Open_CV.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "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"
    },
    "colab": {
      "name": "Open_CV.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/princetyagitech/Computer_Vision/blob/master/Open_CV.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jv2sHiP_J1JD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3vOhdUqJ1JR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KsqoucrIJ1Ja",
        "colab_type": "text"
      },
      "source": [
        "# Loading andShowing images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9q4t4sfeJ1Jd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Command for showing the image\n",
        "img=cv2.imread('lion.jpg',cv2.IMREAD_GRAYSCALE)\n",
        "#IMREAD_GRAYSCALE-0\n",
        "#IMGREAD_COLOR-1\n",
        "#IMGREAD_UNCHANGED=-1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eGr-W7VJ1Jl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cv2.imshow('image',img)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "grtuPYCmJ1Jt",
        "colab_type": "code",
        "colab": {},
        "outputId": "3264aa96-adb4-44e5-df23-8008256c3542"
      },
      "source": [
        "plt.imshow(img,cmap='gray',interpolation='bicubic')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J2gR4Bf3sBMuutLzMEfhniqqLQbhkAO/3gBuQdPYfMaUas9fvmnSFccCO8pjNZnNAH4VXhcp68nrWJg78ERuNgMbjaD5ZHp4WJQJezV8BGKh7tUTpaVt0hdXVH/PScjOOK4kobOY+GaXpbD0b53q9tiqIAD+71ye/mgLO2pz51eQ2pOfOgD0Qs/gI3DJW7/8j5uvXszdAdW3Gqk1eQV7ZfQT4rJNu4NaYfU0JqfhTl95OXRM4Y6MRo1rUfq8rgdrA1ziLKoVIuWT5OSnwuDXmHoXXMaXx1DwVTfyaicXL58w8AhkFMb2m4br80LN8XRQINV1vr4hla1lVAXRdi8rIsmg5Wedav3u9IlCP4tXE20Tj9xnHrmCYZi3sInInwL420PndxWqB+tECtd8Z2LobpK8AhsPhHItX840zfD9TP2L9qmAWOT5Br/vE9AFBMONvZ8HRQGM4TiKfTBGo6n9nv5GiUCDsw5yiyZjl7+DeJz8P1+Vl49cy8PM02XcZoGlZNFwGLFlaEcN0wFPF5eGA7pMwOXZrZEL7wJWdkgkPF7F5DauKJVvNaDtkZfW2jRRGphAyMqR1idLuAu4ufOwrdwLsgfkO5H8He/6OgI/iikDBWsNo2BrgO/A70GcfBXm9FuXncZifD+5IidXax8VBucZoNVzkwZKxZwdZZ78KchpmUanVMbrfx85eq4NO0Aj4I8D3OjuQKvOP0te29bSzTUauKFjPWjsp4NWAg3k689YNZ5Uszcx84uWNlECkdLO+dhCPTD1dZcrSjsZqV3myeJ7GbebBInJnwB54GagBzDHiPuabKJ0o3WwVoPczoI9MNxGzpznHr2n6kULoKl9UJ1d43q4UZR0ZwDmAs0y8FpllasDv/3VgO9vvw5xqDNd/R+WKgCMCbYrXwSe3A1C0WuB9fUApy7MPqEaA2ieMyiKAr2myzowX1Tda2Wgc7ysvryoXDRsBeZeZh3XNVh3Z9Whc6u/at0rXNR/ntf+exiJy58AeeNnWzAHpv7uAMTN59PGVj+5nQJ+Bf7Zhy7TdVdMVnNeB7RFd6yvqi18D59oqgIxUD/daBOx96Z1NVjUXab4KGryWxWH7ZEqH/70M2aSLANrNVDVgyEDQf9dAsU/bKZAD3X75NfZbY+9u0nPxenj7uKLMTDaaRhQm2qvos4qohc8AWdvOFbdL1i41EI/yiJTOonJnwN4nmg5UB7WIyUagHoVXEI2UhYIv7ynQK7A7yEc2e15nfDffOMhr2TKF5RPB2yMbmFGbE7Q1HtlalzLQ+BHo+zUth15fWlp6ifFGniq+Acd7kdmjVpaoXA5AUbvVAF/TcNB3ZZOBsIOqA533HcP0YeUeLiqbgq3mkYFN1G8R8Ef1YNgae8/AOAqjBLFLYWaKskvBanxvi9tIbXxlYbQMi5A84A6BfSYO+LX/OokcLCkOmJl7pAI/gXoRJu92elUUXkZn8tEqxusWtYWKDyK95kxYl+Oz2fxRwhnYq0LIQN/jeBkc8CNArzFPho/MHl3KR9PQ/562jhVXWN7eEdN0oHRxU0hXesrYtW1cwWidvey1jVYg3lDPFJL3m9c5qqsr9Rrwav4Zo3dlrWXIViwZSHcxfG83l6gPuxRCNEb6xFlUOsG+aZq/B+DfBfCkbdt/48W1+wD+PoAvA/g+gD/Ttu3z5roEfwvAnwJwDOAvt2377T4F0Q7UweCV8msK3pqGM2LgZX/6iMVzULkbJUFef2s4vZ6xe33RSumAwAXTlVRUH62XtwcQu01GoMt7viyPgBt4+SlQBf6uaw4aNQYeTWJOXlcCHs9NO1qGqDwRu/aJ2mUG8Thej6gPKJkZwpVyjcH7BnQEMsxb29DbS+NFgJvJIisMlpH5aT9rGpEicYCnRLZ+rWdtBUHx+H49U8b8r9IHjGvKIBsrem9R6cPs/ysAfxvAz8u1nwXwS23b/s2maX72xf+/DuBPAvjqi88fBvB3Xnz3Egc4/x2xege/6BoQ+9Nrmgr8EdD38bpRoM9WAloHN+VE7D4D+QzsI9GBEYG9swrffFNWqRPQAbMG2npPJ2EN9Jmnemn0ief5sk4ROGt5XDyMphNNuOhaBILaXg48WV96H0XC/BX4VaKNdear/adpaXtGZdLy9DGDOKA6kHaxfB33DsyZrd/niZqesnbPAF/TiRRgRDKicZe1SyZ+/zaA3wn2bdv+s6ZpvmyXfxrAH3vx++cA/FNcg/1PA/j59roU/7Jpmp2mad5u2/ZRVz6ZScI1eAT0kakmCpux58yU4+DvG638XfPOUQWQmYkc0GnjjwBf28TbJum/ue/omgPsYDCYs+P7hNfBXgN8XufkUvDkBMtMLkxfGVoN7D0dL7OvALKJ4/EjReWsNGNglBrwaD9kwMNyRGDqZY/S9XpqHgqiuprT9gfm7fOujLU8UV2junh9o+uerl9bWloKV07O8r3+/OYYj8xWfSQC4Kjds3jRN+N3KYdFgR64vc3+IQG8bdtHTdO8+eL6uwB+KOE+fnHtJbBvmuabAL4JAMPhkNcAzLN6Xq8BHiVj9dH/SAFkYE9QzxRA9iHYM15km8/Av8bsozaKxFmtX/dvBzF956ubOvjdB/AZNlp+a3rMW+M5q3OA177PyhRNZE8rY05+jcqHUmP6LhFgZey4dl/z1bSyNvX+csVHEPfVXARe3u61urKcmbjCyeLX5i9wzdSpjFxhaV6aH/em+DtbHXj5s3FSC3Mb+TzScPm8N2gj9AlL3LbttwB8CwBGo1EbgbObPZw9RJ3PD8NE16NvBeCmednF0k0zEeBHJh0HejUnZYomAnuvh9Y/ad/yW4GQ39FkdjBUwM/A0cGd6dVYtbJ1TrgobQ0L4KWXaERMvwxEyYth+wBzbfI6s4+YPr99g7aLvTM9BV3vj+g76u/IbJSBHz+Xl5dzZY9MW1qvrA6al5e9q75aLrYZQVzT0fnIuJeXl7i6uirfDO8kkfleXV29FC9qPy9btnrK7nWBtsbLxl0UpovoRXJbsP+0eWGeaZrmbQBPXlz/GMD7Eu49AJ/cMo+XxJWBd6Jfc/BkmMh848xdgTsDdjfpML5uxrrpJ8onW1GwjF7fqN4Rk9P/DsYKlBkwKRNyUFVgjpb/Gft30Gc/ZWG8Ps7StQ5+zdPWfHUsuaKojb8ICLvMDt4XEYBE/chrDiT+W/MlQEYMXBWWAidF+4PC8TebzXBxcTFXLjfPedk8LVWSDuqahhMRJ3w6D2kVIGCfn5/P1cPdm5n+bHZ9ON7FxQUuLi7mlJ7WzfsyAmBtE62Ptrv3V1+pKYNF5bZg/48B/CUAf/PF9z+S63+taZpfwPXG7EHbw16vUjPh6H0HPb8WMWHt+NrGqJtTIpt+BP4R8+8C9K79gqi+Xm9KDeQj8I0GkjNK5k/wiMCb6XJprICqcbrs6hlYK7vXzWMHlYjZRwrN8/U4NckYVWS2qLE1/V0DAu/njFnq9+XlJS4uLgqguSLy50XYx3xf7/LyNSwsLS0VIGS7kwmr0vIVVwZ20YolaptsFebzZmVlBSsrK3PsfjgcFmbvYK9joW2v2f1wOMT5+TkGgwHOz88BAJeXly+NsUxBR32qEinpPmMsC/MqoN/H9fK/xfVm7G7TNB8D+Bu4Bvl/0DTNzwD4HQB/+kXwX8S12+X3cO16+VcWLZCDOq95I+q1zMSh1zNzSHS/BtQR2Cub1zNzojhUNMpQavlk5dS2Auo22j4sW1kfkB8m5fGV6SqIKkhrXjrZ3cTSZ0ywDJoG062JlkOveV21DbOJ68rPyxhtOrpyjRSWl5fiCiQqa9u2OD8/x/HxMc7Pz3F5eYnT01NcXFwU8ONqc21tDUtLS1hbW8P29jbW19fnxtZwOCzj+OTkpJSBYM80mS4QHwPhwjyyNvf6u5LSNHRPbGVlBQBK2bgC0Xmk7aQ2e105q9CsUwP8qB9qkin3iHRFBMDjLyp9vHH+fHLrTwRhWwB/9VYl6SERqNcAMQN//c48ZSL27iYdVwzOlPw+Ad4Vht9TU4/Xh3Vmnaz9y+8MnJXB15SAmwKiCcPrEfg7A3fF4HHJLBVMFcx1DESKKJqIEXunaB5dyiZjqpkyiBhsNKGzcml62vYU79vz83OcnJxgMplgOp0WU4b2tdrkj4+PC0guLy9jY2MD6+vrhemura1hOByibVusrq6WVcLq6ipOT0/DMivL1/pmTD5Sdt7OtTHIPNRkqnNKw7i7sPYdx/pwOJwz7zCsrzK8Xn2kD0t/FcbeV+7ME7Sq9ZW9RuH8fzTxPF1NU7W950uWruzb2XkE8j7oovDRcQlZ+lpubRNXYCrOWh2YM6bPtCLW74PQQdtBUsHcwd2Zn8d1E5D2b20MRKuILGwmXeDTxbAiII8Y3CJSY49t2+Li4gJnZ2eYTqeYTCaF0ROcOZZWVlYwHA5xeXmJs7OzYoa5uLjA8+fPsbW1ha2trcLwNzc3sbS0hMvLS6yurs7ZsafTKZaXl4tCuby8nANJfhT4vW0c1GptrvOT1/yjBInpudkOuFF6vlJYXl4uoB/NM027bduX9qgi4M/GQTRGsv+fN/jfGbCn1CZEBnIRCGb/nb3zdwSyzhgiE4ubaDRt/WTn4ijoezn4X9N18IuYoH47s6oxeqbn1yj0jFAmHCkVXle2HikYZ8CeRqbII0XjYJ2xb28rzasP26xJjcFF931FoUokmvSsM0H+9PS0fAjwTdMUlqrtPBgMMBwOMRgMcHZ2htlshuFwiLOzMxwdHWFzcxNra2sl3MrKSgFr2u3Z9ysrKy8xfAI+lQ0Vi5qSImIQmUVqIKdmOyoW2thZ9ygvxuPYibDBV9paLs5DxqXSoJLwfQvWI+v7iNX7dySLEgaVOwX2fSZmBtwU18weJgJjXo8YgqYRmXb8moO8rwAiZu/3FeRrjL7GJpSdO1unX3K04Qrkm4w6sDPAddCO2DrTdebPNJiXLp89nueXsaxsBZJJBvja3jXG7+n0kT7jvm2vN15PTk5wcnKC8/PzuY3S4XCI5eXluWcjzs/PC4OnXXttbQ1ra2u4vLwsY4yrAgBYXV3F+vo6VlZWyvgje2cYjhtukpLIXF5e4vj4GCcnJ7i4uCgbvWwPZf1RO3k/+ypU24Jgf3l5Werie2IMF+EH5xvHMueDEzxVvmoecoYf9XfG2vuAusurgDzlToC9ghr/R5XLmB7vRWEytq33IhNPH48cN8XoIOobz4E+8hKqMXuXjLU74Ktpx9m1M24flNkE1TLUwDIKE/VlBLzab9lqIpvknobG1fJk+ToT6yMZs+O9Wl4ElLZtcXZ2hvPzc5yenhaAo7JcX18vD++p+yEBmMyX6a6urmJjY6MowsvLSxwcHGBjYwPD4bAwZG7Ssm0mk0kpN1cJ6g1zdnY2t+qgWYmsP1LewMuEIGvfSPnRk6ZpGqysrMythKlY9KPpMB7HytXVVdnLYLmjPuuag5FkQN9HAXTNk75yJ8AeiAvOAUr7o9/zhu/L6jWsXndmHYF4Hxu7mnn0erQ6cKDX9COA9//ZpIg+0WYZQYG/tX2zwafhIzbvfRmlH0nEoD09nxi1Ad81GZyNaX4KwpFiiPKoKYBaf2WibH4ymeDi4mKOXQMoDHx5eRmrq6uFQZ+fn2NjYwMnJyeYTqcArr1xCHpUEoPBtcvhdDrFwcEBVlZWcH5+XsCc+1FN02B9fb0oD5ZPWTbzpRknMqd4m2lds9WQ9rePJbaHpkulpyYlljOaV5xvXBkR8Nn+7DM/ClzboCaZ8npVps+26Ct3BuwzqVWqCwg1nN7PwHkRVu/xowHUJ14N6COFpspKJTK9ONATmL1d3SySgXIEsJmCIFtSBVAzbURMP2K9TFu/I8kYs+bH9LrKctvJnIX1fHylxHZTN8eVlZU5Nn5+fo6zs7MC9MvLy1hbW8Pq6iqapins/vz8HEdHR5hOp2UMLC8v4+LiAmtra8VX/eTkBPv7+8Ubh8xYzYkEfuDGNZEkjCz74uLiJXLB+nn/qwJcFBDVXAXMr+oI9ufn5+Wj/e0u0668dQ4CLz9s5qtGDZOV3393SW38aRkXkTsH9loBr3Bke3Pwc2BQpp4x5Zr5JHLNzBh8pkC6FEAtDwdXX5F4W7GNIkABXDbQAAAgAElEQVR04FemkzFzHaieX1QOL09WVmfLmaJxhk0GWDMDOYhm9em6pvH7KofIHJSJM1Y1M5BRrq6uYmdnB++88w7u3buH4XCIi4sLnJ6eYm9vD48fP8bBwUFh72traxiPxwBQgHdjYwNnZ2c4ODjA8fFxYegXFxcYjUYArkH79PQUh4eHWF9fB4Biw97Y2ChlpmmH9mqabyaTSTHdHB0d4eTkpIxhAnPWBipOQLxtdYzwmysK/gZufO5VATE/gj3dS6nYuBqgCccZfLa6U6nVs48yi67pt8bLxn8mdw7snX1H9zWcg7b+ju5n5pVFwF1BWplPxPBr8TX9iNWr4ovqE7ULJ4RvpCqYZkxLr0eD2wGN7RWBtJeTaWt45qPxI/aXMcIu4M3SY7joWjaBakzr8xSyVvb9ysoKvvSlL+Hhw4dlE5Esf3NzE5ubm/j444+xt7dXwHp1dRVbW1vF9szN2e3tbTx6dP1A+3g8xvHxMYCbJ8svLi5wdHRUNl0BzD1pC2DOvZIrBN1HILhSUYxGIxwfH3f2gd6rme+AeYWg6aq5l0chsFze32qyodKiB5E+JewmTydGn+dY8HHYpSB/bME+K7iDXLbc93QiAHdF0gXGfk/jZiy8i9k7m9cXmmR5en30PweED2YCvi6n3ZSjJhdlMhGT0Ova7oyXgaoqjgiEIxas6fr9GhjotaicfcQnmMaNWNhtJUtDWfBgMMCDBw+wvr6Og4ODOdMJvW/G4zHeeustbG5uYn9/H8+fP8fBwUEBfDLWtm0xHo/RNA2ePn2K4XCIzc3NYqqhL/5sNsN0OsVoNMLKygrOzs7Qtm1h9DQN0a59dHSEtm2xsrISeuCsrKwUm74SjC4Fm40nio45Zfqsq/r687/Hv7y8LGDP/3wCVw9U83iRyXSRPvbrtf+1ubio3Bmwd/ElCyViZPwdgXkWzuP4CqCvUsjyjeJFgO9xI+USlVuvRUs+lYixO4ipwszYvfaBt5eDbbRqYFwH9Vo/ZQoiMulF5eN/DXcbVtY1QWvX+yiH2WxWWDKfZH3w4AGGwyEODw/L8QePHj3CwcEBdnd38cEHHxRvGD4IdXFxgZOTEzx//hyz2Qzj8biYfi4uLrC5uYnLy0scHh6iaZoC8ltbWxiPx3MsnWU+PT3F2dlZAW762NPHf21tDcC12+bV1RU2NzdLndq2xdraGk5PT9PVX01RR+Ob4qxbr0feN/rbN1sJ9tnGclS2SKJ5mAFzF+D7vdrKs4/cKbB3kAVyc4XHiUA8Ytb8HXnR1DxrMnafAXtthdAVhnWI0o/aQgeiDjQ1kbj5JAJZBf0u+71+O1PLvHIiReBKJ6ubS80808UUo7SdSdXEl/dd96Kwfu3q6gonJyc4PDzE0tJSAcz9/f2yifg7v/M7+Pjjj3F5eYlnz55hb28PH3zwAR48eFCOTCCzPj8/L0piNBqhba/97ZeWlgr4XlxcYDAYYHV1tXy2traKOyNdGWmWAa73AabTaZkr9NpRkOQmsZ9RkylCbxcfJ9E4j8aSAneXQte50TQ3R3lHm6+LSgT0NaCutYkqj2xe9ZU7BfY1IFcFAPR/0Cq6l+UdxXXFUwP6CJg9TrQi8DiZ+YafjAHU2jVrL03Dy6yDK9sw83h6LWL30YDNzCxR3SKl5SuNvhIply4zTQbcDvIOOApEbnoj0B8dHWEymWB3dxdt2xbQvbq6wuPHj/Ho0aMS9rPPPsPe3l6xxx8eHuLw8LCYJRj38PCwrBS4mUpzC0mAetisra2Vh6oI9lQMfJDr9PQUTXPj184jGGgWoT2c7Ri9h6BmolFR8qHA13dVlYmvWiNTT1S2aFxH17J6eRmzVQvD6pxXEncbuVNgT8kayRlsBHoO7m4ycUadbYxGPvZRupGScdNLpGxqQJ8pg1r76D0H5GiidYF9lh5NNrwWAX+UR9RPGi/KN0qbv531ZW2j6WdmoL7Sh6HfJv75+Tkmkwn29/fLNT6FurGxgclkgslkUuzlfFSfLPx73/segBs3yNFoVICLAE+lwZMxudE7Go3KeL26usLx8XE5I55zgGfmHB0dFebOjVja/FdXVzEajco8OT4+xsHBQQlHpaK2e5YP6GcWy8Z9BLQ+Pmp9r0AflSEjI1q+LpKh8yarX0Se+tS/r9wZsO8y4WQMVwdOFo7XM/t3BNgZiPcJG+VRi9vF9iPQjIRtEtnQKW6/jpiFphNtTno6/B+VUa9FeTGPiC3VFEOUfpZfFrcWT6+/ipLoCn91dVU8QFje6XRaPHKUUSsobW1tYWdnB/v7+3j27Bnu37+Pt99+G6enpzg+PsbW1lbZBwBuHr4iC2+aa2a+tbVVlMTa2lp5AparBpp3COQsD1cI2i5q+tFVRNM0WF1dxfLycrnmZ+Jn/eHjKgLXmu07uhaN04yZZ2lHcyNbuUb59imzz/sakekjdwbsvSJuu9Zwbm7xOBo2Cs/fkZulgm+keDIlEoFOdK9LGUXK4lXaMQLyPgM7SyNSItHki5i3t6VfjwA/W6UsOtA9jy7gzu5nE/Y20rbXxyDw4SieMMlN0vF4jOl0Wq7xyITZbIbNzc