtOEdUZqavTX8BIjTle91uNKTDpVFaZJozNSUWFMqm1tQP/+8u8nQ7E6I4C+Xzs73Z0RVWEav6W9dmEaOleqq43QlCwpckY4TBMULu2Vg8SIWL6X1jBNWKzOSNBqGvF3lWEau5yRqEt7w+aMUGOy3bsLqxtEnK56k0xgTVKMANFW1KjMGVHpjMiIkYoK91J/u7HIWn6cVJimpqbwoiLMuMBipAjgBFY5xBOErizSmsAaFplJwzrYiOW7VmdEfFw+bwwYpRSmITGiae6VIeLgXV5u/J7WME0cYoSel6QYicMZ0TR74e8kRryOdbtjV3RFgGBihH4XE2lVhmmCjAu8Nk0R4OWMcM6IjhjXprBJsTojQcI0opgVBy5rrD1MkhqRJTFCE2j//sZVrFsSqzioi1e9TmJEDNPEKUZUdGCVqaYBjOMkivLeNPUZ6eqyr5Kx7n+vsl7CbiyyipEwYRrAuAAJIkacwjRBwrfsjBQB7IzIUVZmnPw0ERSrGHFKYLUbIOm4EY8fO2eExII4YUYhRlT3GVGVM1JXZ9+szYpTMyuZME3QbUxz0zMgHWGaIM6I3+0VX9uawCr+Py5nROxn4iRG6LyXLe0VBaj1POYwTYljZ7kBnMBqB50kHKZxd0bcwjQ04IidXv0SZwKrqpyR2lo5MeJ01VvMYRq3ahrxeVnLGfE7MYqvLZMz4rXvZZwRNzEidk32EiMqSnvDhGlYjBQBdgcWwM6IHXTAy4RpOjrcExX9kIUwjVNJqtXeDlvWC8Tb9ExVzkhdnTH4yoZpRKJsB59U0zO/zkgUYRr67GlwRmh/5HLugl42TCPjjLiFaZzOaVVihMM0jAnxIGMx4o4fZwTwXvdBBnEtnDRX04jHjph46eSMhBEjWcwZqa21324rXmLELkwTV2mveDyXaphGps8InT9BnZGqKnO+UNAEVjdnhBKq3ZwRJzEint9OAilIAiuHaUocUeWGbWpU7FjFiJszAqgJ1ZArUl7uPFmoJkiYRjxe7AZSa85ImOZV1kEsnzfEUFr7jMiGaWRzRoopTJOGahqvc0vGGaH9OGiQfhvUGbEew0HDNG7OyKhR+u3Wrc4OrpMYEVvUOzkjTs00OUzDOOIUpuGckULogHdzRsSBRKUY6dPHvaeASpzCNG5xbLuGZ+JzonBGaBBzirWrQGXOiJ8wjWzOiBim6ew0x/llyOe9J2TVHVg7OvTjJUvVNH5yRsh1COqMWPeHVQipqKbZZx/jNZ32K71PLmd2Q4DCwgeVYZowYqSrq7AYI2ZYjATFznITf2cxYiDjjFAbZkCtGIkrRAOYwzRdXcZaKW7t4J2OF+ugFEXOSBxiREXOiJ8EVjdnRGwNLoZpAP9X4yQ4AXkx4lRl4YXohonhx6TCNKIgUpkzQmIkaDWNrDMimzNiJ0aGDDGe7xSqcfuOvc5/P2Gazz/XRbSKnBHxPRKCxUhQxGQpzhlxhwZFuvr0Wr47q2JEtNPFzyBTTWO9UnYK06jMGUmzGAnqjLiJEVHM1NeHW0yOXquszPk7sbpbTlUWXogThnhsJVVNIwoxlTkjFKbx+104ibOw1TR2YZrGRqBfP/33IGJEnDPsxKmfDqyAfk6EGRtUh8dDwGIkDFaVC3DOiB3Wk8Tpiq5YxMjnn8uLEaeByylMoyJnZO9e/XVpcC4vL7STw6IyZ0QmgdVpohGvcun5dXX6+Sn2wPE7YYvhHqcwoGzllBe5nL3QTSpMQ6KwosJbxHo5I5qmLkwThzPS2Gis8+NUUSMrRsS2EH6ckepq43G7doVzTSsqjG1KOG+ExUgYZNcZKXWsV3Bezoh4UmzcCHzwgf/39LtirwrEMA0NfNZyQ6dqGidnJIqcEUAfxKKqpAHS2WdETF61bqdfMeKVvArId9uVIYwYUe2MiPkiXvlYXjkjbW1GODNomEY2gdVvzoiTM0JiJKwzYidOZZqe5XJmgR72QiUlSawsRsJg54xwAmshQZ0RTQMmTwbGjwc+/tjfe6YlTEODB+FWTSNiHcRV5IxUVhoDT1xiJC19RkQxIgqIoF1YvXqMiNsS1hkBzEI36Woa2eRVwNsZof2YyxmTfFQJrGGcETp2VIZp3MSINZnU+p2LyehhL1RSsj4Ni5EwsDMih/Ukkc0Zee89YNkyfZ8uX+7vPanBWtJhGq+rNSfxGoUzApivqNIsRqLoM2InIIK6B0GcEXGc8BsWE4Vu0tU0QcSIU86I6FYFFU9+wzRhc0ZUhGk6O837xE+fEcB8ToS9UMmqM7Jhwwacc8456N+/P2prazFu3DgsWbLE9Tnt7e249tprMWLECFRXV2PkyJG45557Am90anBLYOWcEQO/YRo68R5/3Pjf5s3+3jPpahqvpDrr2jSypb1hckaA+MSI+F0G6ahrF6Zxc0a8+ox0dBgTStJipLLSf7l5WsM0Xng5I6IYCTopyiawqijtjSJMI5YAy4oRsTKuSMI0vmbM7du3Y9KkSTj22GPx5JNPYuDAgVi1ahX6egz4Z555JjZv3oy7774b++23HzZu3Ig8xQmzjKqmRsVO0DCNKEa2bPH3nmkK04jIJrBGEaYBzPYuDWhROiOA/hn8LsQnDrB+nBGnnBEA+Owz/VYUEEEH4rBixC92x1ZS1TRRiJEw7fmjSmCNoppGvBixe5zdnCI2J4wiTJNFMTJnzhy0tLRg/vz5PfeNoo