1ipqE3DYBizz85OSkbqqPRqJyJw7Dr6+vY3t7G8vIy1tfXsba2Vpg/T9LkKmB/fx+j0ai4c/K9DfSpPzg4AHDtzrm1tVWe5OUKgR5n/J3Zx2ttGwFpZjKMxlM07mumlz5KKFtRAC9vGOvvyBSl6UYKTst0W1POnQF7SsaMI6ZHUZOBrhCiuDVG7uacSFlEZcq0eA3Ya/G77mXSBdYeP2MO3l6RuaRWpixuF9hHbCcC+j4StV20bxHd8zL0ddtU6YrDdOklc3JyUvy+uS+xv79fHlTifZpRVlZWMJlM0DQNxuNxAX4y+4ODA2xvbxeXyOFwiNXVVWxvb5ejizc3N19629PS0hLG4zHW1tYK6E+nU+zt7aFpGty/fx+DwaCUgUqGZ+kD1yuEtm1Lvk3TvPQiFZWMEddMkH3MLRmx4b0+gJmNuSh+jYhk4TPgjkxNkfl6EbkTYK+gCuRPw3oclT52fP/tYbJrHtdXEG4aiYC6D9jXFEcfyYCxi3m7TVzLUWO3fVh+NJFrygGYV961Nsjq4OXITFrZhK8BQTbhsskYsTr+pj83v0ejUXnaczgc4vT0tBxjcHh4WIB+eXkZh4eHhVHrW6O4yQuggPhgcPMmNbYbvWh4TV/lx3IuLy9ja2uruH+SkY9GIzRNU2zx77zzDo6OjvDZZ5+VN1txJcEzZtq2nTvHRtta+9Kv1cDNgTwaA1k+tXu1sZZJbcxE6WVmHP0djSUfQ4vInQB7FQcEB9YsLH/r2448rpuGMkYdXcsAOmKLEchnZfoixAde1/+utLJJkK0WsnQYp6Yc/HcG1LWyZWDrsqgZpyt8LW//TVZ/dnaG4+NjtO31g0nT6RRnZ2fY2dkptvS2bcvm6Ww2w71793BwcID9/f1yoiXZPU1ANKfs7u5iZ2cHa2trZSN4Nrt+upV5Efxp5qHJaHV1FWtra9jZ2cHOzg729vbK/KLXDtk63S2fPn06Z9emeYiumfrGLN/sz8x4tfb2cRGNh5q5po9EyiAq8yLzSiUiW9H9V2H1wB0E+0zIRiOJwFRtgpld0ONGzDuKpxq2a9D0Ze+/V5LV0xlQNNAjAI8Gah/vHIb16zVW1EdJRNLF0voumX0S1lYDLn5wGDc16VpJRq4PNxH0CejLy8vFnn9+fl48YWg7397eLiA/nU4LMI/HY5ycnJSXmKgdfzQaFU+djY2NsuG6urqKtm3LgWrcC9je3i6HjQHAdDrF6elpMfVQYS0tLRXl4HWK+isDvkgh9JVoFZF57WR56bWaIlpkJdBFLDIm/2PN7DP2piARebJ4XP3NCZR5+3SxcQqVR5cNOor7KvIq6URsM/rvcboGUR/Gtej/jOHXlt2uiKIysi80jZoNt2tiZUvpvqyLZdGnZZummfN7X1lZwcHBwZzLIsfu5eUl9vb2MBwOy8Yr688jDHhqJYGVxxYfHR0Ve/pkMiksmyaek5MTnJ6eFpbP99Ly/sbGxtyhYjwzh+WjgiHgj0aj4t/Pdl9fXy/n3Wu7U3yDMxsvNTa9iO2+1k+aVm3c+vVspeKyCDmoKYa+cqfAnpKxb/2dgWDGoDkZCdjAzUFKDiYalpOztqoA5h+E6luv3wuJBkpf8K6xqsxemk0av6+TRE9V5L2MCGRpaZ4K+q+yDK5J1+SntO21rZ6nRKrfOc0xTdPMHZswGNw890FvGb6whMDMuvIkx4uLC2xvb+P9998v4EqzTNu2OD09LSsKAi/7lu1Pj5vz8/Pi/TMYXL/WkO+h1QPDeFYPcL1hy5UBN3NpMtrY2CgHlfVduekKwIG0tprU9LLxm7H7iO1HfbuoIllEPs/xeifBXqWLJfcFWGV1nFwK/nrdw2R5+cogWoVE0sUGo0G+CFAtyjadpeqkqi2zPQ1fmvq9Lvc5reciiqU2+aN8al4dWTtrW2h6NfE4wM1xA3SppB88Dzfju1/pNkl3TPrAz2bXZ9PQNXJ1dbW4OPJws3feeQc7Ozu4f/8+AOD4+BhXV1dYX18vtnU30TVNU0xENBfpS3YI5m17/cDX8fExDg8PMR6PC7une+b9+/fLJjEVEv3zeaQD3xXbd9UZzfNsgz4DaV9BLOqWmcXVa31WxSxL11j7vOXOgj07QP/XwvYVZ+Da+FnDR0DelX9WpshM4CDi/7Xz+w6SyOygcXwg91EQNcDuuzrwcur9qI2jNs/+Zyaf2hJ4kXIvKhGQ8bAtHg+s7pZqt6fPO89Wb5qbF4nr6YxN0xTzzVtvvYUPP/wQH330UdkY5QpidXW1sG6COc1GfOmJngF/dHQ0d5bOYHD9JC3LRNv9+fl5MR0B18cof+1rX8OTJ0/K4WfLy8vY3t4uG7S8tggYR+0Z9ZUr19rmbB8TYRfwRmO5hiU1s082brvq3FfuFNgTTLqAPQIDjevLef9du9aVH1D3EMqkD8vWTo9YtpY5GgwR0C9aBs8zYtRR+KwuUfm8HtE9ZffRKqFWpho4dK0UNcyrsitPg2DPscp7BFMCvJ6Jc3x8XDY56bNOZs4HozY3N/HGG29gd3cX77zzDt56663CwI+Ojgr7py0fuHHXHAwGxbZOezyV0GQyQdu25elZsv6mufbGmUwmePbsWVktkOU/ePAAo9GoHLnMt2LxVM2zs7O5for6xVd3WZ91eYp5X0ThHfyz/1HYrN+jOmRSWzV+niy/E+ybpnkfwM8DeAvADMC32rb9W03T3Afw9wF8GcD3AfyZtm2fN9cl/lsA/hSAYwB/uW3bby9SqAgogJfPrY9AN1IGfWXRsDXgiDqIZiP1GFJRj5Uu847+z/LS3/6/Vk6mnZlmFhnAfs0nUI3l+zX/HZmMusqupgu153sYV7p9Vj5d4puK3PikGYYPV7VtW8AZwNwmqR6r8PDhQ3zpS1/CO++8g/F4jNXVVTx48ADj8RgXFxfY39/HyclJOfKAnjX6Im7619OTZza7PuZgbW0NW1tbZWXw5MmTsilMb5+lpSV8+umnmEwmZdXB1yO+/fbbGA6HODk5Kaab0WhUzubnGIj6QeVVAD+7r//7jmXdOM7mwW1WI7UVQKYEbqsA+lDUSwD/Ydu2XwfwRwD81aZpvgHgZwH8Utu2XwXwSy/+A8CfBPDVF59vAvg7vQoyiM9Pzyq1SGWd8UeSrRYyyVhzBgYOHPrJNogjkK6x+dpHw/jGVVd5vX2A261uonS0rTMGFYXL4nn8Pn3ZVcZXDcvJ6a/MU3szQZZAT/s9TSzc6KS5Z3t7Gx9++CG++tWv4uHDh+U8evrKn52d4dmzZ8WNkscccJ7pSzpoc+dG8NraWgHtd955Bw8fPiybtTTtjEYjPHjwADs7O3MvCtnb28OTJ0+Kpw83h8/Pz8uKgA97EUTdw84/TvKyMFE63g8ZYfK8+vyPyhT1fVZ+z7/POHoV6WT2bds+AvDoxe+jpmm+C+BdAD8N4I+9CPZzAP4pgL/+4vrPt9dI8S+bptlpmubtF+ksLFpBZcVdDHBRqSmPPuDdN48I6MkIuBGmEmnxRZh8VxlvwxD6tvNtGYjHzx5ciVZBiz7kEjH4RfqzTx3UAUDfc8oNVgAFSAladMFUJk52fHFxgQ8++ABf+cpXystIeMDZ1tZWeeqWXjPMl3W7vLzE8fExzs/Py9Ow0+m0HKrGYxrefPNNfP3rX8dHH32E6XSKp0+fFsDmSuPtt9/GbDYrrpxXV1f47LPP8C/+xb/Ab/zGb2Bvbw8fffQR1tfXy9HJOzs75VmByJVZ2zdj0dEYjFZ6EZt3Rt93ZRDNTf2djdVoBRGtIj3ObVYFNVnIZt80zZcB/CSAXwbwkADetu2jpmnefBHsXQA/lGgfv7g2B/ZN03wT18y/nK/Rpe2cTd5GKy7C8m6zZNc46tETAXwN/D29RVYafcArWyX0BUcgfnGEltGX5lE8z9eXrl1ub57n5yF92iZa9dQUKpkvAZGHnzXNzbkx3KhlGII9VwJ88Ilmko2NDcxmMxweHmI6nZbzbwaDAZ49ezb3/lmuJFgGHqtwenpaDjN7+vRp2YA9OzvDD394PY0/+OADfPTRR3j48CEODw9xcXFR8tnc3MR7771XNmQvLy/x9OlTnJ6elhet7O/vl4e+6HVE+z1B1N1tozb09q5d1/HjpKqmDPS392sEsF3kwsdzdK0G9J7mq4z73mDfNM0YwH8H4N9v2/awklF046VZ0LbttwB8CwDG4/HnswPRU14VHMjCaxKZV/y639cBGdkIFfR9oPRh9mrCicqa/a/V7/OS2gDvG/9VyuV51tpLxfs3u08A5YNLCnAEZZ4TT5MHNz7VTLG6uoqNjQ1cXl7i5OQEk8kEjx8/Lq8H3NjYwNraWrnHY4z5ykDmR4+bvb29YnK5urrCRx99hA8//BAHBwf4pV/6Jfyrf/Wv8KUvfQn379/HxsYGgGtXTpZrOBxie3sbAPD06dPisknlwOcGNjc3ixmKJ3Kurq6WTWF9yXq2iovuO/BH84ZhgRzUHUD72PkzNu/lpLyKP/6rrpKBnmDfNM0Q10D/37Rt+9+/uPwpzTNN07wN4MmL6x8DeF+ivwfgk0UKpVrvtsBcsym7ptXr+p3dj/5njNzT9Y+ClF6L0quVOcrHy6IsPFNCWV2BlwecL0v7roQiVzhnNFE9NPyrKC3Np6vPa1KLoxObwHxwcFDOpgFQ2DzNG3xIiS8TpwlmNpsV1k73yb29veJvv7e3VxgzFQmPQ6atfnV1tbhKEmC5UlD/e9r9P/vsMywvL2N/fx+/+qu/io2NDbz//vtzrynkIWc8MI1lpx/+V7/6VTx48KCczsk6TKfT4iVExaFOCzV3SW9bB3SuFCJHABcH7syc06UMulZ50Wo4SjNLp08efaSPN04D4O8C+G7btv+Z3PrHAP4SgL/54vsfyfW/1jTNLwD4wwAO2lva6yPJlm0uDpa3yafGoB2cHZj1d/SJPG+6AN/LpnlF6fQtTxQua5OI8fYF+T4SMZjaMwGePyd71ydKLytHFL6rviwH3SnpBnlyclL6nUcS0O2R7FbfksY0RqMR3nzzTTRNg4ODg8LQaffmxiwVCHDzQBNNLrTd84lZPgDFQ9ToDz8cDvHhhx9ieXkZ3/3ud9G2LX7jN36jvOh8OBxiaWmpvLR8a2sLa2trxeXy4OAATdPgrbfeKsqGD4+tr69jZ2cHn3322dzqJiMz7As1ewIv2+B5T8PVTCyZ8nDwZ19GyiCK52WOCJGXweseKYpXXbkC/Zj9vwngLwL4taZp/s8X1/4jXIP8P2ia5mcA/A6AP/3i3i/i2u3ye7h2vfwrixTI/ey9IRyAswHiIFljedn124BYBFSZfV5Bs2uwMA1XEFG+DlIZo8+Ug9Zf0+zbFou0WZ90dYMsK3umuKI6st1refe9XkuDTJqnTaqf+mg0wubmZgFC2vDX19cBYM62v7a2hvv37xfmvrS0hI2NjfLmp7ZtyxOp+lrBweDmeAUqgtPTU0wmk7I5TLML06dZZmtrCx999BHefffdsor4/ve/j0ePHmFzc7O8xJymp3v37mE8HhdTz/n5OR49elRcQvW0y52dHdy7dw9HR0fliWE1YWqfd4E021nnReYOqY4QtffiLmLeye7V8KbmotkF6q9izunjjfPPEdvhAeBPBOFbAH910YLUQPfzSkslY8wRm8uuKWBHyoUTOwMeHRgORM5cdCBGdY1AKGLfEVh6eT2dPpKBYASGfWyXUZtnCkiVWtaHGsc3lT29qE590vXwNMHwXHoCjR9WRrY/Go2KOYeeWcPhEPfv3y8+9zx9EkBJl66TPBeHXjgUnkhJsw9dIZnP5uYmrq6usLGxgaWlpXLk8vLyMh4+fAgAeP78OVZXV8vRyhsbG+UgNo5Xrkj4ApSrqyvs7++X833W19eLSWpnZwdPnjwp5iYCvtrBm6ZJN20diHU+RSzZAbm2Cavh9HcEtDUHAo+r+XR552j5XwXgVe7EE7SLAHpX2EjjRpLZ2Nv25lyQCKgygIg61MM48+BgpkR2Rv846Dt7j+oZ1VHj9lFsi8htl5zKuFUicM4USXQ/Y/41BdiVbhZO60Bmf3x8XNg1QXIwGBQvG/rU8+wZfTk3N115n6B4dnaGyWRSPGJGo1Fh1fR64ZO4tNOfn5+XJ1hPT0/RNNfvoSXDXltbw/b29tx5ONwUBlA2hnkOPvcV+AAXz7Xn8Q1Nc+3S2bZtYf0cx5ubm9jc3CwrEZp5lKG76USvR/0TsW41h2WbqPwfKZcItBcx57hkqwm9n9Wxq/5dcifAPmOVLn3MN1kc/a+d+yrlzUDEw7gpR9m8D8RoEEZ1uo2XSB9A6wJ5LWstnVq6fcuj/zNbvIftA+rZdQ8T7RVEylDFbfXHx8c4OTkpT5/ytMrT01Ocnp6WTdKmaQob57k2Z2dn5agCHbdt25ZTMWknZ9qqaPTFIZeXl5hMJjg5OSngD1yfTsljFyhUOnzf7cbGBra3t8vxCbzHJ3MHg0E5K2djY6OYm7hHQADlwWyz2WxuY5rn9+uLh9jGTdMUxRNtnno/9Fk1KkizPLxGE0+WVgTwtXuL2O61zjWMY9xF8etOgD2lr7byxmHDvIqd2cvgYN2VVg1g3JTjgB8NmojV+BLV2yMDQC9Tdq+rPaJ7WboRcEft2fXxsJGpppYX4ygI9gX9PqLpcKORx/fyqIC2bQv7JqPnSZYEy6a5cbVcWlrCdDpF09wcfkbbPJk6bd98GnU8HmM0Gs154wyHQ5ydnZX89IlZ2usJyOo5RBfJ4XBY4nGMEQjpkcPfx8fHZe+B5hwlVIPBAMfHxxiNRgBQ6suz8CPG7JJ54dTC6VyLADpyk9Q9Is2rxsT1fxQ2i3sbRXBbuVNgv6jdnh2YmW5qANk3j76SAUjXfbJ+NelEG08Z4Lsi0fy8fFFZamFUuJrIWP2iS8sMoPV3bVNWw/VRIEC+UR0pgihsrS6aB48K5lOqfOsTTSt8QIrAd3FxgfX19QKSwLWdnWnSL51Pz9L+rawYwNwTt/qCcgI+gMKS6TbJfYTZbFbcIFdWVor9n26U9ODhZis9h2jOoZePAri+A3c2m+GHP/whLi4u8PDhw/IyFJZZx3VGaBS4a0pBx2TEkiMAdYBnuC43UA3H/vcwNWVTS9fr86pyZ8D+NmCrg6MG+rdJv08DR2Ci5dA01CyTgZcP5NpHw3eVP2LBkYLwetXaowaCNdB1ZdFVrhow+28Hbb2mfVRTEFS4mn9NkVCYF80v3AglqJGRc7OUNnged0zAo4mHG5kkAfytryNUX3q+UpAsnm+ZWllZKYyeZhKGp+/9yspKeaqWDz2RufMhMHr10JWTdaCiaNu2mIa4D0EApHmK78xdXl7Gm2++ibW1tXLiprN7Sma6zVYBDt7O8l18rkbMvOaamW3yOrh7ujp3+7B7VXSa3iJyZ8D+NhKxAf2uNYgCJwelm0NqgElGVANuDa9mmywOED8fwIGjAznKy8vpsgjYR986aWqg7e1WUxSZQogAti/QZ/c8na6yROXN4rB9aBunDZt2c32YiWy3bdu5F4QQfHkcAU06fPCJRwNzU1Rt3zs7OxiPx8Wzxc0s5+fnxT+eD1np0cp0/WRZeHImP2rnZxm4OuCTwWoSovvnxsZGWbUA12agZ8+eYTwelxUPVwNURn1cGIHFT7vMTEWqSGokMQLsiK1nSsWvK0ZEK4MI8F9F7hzYa4PyfwTk0cal3quBfbZMjOJkE7x2LwL4iO0rSERLQa2HDtCuZWUErv4/U2QuCuxe377tE6WZpaVgrNczU0ukJDKWn8WLlEmtblF5CXTHx8eYTqdlhcCXbxPsfBU6GAyKVwvPwQFQ3lTFEyY5DkajUTG3cPN2Z2cHW1tbRSm0bVuAncyebpa6P0C7vZZxMplgdXUVo9Fo7oGw6XRaXDJZj7a9ecUiX17OcUpTFf3v1UWTewn09OG+hY7/yJau84jSBd5aVl89uImnj5dNH9B1EK/tE0RETcdJNJduC/x3DuyBl1k6MA/+WZwu7Rx54DjAR6DfBVoOGNpJGs5XA1F6Wg5n+Rw8GXOoSQTu+jsqUw3k/X/EiD1cBOJZ/D7g6r/1v74bNQoXAXwG6lF52vbG5OPmm5OTkzkbOVkujxHmcQjql662evrM69O2fK0g+5/snGfLr62tzQEq/fLpt889BJaJY4lzgu3DctC189mzZ3OvQWyappy/0zRN8fPnSoAvZ2GZeT4PX6BC8Ds8PCymK5q06Cqq7a2eOJlEDJ59F93LxJl6DU8yc5PHV8CPCJ+Gy/KPlNxt5E6CfV/JzDA1E00tnUWlBlw6cdzmloGiM3xeAzA3KbU+mm5fje9sIZtMWVlrQJwpg2ipW2PXDsRdNnkHc61XFN59la4AACAASURBVDbKg+X09ojqqMqEDFiBXicvgDnfc4IbzSYEXR5UxqdotXx8upWMeTa7flMV/fb5gBNNLSsrK8UzZzwe4/nz56UealIZDodFiYzHY2xvb2Ntba2cuUNbPTdo+Z5Z7i3oaZZPnz4t9noqOx6ZzGMYqISo8HjMA5US54D3ZbZRyvBULl1zOWPx3teRaBmi+ZKlnYF7FDeqa1TG27D7Owv2kekmM+nod5ZWFD9bAahkjDgLo/8VHNg5yvyzgaYDmOVy0Hfl0afzs3JmNscI0DVMjYVH8bM0avEixh3F7YqTAX5kuvEVTVYfhqUp4/j4uJxgyf4gwPFQs6Zpih970zSF9QMopg09QoH9zTh6qBnt7nwhCBUIw9F7h37yZ2dnc68EZFi+MpDsm948bBt9aQnzoeLhymU4HOLw8BA/+MEPMBgMyqbvbDYrr01kOwyHQ3zta18r+we8Hu1VRX2RMd7MpKNxPF4XIGceNDXPGjcVMTxwQyZcsrnrpqaoDn3lzoB9BNo1MM6A3+31arqpgb2vBhRIgXzTJRMHGs0vA+cuwHbQ1w6vKaIoTMbko7JH92vhPY6zZp2AiyqpGtvXiZSBfBTXQSWqh97TeDwumCwXQPFTJ+gp8OlJlr6C46bqbDYrZ8wT5AngdHekEuFHTUJkuPTOoc1+Mpng6OiorEpYPj6QBaBsttK3nh4/l5eXGAwGZcN2PB6Xs/X5zMCHH36Ivb09/OhHP5rzanr+/Dmm0yl2d3extraGzc1N7OzslI1b1oH5apu4iUPHbtfYp/TxcY9MKZFpxmWRdDSOp+t4k6V5G5Cn3BmwB/JjiR2wHdCj31wiM7xvimVpAfFRCtHvLolA0DvVJWIl0f2MbdTuZQPWATL63XXNAdLNINmnrzh4Z2krkNc+EXD3qQvz1LQIprRX08uEvuoEcQI2zTW83zRNSWM2uz5i4dmzZzg+PsaDBw8AoHjocGzSbMMNTrJ62sXbti1n3/Osez7wROBmOJadZdVXChLcqag4Xwj2g8EAk8kEW1tb+Mmf/EmMRiP88i//Mn70ox8V089gcPMg2XA4xLvvvltevEITkPr2syzAy6RMrztpqhHGDDyj/55eFylzRZStNqhgM0Wi5EfDRmWMytUldwrsI6mZYBysFdT1W69n8TRdDwvkyy/Gy65n4LGodnYlEbGFDMyZJyVjrIsAfQT2mo8rSAdkB1ovYx/xMiwK8lE5IiUQ5csPTR7Hx8e4urrCzs4Otre3sbGxUYCUCoEPGSnYst58CnUwGOD09BSHh4cAUDxVuKGrtnP6uauPvvq9t21bmDO9arhRS1/7tm0Lo+bRClQcakOn+Wh3d3eOlQMoD1Tt7u7ij/7RP4o/8Af+AH79138d//pf/2v84Ac/KC8cX1lZwVtvvYUPP/wQa2trpZ1pCmJdsvNpKJE3TcSINa6DM69FXjZ9ADUqX0bOugDeV3gK+np8g1sGFpU7D/YqNVYffRz4u1YC2hlRWotKpomjjurS0lFafZd3NWbSB8Cj/x4nMoNk/zM27WWt9THrqxNAQZxPj/Yx+9SYvip5Z3Q04fD9q1tbW8UNUg8to32dtnj3o6fpZDqdYnt7uzyQRUBXxsszaQjIZOJcNWjdydCpLPSF54zDDVOaa6jAeEwDgGKTJ7gvLy+