50Djz11FN48cUXsXr1avT7x5c4cuRI/1uaRuycEc4ZKSRImObjj4G33jL+lwVnhAZ/TXNu8JZkAitgvqKiQShqMbJ3r38xIoovFX1GAODTT/XbNDgjfkljNY2MGPFKYI3TGQm6UF5Xl1HOrLKaRlaM2FXmlXqY5vHHH8eECRNwxhlnoKmpCePHj8ddd90l9Zxf/OIXGDp0KL7whS/ghz/8Ifa4nPzt7e1obW01/aQSdkbksF7BOQ2iYuLYokXm/2VJjIjvH9QZiaIDK2AfpvErFGQQP0+QQU5VnxFRaKVFjAQJ4WY9TCOTMxJ0UnRyPIJ2YLVuhzj/NDSoC9PYiSNZMVKEYRpfYmT16tWYO3cuRo8ejaeffhqXXHIJpk+fjvvuu8/1OS+//DKWLl2KhQsX4rbbbsMjjzyCSy+91PE5s2fPRmNjY89PS0uLn82MD256JkcQZ4RCNKNH67dZCNOUlxufgd5fNoFVtulZVnJGcrngg5ymqUtgFTv7uoVpitEZyUKYRkwqDiqe/K5N4zdnhFzOmhr9uaqrafyIEdr2IgzT+BIj+XwehxxyCGbNmoXx48fjoosuwoUXXog777zT9Tm5XA4PPPAADj/8cJx44om49dZbcd999zm6IzNnzsTOnTt7ftavX+/vU8UFNz2Tw68Y2boVeO45/ffvfU+/zYIzAhgTgJczkkQ7eMA8iEUpRoDgvUY6O43eE2I7eJkEVrdFGN2cEb8DsV2ZsJWoxEgWq2miDNM4OXxBwzTW41bMFwGSC9OIbQJKPUzT3NyMAw880HTfAQccgHXr1rk+Z+jQoWikL/Ifz9E0DR879I6orq5GQ0OD6SeV2JX2cs5IIX4TWB9/XB849tsPOOYY/T4/YqS93bhaiFuMUEUNDVReA2ScC+UBZns3LjHid5ATJ0/RGensdBY2bhMAfQd2zkgcYRprmCDunJEknRE/OSOiMPRTgSWbwBq06ZlVjJAzsm2bIZpF/Jb2ui2kCdh/33ZhmrBiJEvt4CdNmoQVK1aY7lu5ciVGjBjh+pxPPvkEu4WrmpUrV6KsrAzDhg3zubkpw80ZYTFi4Le095VX9NtvftNYr2LLFvkBilyJXM69KVUUyDojstU0We0zAgRvpkSfNZfTt40EHuAcqpERI3bOiFuYZutW4Gc/A9auLfxfkKZnYS5U7HrYZClM45Uz0rt38EXb/Caw+m165uSM5PPG/0RUOyN2TphdmKaUckauvPJKvP7665g1axb+9re/YcGCBZg3bx4uu+yynsfMnDkTU6dO7fn77LPPRv/+/XHeeedh2bJl+Mtf/oKrr74a559/PmrDDqxJw03P5PDrjBCnnmq0iBb7RHghtoIvi7mvn5cY8VtNE2XOiFOsXRVBBzlReFE7fTqGnEI1bucdbQc5I7JhmvnzgeuuA26+ufB/SeWMtLYaotyrtFcM0wTp9eJE0DCN3TbYOSOAPwHl5YxYO7D6bXpmFSPV1YUOqIgKMSKuW2Mntko9THPYYYdh4cKFePDBBzF27FjcdNNNuO222zBlypSex2zcuNEUtqmvr8czzzyDHTt2YMKECZgyZQpOOeUU/PrXv1b3KZKCm57J4TdnBNCt0COPNNv0sqEa6r4a57o0BA1Sfp0RrzBNKeWM2AkvryRWmZwRstRlwzSbNum3a9YU/k9GjFi/QxVihI4rQD5Mk887OxNBCCJGgMIW54DZYaLFCwF/E6OTqLbuf1XOCOCexKqiz4j4Om5hms8+M0RLxsWI7xnz5JNPxsknn+z4/3vvvbfgvjFjxuCZZ57x+1bph0t75ZAt7RXvP+kkY4IeNEgftLZsAfbf3/v9khQj1kkjC+3goyjtBcLnjIiftb5e//7DhGkI2dJeOo7sctuSckb8iBFRzO3Zo050BskZAfTjzfrZRWeEKrDa2oI5I6o7sDo5I4Aeqlm/3r8YkS3tpdepqnJ3RsQqw6CuKa9NUwRw0zM5xElFbH1sRTzZTj3V+J1CNbLOiN3gEReyzohXO3inME2WEljD5oyIn9Wr10hQMeKWM0LHUZrECIUFysu917epqjKqL1RW1AR1RuzcGetKykGqm6LqwOrmjLhV1KgI04ivIyNGxBJ2v6TEGWExEgbrxCLG+ViMGIiTijhAWqGTraoKOO44435KYs2CGLFOGqqbnmWlzwgQPkxjdUYANc6I7Kq95Izs3FkogpIWIzJuVi4XTUWNHzEiimy7ihpriXSQiTGqDqxuzkjQMI0qMULirbtbv6X8qiCwGCkCrMmIovJnMWIghmncBlEakI491jzIixU1MqRBjDi1g/fb9CyqnJE0l/a65Yw4OSMyOSOE3zANAGzYYPze3m68X9wdWJ0cN6/nJSVGqCIKcBcjVmckigRWv2EaGWfEToy4iR47MSJuj+gcyzgjRJiLlJSIEc6yDINTAyuAE1hFrM6IE9/5DrB6NXDBBeb7sximIYfMq5rGq7S3o0M/vugYy2ICq4qcEa8EVllnJJczlwrLhGkAXYxQvpK4DUk0PQO8K2mIKBqf+REjgP55OzrkxIhKZyRsAmtXl+480DaqDNOIfUbszv3ubn9iJMy4wGKkCHAq0wTYGRERTxS3K7qmJuC22wrvz6IzQoRtB9/ZaZ4oVeWM7N5tDD5ZyBlRFaaprzeXe8uEaQBz3ghtQ22t+0VHFGKEkHVGVIdp8nlj0ThZMVJVpT/HmjPS1WVsV5TOSNAwDaALgaBhGrvzyitMQ3/v3VsoRkQBWlenH8dUIVYEYoTDNGGwJrCyM2KPrDPiRBZzRggVOSPiwCx7ReyEeEXlVPGjiihKe8MmsFqblDlNfprmLUbcXBFxW5IUI6rDNHv2GK6fHzECFDojdg5TGGdEVQdW64rTfsM0YUp77bbbzhnJ5czHXxGEaViMhMEtJhw0magYEfsHBJn4shimIZzECCU7y1TTiNZ82CZuNTVGTJqagKUtTBPEGZHNGbEKCKcwjXhlCphzRoKKERUdWAm/zoiqMA2JQTE51gsnMULhD1qADgjnjKjqwCqOV07OiIpqGienxrrdTmsR2SViB4HFSBHg5IywK2JGHLiC9LTIcpjGaYAEzFdHMmEaFR2LxSsqEiNZ6DMStTNi3UbRFQGy64yoDtPQ/u/VS14YO61PY7fYYJBjRnUHVnE7RDEiHjtRVdOIf7s5I9btUSFGsrQ2DWPBKYGV80UKoZMlTJhGXBTKjayIkc5O56tluzCNquUTaBCjgTQLOSOqElhlwzRWMaLCGSmGMI3f5FXAeX0aa/Iq4J5Q7ITqBFbxtdrajO9bRZhGpRhhZ4TpwSmBlcVIIXTAB7kKb2gwnicTqklzmEZ0QMSMejdnhJyCsD1GCBrEohYjKnNGwiSwijkATlUIXmIkjDOiYtVe62STVDVNGDHilDMiu1aQE6pX7QWM/UuLKwL2CaytrYUiKwkxwjkjJQ47I/KEcUZyOSNvRCZUkyZnxGmABNydEXEiU+2M0CBGE27ackaChGlkc0aszojTlTgdQ7Sy+ObNxnv4FSP5vHl9mCDjQ2Wl+XlJh2lUiBE3ZySKBNYgzgiNN1VVZgHYt6/xu9iiX3y/IH1G7LY7rjANi5EM49QzgnNGCgkjRgD5ihpNs+8LEBdednp5uZHcLIoRt4XyohIj1vdSTZwJrCpyRsRVZUmo7befsX82bjRvg6wYoe0Le7EiHltZCtN45YzINqFzwm8Cq5+cERpvrGNJRYWx9pU1VKOiz4j4OlGHaXhtmiLAKYGVnZFCwoRpAHkx8vnnRovkNIZpAP/lfVHljBCllDPi1ixKDCeJiy0OHar/TqEau0nUDicxEvRiJYgYSVOYxilnJK4EVj9hGqszYjeWUKjGWlHDYZpAsBgJg8rSvWInrDMiG6Yhe728XF2OhR9kEg3F48ZPaa/qnBEiCzkjUVXTiNa7eDVOx1GfPkaohpJYZZ0R6yJxSTgjWQvTBNneKBJYvZwRwDmJVbbPiGxpL4dpGE/YGZEnTGkvIO+MiPkiSfR68StG/DQ9iypMk4XSXtk+I37FSGWlUaIqbqebMyIrRpySlUstTBN1zohMAqum+QvTyDgjQcRIGGfEmrSsupqmvd0cqowZFiNh4ARWeeLKGUkyeRWQC9OIcWM/7eCzljMSRZhm717zsguEmwXvFqZxWtmWxEhjo+GM+BUjuZxcjoAsohjJUjWNU86IWzWNrHjSNO9Ve0UhAqhzRootTCO+VwKwGAkDJ7DKQwd8XGGapMSIVzUNIOeMiJUYNAFkLWckigRWwD5UEzRMI26n6jCNuD1ulVOyZD1M46fPiOwxIwpTt8o1Wk/H7nF2pMkZiasDq/heCcBiJAxWZ4RzRpxJIkyTBNYrVq8EVq9qGsAYtEs5Z6S62thvdqGaoAmsgLszEiZMI24Ph2miCdO4OR7ifhYFrJ8wjeqckTSW9lZWGiFtFiMZRXY5eKZ0wjRlZd6Tht0E5RSmAYzPlLUwjcqcEcA9iTVozoj4PnY5I2KYJqwzkmQCaxqqaWTawft1ckSh65TACpg/v5/SXvq8aQ/ThBkbcrlUtIRnMRIGzhmR57DD9IP+0EODPZ/CNFu32ucMEEmLEcCfGPFqegYYg3bWxIjKnBHAPYlVNmfETYw4hWnIGdmwQQ+bZVGMZKXPSFBnJJczFoAkxJ4+FKYRXQA3rPtXtTPidjxYL3KjzhkBUlFRw2IkDJwzIs+0afoA/53vBHt+//5G1YPYotlKFsSI3YBkPWbEgZU+k6owTVZyRqyf18kZoRWQgWBhGrucETFMM3iwfux1dek5BFkSI2kK01hzRlQksIoTtZ3IoH1NYkT2WLeGW92ckahKe+lCJeowDcBiJPNwaa8/ZAZvJ8rLgYED9d/dQjVpECNiRY1XAquTM5LLGc/NqjMSxPrN540B0SlMY3VGxEnOa22aIGGaykpdkADA+vX2k6gTUYmRLFXTxJEz4nQM076mzy+77/04I1kP0wAsRjIPJ7DGi0xFTRrESJAwjZ2bRo+LOmckjj4jsv0LxMFQNkzjJUbo85WX2+9D69W4uDghtfymUM3KlcbzsuCMpCFMYydGxGUbVDgjXmIkCmck7j4jUYZpUtASnsVIGDiBNV5kklizJEbcBiTxvqwmsIqf3XpV7IQ4CckmsHr1kGhq0sMsw4fbW/nWMA3tb8CYKCmJ9cMP9duyMrnvQ8yXSKIdvOowjZ8QFUHbSuKDtoeWbQjTDt5pXRqCjgc6ZmSPdbeQCEFhmj17zPs3LjEiCkJ2RkocTmCNl6yIEQrTVFQYeS4iss6INUyT1dJeQH6Qo0G9srJwn3g5I3ZJjIAuRp5/Hvi//7N/T2uYhkI09fXGNpAzsny5ftu7t1wiZFqckSTDNAcfrN++9JJxHx3TuZw5rOnXGYkqTCPjjDQ2GsebGKqJqrTXuk1lZcD+++vHRXOz82eRgcVIxuEE1njJWpjGacKQnaDoPtVNz6wTSVRiRHxd2bwRt26zXjkjbhPN0UcDBxxg/z/rBChW0hBWZ0TWGUiLGOnoMJyIoGhaMDFy7LH6pL1qFbBmjX6fWNYrCna/oT0n14AIGqaRyRnJ5eyTWONqegYAr7yiC2SZ/CU3WIxkHM4ZiZesOSNOA6RMO3ig8DhSJUbKysyTSVTHq9i/QHaQc+oxAjiHacJO8tYwjVhJQ5AYWbXKvC1eJN2BVXxO2Immvd0QNH7ESEMDMHGi/vszz+i3TknA9F3k8+4l/ISsMxJFzghgjEkksoDwpb3iMSPuB7vvvH9/oKXFftv88I1v6BWPw4eHf62AsBgJA+eMxAud+FlxRrwGSK9l5a3PVyVGAGMydQolqcJvYpybM+IUpvGzGqsdVmdErKQhKExD35esGBHLWpOspgHC542IItC6BpMX3/iGfvunP+m3dsmrgP/t9euMBKmmqahwPveOOkq/fe454z6VzohbUzeVzJwJ3HuvIRoTgMVIGLi0N14oTOPkjGhausSITJjG7WrZep+qnBHAmEyjCtEQfp0Rpx4jQHTOiFPOiJ0zYt0WL5IO05SVGd9x2LwRMVxol5vjxnHH6bfPPqsf83bdVwHz55I5ZuJwRtxWAJ88Wb999ln9VtPU9RmJU4ykABYjYbDG9jhnJFq8wjRtbYaNnOYwjV01TZxhGsC4Io16gPPbaySIM6JKjLjljJAzQsjG6FWKEdGN8PO9qaqo2b5dvw2Sn3DooUDfvvq+ffNNZ2dEDO3JbK+XGKH7w+SMuI0lX/2qLvg+/FBfu0jMy1HtjER94ZAwLEbCwM5IvIhhGrvkNppEysv928gqUeWMxBGmSZszIpMzolqMOOWMiJNQba2RrChuixdJOyOAul4jtDaPVZjJUF5uuAh/+pOzGAH8HTNxhGncxEjfvsCECfrvzz3n3fMmqBipqpKr3sowLEbCwAms8UIdWLu6jKs0ERIjDQ3JnrhBxEjczkhcYkRlzohXn5GwOSNuYRrAHKrJohgJG6YJI0YAc96IWxdbP+LJb2lv0DCNGySynnkmOjFS5CEagMVIODiBNV6qq40Jwi5UY3dFmwTkyjgNfDIZ9Xb3lXrOSJJhGsA8CWdJjKgK03z8sX5rzZ+R5etf12/feANYt07/PW5nRHWYBjA+17PPmhvwqegzwmKEkcKp6RnnjESHW0VNGpJXAd26BZxj63TctLcb4SavapqyMrUil7YtLjGiss9IXKW91uNIlTOiogOrbDUNoC5ME1aMjBihN+nq7gYef1y/z02MqHRG/HZg9eOMTJyofzebNwPvvqvf59SAj50RR1iMhIGdkfhxq6hJixg58UTg2muBG2+0/z8dH+Jg6+WM1NaqDT2lIUzzySeFuT9uOSNxOSNpDdM0NOihyoED/eVEqQ7TBBUjgBGqodey2492Cxc64bU2jbWSSHXOCD32n/5J//2JJ9zfx2+fEafuq0UIi5EwWBNYOWcketwqatIiRmprgZ/9DDjsMPv/24kRr5wRlfkiQPJhmkce0cMec+aY75ct7c3njftV54ykNUxTWalfeb/7rj93RXWYJmjOCGCIESJsmMZrbRpVpb1eUN7Ik0+a39dpe9wuRMRjxq37apHBYiQMdNDk8/oPOyPRk4UwjRdBnBGV+SJA8mLkzTf121dfNd8vE6YBzFf5WQjTiCG5MOPDkCH6jx9kwzRLl5oXs7MSNkwDAMccY/78cSWwRpkzAhh5I7R2kZczIoppDtMAYDESDvHqxGsFVkYNWQjTeGHN8Ae8c0ZUOyNJ54x8+ql+S4mMhJsYqa01usWKoZq4wjRhnBHxu457fPAK03z+OXDBBcC4ccC//Iv9Y1pbjX0exhmprweOPNL4O62lvX6dkbFjjbHJ7X1kquZYjDC+EQ8irwZWjBqyEKbxgo4PmvxyOffVfQH1YuToo4GRI4HTT1f7ulacckacxIhbzkguZ583orIDaz5vuANuOSN+m54lKUbcwjRvvw0ccghwzz363+RYWaEcj8ZGf+vS2CGGauJyRuj4C+KMyHzXZWVGqEZ8XyssRhxhMRIG8cBSsRAW4w0tlU2Do0hWxIh1gvKKLwPqxcg+++iLe33/+2pf14rTVS6F2bZvNwsLt5wRwJgIxYoalWvT7NplhFOsx5E4EVuFihNpECN2k3s+D9x6K/DlLwMrVxqhn61bjfNIREWIhhDFiJ3DpNIZsR4TUeWMAEaoBpAXI2VlhVU3LEYY3zg5IyxGomPECP127drC/2VNjNDk4OSkiQOn6pyRuHCaWMgZAYD1643f3cI0gH0XVpU5IxSiqa4urGDI5YBbbgFmzNBLVGWQDclFiTVMs307cOqpwA9+oO+7004D/vpXI8zw978XvoZKMTJ+vL7SbEWF/etF4Yw4/e2E+DjZ8SSIM+KWK8ZihJGmrMwot1SRLc94Q2Lks8+MODCRVTGShDMSF145I4A5VOMlRqgl+2efGfepCtN0d+vOAODsfFx0EXDbbfJl1lYxUlERf3dgMUzz3nt6+/L/+z/9u5k7F3j0UX0p+n320R+3enXha4TtvipSXg689BKwZAkwYEDh/50EbD4P/Pd/Ay+/bLhXfsWIrDMirpEjO54MGwaMGWP/vgSLEUdYjIRFbHzGOSPR06ePMThY8w2cSjLThqwzUgxixC5nZM8ec5hF/B7dckYA40padFNUiREA2LhRv1UlaIP2uVAJfb7nn9cbdK1erecLvfoqcPHFhjjad1/9NmpnBNAvKg46yP5/TmLk5ZeBqVOBr3xF/3niCe/S16BiBAC+9S3dxdlvP/nnkDvCYsQ3LEbCItvam1EHuSMffWS+v9ickSiraeLCbmIRXRHA3hlxCku1tOi3ohgJmzMiDvSbNum3qgStnTMSN3TsrFyp79/jjgPeekufaEXiFCNuOIVpxPP9lVeAk04CFi7U/1btjADAAw/o+8nPc048Ub+1c3yAws6sLEZ6YDESFrHxGSewxsPIkfqtNW8kK2KEjhmvCSrKPiNxYRemkREjTuLLToyEvQgoKzMGe3JGohIjSYwN4vnwb/+mh2jEFYgJtzBNnGLEyRmhCroTTgB++EO9Cy2Fa5yOF6d1X2TxG1I7/nhgwQLgzjudHyOe77JipAQ6sHI8ISx2YRoWI9Fi54xoWnbESCnmjLg5I6KoTEKM0Pu1t0fvjCQxNpx6KjB9uj5RnnCC8+PcnBGVOSNeODkjVIF14IHAL38JXHMNcMcdwPvvF3Z2JcI4I0HI5YCzznJ/TEWFITK8xEgJdWBlMRIWDtPEj11FTVubnoAIZEeMeDkjxRCmscsZoUmlvl7PHfGTMxKVGCHRpDpnJA1ipE8f4PbbvR9Hzsi6dfo+FXt0UMJwGpwRqvrp3x+4/nr314pbjMgQxBkpATHCYZqwcAJr/NiFacgVKS/3t4hYEtAx47Zir/g4ILtixM0ZoZyFjz82hKRszsjGjYWrZYeZaGj/RuWMeLlgaaC5Wd8P+bz53CJXpLbWWJE6SpycERIj1PhQhqClvVEinu92xyyLESYQojPCOSPxYBemITHS0BB/6aRfZAfIYs8ZOeggXTx2dRkiwCtMM2iQvl/yeX3VX8BIYA0bpgGKM0wjSy5nuCNiqEYM0cRxbnk1ygsjRtgZSS0sRsLCOSPxQ87Ixo3GyZqVfBGg0AkpNWeEJpXmZsP2X7fOfA45fd6yMiNvgUI1qnJGgOjCNCoWyYsDuyTWOJNXAW9nRFwDxougHVijRFaMAIaIZTHCeMI5I/HTv7/hFNCElCUxIuuMFGvOCDkjAwcCw4frv69bZ5583Jwga96IypwR2gbVzojT32nDLok1bjFiJ2DzeTXOSBr2v7gNXmKE+vGwGGE8EUt7OWckHnK5wlBNlsVIqTkjJEaampzFiFspo5MYUZEzQrAYMe5LgxjZvt3IKxo4UP61shymAViMMD7gME0yWJNYsyxGSi1nhK5wnZyRmhr33ASrGFGZM0KoDtM4/Z027MI0cZb1AvZhGgrR9O3rT1CwGMkMLEbCwgmsyWAt782yGCm10l6nMI1XWS8RZZiGiMoZSbtrKjojlOeSBmckSIgGSKcY9BIj5eWGGGcxwkgjZj6zGIkPckaKIUxTSk3PxHVpnJwRLxcoCjHCYRqdkSP1ifDzzw3RmIYE1iDJq0D6E1i92tjTuVICHVhZjISFDixRxaf96qcYyLIzUqrVNJpmTHCVlfp3ZSdGgjojKnNGSjVMU11tiI6//91cdh1XmMbOGQnSYwTIZphGvJ+dEWc2bNiAc845B/3790dtbS3GjRuHJUuWSD33lVdeQUVFBQ4++GC/b5terH0ExPuY6CimBFaZapqs54wAumgQQzS5nCFGtm0z/icrRrZs0XNRVOSMiNtZVqZ3h1VB2LVRkkAM1WzapFeyVFT4dyWCQt9/Z6eRtKoqTMNiJLX4EiPbt2/HpEmTUFlZiSeffBLLli3DLbfcgr4SXfl27NiBqVOn4mtf+1rgjU0ldGCJlmIWBpysQ2GaDRv0q7csi5FidkbEQXTvXrMYAfQmdfSdLV+u33p91v79DfHw8cfqwzR9+qhr7pU1ZwQwJ7FSiGbIEPNqs1EiCkNyR4KGadK4/2XECD2GxFgJiBFf8YQ5c+agpaUF8+fP77lv1KhRUs+9+OKLcfbZZ6O8vByPPfaYr41MNdZ2z+J9THQMGqRf5XR06IKExUg6sYoRusIVJ5Xhw/XFzlas0P/2coFyOd0dWbVKD9WoFiMqj6E0ToZeiM4IiZG4QjSAWYzs2aMv71BMYRqvPiN295eAGPHljDz++OOYMGECzjjjDDQ1NWH8+PG46667PJ83f/58rF69Gtd7LWqURazOSC4X3xVEKVNWZlj8H32UbTFSzE3PcjlzRY3VGQGMkBuJEZnPKuaNqM4ZUZW8CmRfjFBZb1zJq4A+foqL9AH2IlaGNIoRP2EagsWImdWrV2Pu3LkYPXo0nn76aVxyySWYPn067rvvPsfnrFq1Ctdccw3uv/9+VEgmdra3t6O1tdX0k1roM3mtwMqoR+w1kmUxUszOCGDuNWInRkhUBhUjqnNGSl2M2IVp4hQjQGESa1BnJI05OyxGbPE1c+bzeUyYMAGzZs0CAIwfPx5Lly7FnXfeiWnTphU8vru7G2effTZuuOEGfOELX5B+n9mzZ+OGG27ws2nJYVXwaTjYSwWxoiZLYsQqPrwGpMrKbItc0RlxCtMAehIrENwZ4TCNGsgZ2bgRWLlS/z1uMVJbC+zapTvOmlZcYRo/pb1ECYgRX85Ic3MzDjzwQNN9BxxwANatW2f7+F27dmHJkiW4/PLLUVFRgYqKCtx444147733UFFRgeeff972eTNnzsTOnTt7ftZTCV8asYZpsjDYFAtir5EsiRFZZ2T4cH1imDw5+m2KEvEq180ZIWQqh6IUI6XujPTrZ+yDl1/Wb+PMGQHMx8znnxvjK4dpihZfl1uTJk3CCrJS/8HKlSsxgq5QLTQ0NOD999833fef//mfeP755/HII484Jr9WV1ejOis731ram4XBplig4+7DD42Gc1kUI07HTHW1Hrooy3g7IL9iJImckbjCNFlxuPbZB3j7bcOtSsIZAXQRQq5IXZ3/kus07n8WI7b4+mauvPJKHHnkkZg1axbOPPNMLF68GPPmzcO8efN6HjNz5kxs2LABv/vd71BWVoaxY8eaXqOpqQk1NTUF92cWqzOShoO9VCAxQoJXZX+IKKF2z9Ru2+2YKYZkaDFnRFyXhggrRmj/cZhGHfvuq4sRIsmckaA9RoDCyhVVJdthCCJGuAOrmcMOOwwLFy7Egw8+iLFjx+Kmm27CbbfdhilTpvQ8ZuPGjY5hm6KEnZHkoDANNQZqaEjHYCODTHlfsWBXTSPa7c3NZtHlJ0yzfTtACe5pDNOI64wA2fmuKYmVaG6O9/3tnJEgTddEtywNIRogWGlvWrY9Qnxfxp988sk4+eSTHf9/7733uj7/pz/9KX7605/6fdv0wjkjyUGNmKgxUBZCNERlpVEFUuxuGl3Vbd9uXpeGqKjQcxLoIkbGGWlsBHr31pMcaUXgNFbTAObvOivjAyWxAkZPnzgRnZEdO4zt8Iu4v9Myoft1RqqqsnORFYKMB6NTAIuR5KioMK6QgWyJEXFAKhUxQmWitC6NiBiqkS1jFr97et2gRBWmAbLpgoliJO4QDWAfpgnijKRx3/sVIyWQLwKwGAmPtQNrsU8saUNMns6SGEnjIBkVNLFQVRytSyOiQoyksekZkM3vWgzTxF1JA9iHaYI4IzJltHHDYsQWFiNhYWckWYpBjBS7gKXBVBQjVsTvUXZRwKicERYj+r6lbU3aGQkjRnI54/xKoxiR6TPCYoSRwuqMZGWwKRYoiRXIrhgp9mOGJhbKCbETI0mHacScEQ7T6LlYdG4lIUZEZyRMmAYwJvy07Ht2RmxhMRIWdkaShZ2R9GPNGbGbVIKIEWtJcJhzT3RjVDsj4tVvlsaHceP0Wx/ds5WhyhkBjH2eRmeExUgPRT4KxgCX9iZLMYiRYj9maGKhBlppdEbq64ELLwTyeb0DqUqy+l3ffjvwz/8MfPOb8b+3nTPCYqSoYTESFjqwaG2aYr/KTRtZDdOUUjWNdTD1EiNBckbKysI3iBOaNyolqy7YsGHA2Wcn894kYFtb9ZJwIHiYRlzjKQ347TNSImKEwzRhkV30jImGlhajMiNLYiSrV8tBsHaPtJtUGhqA/v3132W/RzGXIc37sJS+a1WQM0J5RhUVQN++wV4r685ICXRfBdgZCU9W2z0XC1VVevOzDRuyK0aydLUcBOtgaueMAMA99wDLlwP77Sf3unV1uoDZujXd5x2LEf/QMbN2rX47cGDwNZpIhGRVjLAzwkjBzkjyHHywfmttYZ1mSmmCkgnTAMCppwI/+pG/bpMUqknLRGNHKX3XqrCKkaD5IkC6nREu7e2BxUhY0rgqZKlx773ASy8Bhx+e9JbIw86IGkiMpHmSZzHiHwrTfP65fqtCjKRl37MzYkuRj4IxwM5I8gwYABx1VNJb4Y9SSmCVyRkJCouR4kTlMZNmZ4TFSA/sjISFc0aYIJTSBCVOLHbr0oSBxUhxYi3vLtYwDYuRHliMhIWdESYIpRSmEQfTAQPUrkDKOSPFidUZCSNG0taBlUt7bWExEhZ2RpgglNIEJU4sKkM0gNEldMgQta+rklL6rlVhdUY4TFP0FPklWQxYr2qL/SqXUUMpOSOiGFGZvAoAX/qSnrwsWw6cBCxG/KPSGWExkgmKfBSMAQ7TMEEopQkqSjECpD95uZS+a1VE4YykZd9zaa8tHKYJC4dpmCCUUjWNOJiqDtNkgVJywVTBzojxe4l0YGUxEhZ2RpgglNLVctTOSNrJ6qq9SaKyNw13YM0ELEbCwk3PmCCU0tVyqYuRUhKeqhDDNP36hdtvaQ7TsBjpgcVIWNgZYYLAYqR0YDHiH/GYCROiAYwFFcWFFZOES3ttKfJRMAY4Z4QJQilNUJwzYv8744zojIQVI9deC3z1q8DRR4d7HVWwM2ILi5GwsDPCBIGdkdKBxYh/Kir0VXrz+fACtlcvYPJkNdulAjrfy8qcVyIuQTHCYZqwcJ8RJggyV0fFQl2dnjxYXg4MHpz01sQPixH/5HKGOxLWGUkbdO67HQslKEZ45gwLh2mYIJSSM1JVBdx/P9DdDTQ0JL018cNiJBg1NfqqvcUW2qPz3a26h8UI4xsO0zBBKLUJ6owzkt6C5Ci171oV7IzolIgY4TBNWNgZYYJQSs5IqUPfdXm52kUCix3KNSo2MdKnj/nWjhIUIzwKhoWdESYIfLVcOtD3y6LTH4cfDnzyCXDIIUlviVr22Qe4+25g9Gjnx5RgB1Y+O8LCTc+YIIjHTXl5ctvBRE/amm5lhf/+b2DPHr0aptg4/3z3/4vzSIk4IxymCQs7I0wQ6LipqGDrvthhMRKMsrLiFCIylGCYhsVIWFiMMEFg6750YDHC+IXFCOMbFiNMEFiMlA4sRhi/sBhhfGPtoseTCyMDT1ClA3/XjF9YjDCB4MoIxi/sjJQO1NyKxwZGlhIUIzwSqqCiAmhv13/nAYeRga+WSwf+rhm/9O8PjB+vJ/CyGGGkYWeE8YtYTcMUN1/4gv49H3hg0lvCZIWyMmDJEr3SrkSq7XgkVEEpLXrGqIGvlkuHUaP05l19+ya9JUyWcFrRt0hhMaICbu3N+KWlxXzLFDcDBya9BQyTanjmVAE7I4xfRo8G3n6bxQjDMAxYjKiBxQgThPHjk94ChmGYVFBaQamo4ARWhmEYhgkMixEViM4I54wwDMMwjC9YjKiAnRGGYRiGCQyLERVwzgjDMAzDBIbFiArYGWEYhmGYwLAYUQHnjDAMwzBMYFiMqIAEiHUFX4ZhGIZhPOGZUwXc2pthGIZhAsNiRAXkjLAYYRiGYRjfsBhRATsjDMMwDBMYFiMq4OXgGYZhGCYwLEZUwM4IwzAMwwSGxYgKOGeEYRiGYQLDYkQF7IwwDMMwTGBYjKiAc0YYhmEYJjAsRlTAYRqGYRiGCQyLERVwmIZhGIZhAuNbjGzYsAHnnHMO+vfvj9raWowbNw5LlixxfPyjjz6Kr3/96xg4cCAaGhowceJEPP3006E2OnWwM8IwDMMwgfElRrZv345JkyahsrISTz75JJYtW4ZbbrkFffv2dXzOX/7yF3z961/HE088gbfeegvHHnssTjnlFLzzzjuhNz41kAjhnBGGYRiG8Y2v2XPOnDloaWnB/Pnze+4bNWqU63Nuu+0209+zZs3CH/7wB/zxj3/E+PHj/bx9emFnhGEYhmEC48sZefzxxzFhwgScccYZaGpqwvjx43HXXXf5esN8Po9du3ahX79+jo9pb29Ha2ur6SfVcM4IwzAMwwTGlxhZvXo15s6di9GjR+Ppp5/GJZdcgunTp+O+++6Tfo1f/epX2L17N84880zHx8yePRuNjY09Py0tLX42M37YGWEYhmGYwPgSI/l8HocccghmzZqF8ePH46KLLsKFF16IO++8U+r5CxYswA033IDf//73aGpqcnzczJkzsXPnzp6f9evX+9nM+GExwjAMwzCB8SVGmpubceCBB5ruO+CAA7Bu3TrP5z700EP43ve+h9///veYPHmy62Orq6vR0NBg+kk1nMDKMAzDMIHxJUYmTZqEFStWmO5buXIlRowY4fq8Bx98EOeddx4efPBBnHTSSf63Mu1Q/otLHgzDMAzDMPb4upS/8sorceSRR2LWrFk488wzsXjxYsybNw/z5s3reczMmTOxYcMG/O53vwOgh2amTZuG22+/HUcccQQ2bdoEAKitrUVjY6PCj5IgZ54J7NkDnHpq0lvCMAzDMJkjp2ma5ucJixYtwsyZM7Fq1SqMGjUKV111FS688MKe/5977rn46KOP8MILLwAAjjnmGLz44osFrzNt2jTce++9Uu/Z2tqKxsZG7Ny5M/0hG4ZhGIZhAMjP377FSBKwGGEYhmGY7CE7f/PaNAzDMAzDJAqLEYZhGIZhEoXFCMMwDMMwicJihGEYhmGYRGExwjAMwzBMorAYYRiGYRgmUViMMAzDMAyTKCxGGIZhGIZJFBYjDMMwDMMkCosRhmEYhmEShcUIwzAMwzCJwmKEYRiGYZhEqUh6A2SgtfxaW1sT3hKGYRiGYWShedtrTd5MiJFdu3YBAFpaWhLeEoZhGIZh/LJr1y40NjY6/j+necmVFJDP5/HJJ5+gd+/eyOVyyl63tbUVLS0tWL9+vevSxkx4eF/HC+/v+OB9HR+8r+ND1b7WNA27du3CkCFDUFbmnBmSCWekrKwMw4YNi+z1Gxoa+MCOCd7X8cL7Oz54X8cH7+v4ULGv3RwRghNYGYZhGIZJFBYjDMMwDMMkSkmLkerqalx//fWorq5OelOKHt7X8cL7Oz54X8cH7+v4iHtfZyKBlWEYhmGY4qWknRGGYRiGYZKHxQjDMAzDMInCYoRhGIZhmERhMcIwDMMwTKKUtBj5zW9+g5EjR6KmpgZHHHEEFi9enPQmZZ7Zs2fjsMMOQ+/evdHU1ITTTjsNK1asMD1m7969uOyyy9C/f3/U19fjn//5n7F58+aEtrh4uPnmm5HL5XDFFVf03Mf7Wh0bNmzAOeecg/79+6O2thbjxo3DkiVLev6vaRp+8pOfoLm5GbW1tZg8eTJWrVqV4BZnk+7ublx33XUYNWoUamtrse++++Kmm24yrW3C+zoYf/nLX3DKKadgyJAhyOVyeOyxx0z/l9mv27Ztw5QpU9DQ0IA+ffrgggsuwO7du8NvnFaiPPTQQ1pVVZV2zz33aB988IF24YUXan369NE2b96c9KZlmuOOO06bP3++tnTpUu3dd9/VTjzxRG348OHa7t27ex5z8cUXay0tLdpzzz2nLVmyRPvyl7+sHXnkkQludfZZvHixNnLkSO1LX/qSNmPGjJ77eV+rYdu2bdqIESO0c889V3vjjTe01atXa08//bT2t7/9recxN998s9bY2Kg99thj2nvvvaedeuqp2qhRo7Q9e/YkuOXZ4+c//7nWv39/bdGiRdqaNWu0hx9+WKuvr9duv/32nsfwvg7GE088oV177bXao48+qgHQFi5caPq/zH49/vjjtYMOOkh7/fXXtZdeeknbb7/9tLPOOiv0tpWsGDn88MO1yy67rOfv7u5ubciQIdrs2bMT3KriY8uWLRoA7cUXX9Q0TdN27NihVVZWag8//HDPYz788EMNgPbaa68ltZmZZteuXdro0aO1Z555Rjv66KN7xAjva3X8+Mc/1o466ijH/+fzeW3w4MHaL3/5y577duzYoVVXV2sPPvhgHJtYNJx00kna+eefb7rvW9/6ljZlyhRN03hfq8IqRmT267JlyzQA2ptvvtnzmCeffFLL5XLahg0bQm1PSYZpOjo68NZbb2Hy5Mk995WVlWHy5Ml47bXXEtyy4mPnzp0AgH79+gEA3nrrLXR2dpr2/ZgxYzB8+HDe9wG57LLLcNJJJ5n2KcD7WiWPP/44JkyYgDPOOANNTU0YP3487rrrrp7/r1mzBps2bTLt68bGRhxxxBG8r31y5JFH4rnnnsPKlSsBAO+99x5efvllnHDCCQB4X0eFzH597bXX0KdPH0yYMKHnMZMnT0ZZWRneeOONUO+fiYXyVPPZZ5+hu7sbgwYNMt0/aNAgLF++PKGtKj7y+TyuuOIKTJo0CWPHjgUAbNq0CVVVVejTp4/psYMGDcKmTZsS2Mps89BDD+Htt9/Gm2++WfA/3tfqWL16NebOnYurrroK/+///T+8+eabmD59OqqqqjBt2rSe/Wk3pvC+9sc111yD1tZWjBkzBuXl5eju7sbPf/5zTJkyBQB4X0eEzH7dtGkTmpqaTP+vqKhAv379Qu/7khQjTDxcdtllWLp0KV5++eWkN6UoWb9+PWbMmIFnnnkGNTU1SW9OUZPP5zFhwgTMmjULADB+/HgsXboUd955J6ZNm5bw1hUXv//97/HAAw9gwYIF+OIXv4h3330XV1xxBYYMGcL7uogpyTDNgAEDUF5eXlBVsHnzZgwePDihrSouLr/8cixatAh//vOfMWzYsJ77Bw8ejI6ODuzYscP0eN73/nnrrbewZcsWHHLIIaioqEBFRQVefPFF/PrXv0ZFRQUGDRrE+1oRzc3NOPDAA033HXDAAVi3bh0A9OxPHlPCc/XVV+Oaa67Bd77zHYwbNw7f/e53ceWVV2L27NkAeF9Hhcx+HTx4MLZs2WL6f1dXF7Zt2xZ635ekGKmqqsKhhx6K5557rue+fD6P5557DhMnTkxwy7KPpmm4/PLLsXDhQjz//PMYNWqU6f+HHnooKisrTft+xYoVWLduHe97n3zta1/D+++/j3fffbfnZ8KECZgyZUrP77yv1TBp0qSCEvWVK1dixIgRAIBRo0Zh8ODBpn3d2tqKN954g/e1T9ra2lBWZp6aysvLkc/nAfC+jgqZ/Tpx4kTs2LEDb731Vs9jnn/+eeTzeRxxxBHhNiBU+muGeeihh7Tq6mrt3nvv1ZYtW6ZddNFFWp8+fbRNmzYlvWmZ5pJLLtEaGxu1F154Qdu4cWPPT1tbW89jLr74Ym348OHa888/ry1ZskSbOHGiNnHixAS3ungQq2k0jfe1KhYvXqxVVFRoP//5z7VVq1ZpDzzwgFZXV6fdf//9PY+5+eabtT59+mh/+MMftL/+9a/aN7/5TS43DcC0adO0oUOH9pT2Pvroo9qAAQO0H/3oRz2P4X0djF27dmnvvPOO9s4772gAtFtvvVV75513tLVr12qaJrdfjz/+eG38+PHaG2+8ob388sva6NGjubQ3LP/xH/+hDR8+XKuqqtIOP/xw7fXXX096kzIPANuf+fPn9zxmz5492qWXXqr17dtXq6ur004//XRt48aNyW10EWEVI7yv1fHHP/5RGzt2rFZdXa2NGTNGmzdvnun/+Xxeu+6667RBgwZp1dXV2te+9jVtxYoVCW1tdmltbdVmzJihDR8+XKupqdH22Wcf7dprr9Xa29t7HsP7Ohh//vOfbcfnadOmaZomt1+3bt2qnXXWWVp9fb3W0NCgnXfeedquXbtCb1tO04S2dgzDMAzDMDFTkjkjDMMwDMOkBxYjDMMwDMMkCosRhmEYhmEShcUIwzAMwzCJwmKEYRiGYZhEYTHCMAzDMEyisBhhGIZhGCZRWIwwDMMwDJMoLEYYhmEYhkkUFiMMwzAMwyQKixGGYRiGYRKFxQjDMAzDMIny/wEhWlb9otF30QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [i for i in range(len(listed))]\n",
    "plt.plot(x, listed, color='r', label='Entropy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28388437-7c1f-4cd9-a906-3b4fd31a7bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_frames_dir = './selectedFrames'\n",
    "# if not os.path.exists(selected_frames_dir):\n",
    "#     os.makedirs(selected_frames_dir)\n",
    "\n",
    "for f in selected_frames:\n",
    "    image = cv.imread('./grayvout1/' + f, 0)\n",
    "    cv.imwrite(f\"{selected_frames_dir}/{f}\", image)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}