Xl4/QnET7+87ODnZ3d/HVr34V3//+9/FP/sk/wXe+852yh/DBBx9gd3e3KCqWj/+1fQmK0ZjI3Cf1HseFzmFXBIsogS7py/C9jJmy8tWAl3FRubNg78u3GqunKIt3Rq8bXZESiK5lHy+nfrs4mLED+bi5e+xoPNfkPjAVIGsDNyqTf/dl9vyvp3Vm4J5dc9s68PJBZ15PHwu19lZgp306AvcM2CPwpyhY8A1UBOCtra3izaKTl4x+NpsVZk4b+2w2KxunZLR0kSSgMzzBW8+u8fZwJcd+Go/H2N3dLWfg6wvO9UldNU3x4azNzc3i9bO1tYU333yzeOE0TYM333yzvFeWq4F79+5ha2sLx8fHePr0KS4vL/Hee+/hrbfemjt+mSsRrkqcQGRjPmLMkUTs38dRl/dLH/FxGgG0lzc74C1SGDUzbF/5sQD7rPG1s9wm7x/3afZ4EehnJiAvV3TdyxwxVx9g7ORoBdClaKKB7P8dSG8D9ix3FzOPAJ0D2xVFrQyRAnDJyhqx+y4G3wX4Wr/j42NMJhMAKC8j4YNObduWt0axzgR1HjfAF4owH7oj0n1RPVSGw2FRKqPRaO4JWGXEugLgb26gbm1tFU+hw8PD8jAUj1jmkQrcPxgMBiUez7FZXV0tZ/XwaAW6anKlwZecb25u4hvf+Aa+853v4OLiAt/4xjfm3EdZNz3jfzqdzm3SRmM5G+vZqiCar1Haft/B2RVDtmHsZeA9XZVmwJ2RtL7KrSZ3Buy9o/y3s25e047hstgZu/oyK8P3676hG+0HuGTAzk7RDlLtH90jCLIcWR4RkHetOGqAru2fAb4DXg0IF2HwEZhr2IjxR+zf20XLUAN59RpxoNd7/uHTsjybZmdnB5ubm4Vt0xRCs83V1VXxhOE4ZR7qucP0ab4h62Y9+DQqbfG6UmV6VDSsh24cz2bX/vnT6RTAjb2dNnmWt2mal1wxT09Piwnq3r175Ujlra2t4roJYO78nM3NTXzta1/D2toa3nvvvXJwmh7axlWuuo/WJCI3fcNm5p8ac/a5GikBHXc6pz1/xwLuWXjcWlk9z75yZ8C+xuIjgFcl4AAfgbyG8acXdZMrSicyB6k4EKjmd2aRffS+DgiKK7qozWoDwEGfv70O+l/tnsDLQO2bmK4cPH4G8hGYZ+lkisclqkstv6iMmRCggGtTy+bmJsbj8UtmCLJ52vWPj4/nvGdolqHvOU/FBFCAXMGCdm/e40eJCgFfy8jfuoLgeOAqQM/fUbMKlcqDBw+KyYcvIqHpiisU5jUYDIoyAYB79+7hjTfeKC9XAebJm76DNiI6fYG9Szyt24BnpBw8rptnNF8neEB8vHK2utAN20XlzoB9H8lAk9cclB3wM0bvoB4pjy4GHy3NfAlHyVYrNYWWMQkNk7WX/o6ALCq7ho2UWca4mU6kCPoAbRS/tipQ+3yNwUdliNomajv+17jcGFU3Syr3tr05Gph2fR43QNMLwZ5mFfrEHx0dzb1ikEDNg9J4tLGPU4bVDdqojXiw2htvvFFeM8hVCA8t02OMuc/wox/9qJzgyRM36fo5m80Km9/a2kLbtkXBPXnyBM+ePcP29nbx4KGSm81mc/OOikvNO+qZ83mAvs6ryIzqTLrG6JWRUxy4ec3T1PprGMar2etva7//sQJ7lwz8I5u9sgZdDUTmG09DxZkb/2fMO2LfUXn7sP8oLZ0MfRh+JhGzB+Zt75FdWwddBrQOzpq+K5pMqfg9fnQDlq6D+hKRGqNX8f5gn0Ybdzw8jLZwVbhN05Tz32ki4YtHyOhpxydrZpoU2v3Vhk4wpS1fjwUG5o/TUNDXdgAwd4rmeDzGvXv3yisFB4PrIxM2NzcLO3/69GkxP41Go3KGjSokrlaoiPiawl//9V/Hr/zKr+Dk5AQ/9VM/hZ/4iZ8oXkG6d8Hy6wpHXUgVLGviSkH7bxFFkZEzgrWPWc03UiSZrZ3XdYWTAX6tvn3lzoB9xKr6VCYDSgffCPwzpRCxZQcqpq1LMjfx9AXwCNC9Xiwf89Z0s/wWbfsa2DuQa1vUGLUDs+cX5e3KJFIWUTgFewe8DOR9Oa4TV9mcCvPTF4czfb6KkJuc3ChtmmbOCwVAebpW7baz2ayYO/RFJ03TvOQCyTIDN949rBNwY77Rh5RUWdDPfjQa4fHjx5jNro82WFtbK/Z5ev2cnp7ik08+wbNnz7C7u4v79+8X7x6uBHiOzieffIJvf/vb+O53v4uPP/4YR0dHAID79+/jy1/+clF2fGkLy8pXLfrY97pG/RhdW4T9OpmKzDTO0Gugz/8Rm/d5ynR8JaNl6GL7feTOgH0GTrXrGaOtMd2MfTvYUpTFe6d3peHgryuLrjg+KKIJEIF7TVFmbZmBfQTwNcbuv5lOFi9jXXqfS35XDM7+CWrObJl3ZOeMNty1rZwh6qQl8Oqm4mw2Ky6UBHuNw/R0w5QTnGXkQWj6OkHeJ+PVtuADWmwHbgZT9IUiVC4sN8fN2toahsMhjo6Oim8/T7vkC0b44nA+A0AvJB4Ct7KyUoD+u9/9Lp48eYJ79+5hY2MDH3/8Mfb29vBbv/Vb2N3dxXg8xmAwKN5Kmg5fzl4brzVZlP27eF87CfWVhoN+xsz1W4mUh4/cMTPAX1TuDNj3Fa9oxoSBedDQzuN/LqGyJb52SldZ+jB235jR1QTTyGz40cZwplQi8K9JBLZqoslAvQvsI4DOwnmdCH6uPDwtfhzoo9WBt12NGWWrAJaL9naaZZqmKWYbfSMV60MQHo1GpZxN0xSA5/k6a2trWF9fx/Hx8ZyiotLTY5UJ5PSYadt27niDSKFyk5W++m3blhMsDw8PMZvN8Pz5c3z729/G4eEhdnZ2SpsOh8Oy0TwYDPDJJ5/g+fPnxeTEI54B4A/+wT+It956C5eXl/joo4/w6NGjcmyzblDzLH3tZ74nV9u9T185wfD+7qsIIlBVnGD7+/0I8Cm+eqTy1rL65i1XORpfpUZWIukE+6Zp1gD8MwCrL8L/w7Zt/0bTNB8C+AUA9wF8G8BfbNv2vGmaVQA/D+APAdgD8Gfbtv1+n8IsorEWBTQgPqYgMi0wTbcl+3cEyhGQZ5u9vicQhdO0vP6aZ9YeXQMiGkRdLFz/R6DqIJ4xcm/7rBxdNv2obNqPHlfbzdsiUkh+nx4rAMqGP+3QZOLqQqgPUq2urhaGvbKyUoCcZhPa6wnks9msKAE1S9Fuzo+uhAiYasKh3b9pbvar+J/ulPSbPz09xf7+Pn7t134N6+vraJqmmF7ofz+ZTPDpp58W75ytrS288cYbxTvn3r17xS//a1/7Wnn5CZWdn8/DlZKbXxWknRhkY9avd40pHSec1xHgu8KomWT0P1D3yY9A3sug9n1dWSwifZj9GYA/3rbtpGmaIYB/3jTN/wTgPwDwn7dt+wtN0/yXAH4GwN958f28bduvNE3z5wD8pwD+7CKFijo367TsXnQdyMGNzExfj+ZLPgcLfmcfZdyRCSdi9hHQZ2DeVQbgRpllCrEGyGyviKErKCroRsrB+0LZaTTxtGxRH7oi0vS933SiZP3YVT9tP9azaZrCUAm+6lUC3Lw+EEABMrpP8gXkFxcXc68wHI/HAG5eYsIz6flOWQL4dDotdncye5qOFPhpS7+4uCibwQooVFQbGxvY2dkp3kA8kvjRo0c4OTkBgPLWLNbjwYMHePfdd/HBBx+UTd2macrzBjTvjEYj7O7u4vDwEKenpyV/HTu6P0AlGClklWiMRcSkD6OPyEHE1vmbdY3Grz7kpvEjNu+rALXdRxu3WvdFyDHQA+zb61JPXvwdvvi0AP44gL/w4vrPAfiPcQ32P/3iNwD8QwB/u2mapq20OBvP8n3pdzYpa+H0v5ptFGgUIGlPZbkc8LXMGSBHgE8mw+vu169pZum4dCmK7DtqL2/TPhNIJ6tP3qjNsvhdjE3bPLru95wF+XI6qp+m6Q9T+YSkB5eef6N2cwDl4aqlpevXBpLdE1SpEBiHnjA8DZLxJ5NJUQr005/NZuVsmra9eXvZ2dlZMbFcXFzg5OSkHOfgpj4qE7px0nz0ySefYDC4PlXzzTffxLNnzzCdTnF5eVm8gTY2NnD//n18/etfx1e+8pWiiNh2yj45NtTrSBks73O1xA1oJSlR/3nfZ2Swhhc+biKJGLmblSI7O/PKFEbXdd2sjcYx228R6WWzb5pmCcD/DuArAP4LAL8FYL9t28sXQT4G8O6L3+8C+OGLCl82TXMA4AGAp30LpQ2mS5bIxq6gHd1TDczBpzayLH/9OGuMwLyLkbsHkCoZ9f5xTyCNwzaJ0tc4URr6O1KEzkSySZIBfcTuo3aNgLUP0Gsa2f2sLSIgyOpWE7a5PxSlzJqARQ8WumbSTfPBgwfY3Nycc4cErs+u4ebo+fl5URBHR0do27acVjkejwuo8wUjVDp8UxbdI7nZSTMN9wb4Riu+UpDzArju/729PRweHmJzcxMPHz7EYHB9hv7GxgbefvttfOUrX8EHH3yAL33pS9jZ2SkripOTE7RtW87QV08itosDHNufabCdec/Hvc8xJ2tODjLF4ICt8bPxomHdnKLxnXU704/YfA3wFcMoDv59pBfYt217BeAnmqbZAfA/APh6FOzFd6QqX5pFTdN8E8A3Acz52wIoD5FE7Ms7t2mal87SeJF+iUOf3UyL+2TP2DXvKdCqKabGyBnOw0cbtBQFbS97lqeHjwBQxZlStmKKGLmDvIO95hfVoYvZ+/LY7bmahubjeWtbep28vBEb037xjdWmaeZYmJadL+XQIwY2Nzfn+ktNLNoGW1tb5U1Vs9nNYWkE9NPT0/KO2uPjYwyHw3J8A4C5jVqW259DoKmnaW5WtLu7u3j+/Dk+/fTTYiq6f/8+BoMBNjY28P777+PrX/863nnnnTkXzbOzM+zv78+dBaSEgOWZTCZo22t3TwVJLZva9P3BJV05OFhqX7FfFwF83tMwPjY0PxIJHXP6XWP6Ebhr/hHD1zJGJKZLFvLGadt2v2mafwrgjwDYaZpm+QW7fw/AJy+CfQzgfQAfN02zDGAbwLMgrW8B+BYAbG5utr7sVsZXY37cDANe3mBjOr6E9Q5QcZB0cHeQzzZhAczdY726lEMEin7N/2csPwN9b0Nv5+ibbc3/NbCPGJOXU9ONluZR3+sE9z71eN5+nr+vGv2jk9snNoA5jxFlpLq05tk4s9kM29vb2N7engt/dHSEJ0+elAPOuInKPOmpcnp6itnsenP06OioMHYyaT4BS7bPVQYf5tLy8x7bkWnww9MtV1ZW8OTJk7JnsLKygvF4jAcPHpRVCz2I+EJyHqrGVQPToQcOVySsP99Vy3bz8cS+Yjuz7UkGFSAjz5Xa+OgD+BqOcSMwz5RKtHrQ612Az9+RSSerX036eOO8AeDiBdCvA/i3cL3p+r8C+Pdw7ZHzlwD8oxdR/vGL///bi/v/S9tDBbHxtDI+4TkI2NH0KmBcNgrZhQPri/q8lG8ErgBesqvrb6avZ5I4YHMw6bfW0/Orgb3/9/pE9YwUgqfnDJ7t7NeUpWVsvgb23o6afsbuM0bWB+RrE4GrRh1rOuZcAUX9SZDyMBxzesjXcDgsG5iTyQSz2bWHzePHj3FwcIDNzc1yQBg3KqfT6dxzAgTYpaWlYsenT/9sNitgSiClK6oqR+bLTdO2vXlLFd0nCeJ8ry2fAKbCWV1dxWQyKQehnZycFDMPy8409Qlj7lmw7WhuIotv25sjKFh2dWpQccDjmHAA9VWfjxXtUxUHU1f8Eehn4grIx6CbdTScxo1WM+qe2kf6MPu3Afxcc223HwD4B23b/o9N03wHwC80TfOfAPg/APzdF+H/LoD/umma7+Ga0f+5rgx0ors9XcHEbXkK6G5CYBhOQKaZMVyKgpOyBWfo2tHeEV43ZwfAy7bqTBllktnl/b+bgjLWomVzJqO/9QEg4KZ/HPij9oxWIM6ove0ywNU28LauSZS3/lbFpW3NTVICuNZd0yPo6Ov9RqMRABRvlIuLC+zt7eHZs2fliAGONdquDw4OChjz4LPj4+NyHj03PHliZdM0BXB1Rcs0CarAzeYsFcZsdmP35zMCNEOxv4+Pj7G/v4+joyMMh8PiWnl5eVleUUggB27AbGNjo5yiyXq0bYujoyMMBoPyMJa7oGpf+SpOiZX3gfazKnL93yWZN43mreJMXcdsFEbnncetpRUB/iLSxxvn/wLwk8H13wbwU8H1UwB/eqFSYH6Jrh2jjasg7p+o4k1z84h6Zv+OwCEDTS6pItNNBGTSJnP11GsOjD6YFNyyZZ6XW8vA9vH7mTj4afn86IEau6fU2igCXS+L3stMN87AXHFqnaKVhX842ejDzk3Gg4ODufRoyhoMBuUhK8anuYJuiGdnZzg8PCxn4D99eu2vcP/+/Zf6iXG5AUtlcXp6WjaDCd78KLvmioN7YTynh26Z3AsAUOzkZOQ0xbDudBFlnZjW3t5eeaJXXyDO/qfy4AtPmD7fbPX06dNi3qIpSAmFkiody2x/BU5n3tHcdVLjRMLvORHSseaY5KKreJVoozZaIWQKQH9HxLJL7tQTtJF5g9cUSPSMEV2+OctWM4yeqKfHqHpHa7qM64emAUjT6MOc+TsDORemmdnqauy+poS6JGK++luBPjrWwCUC/qyu/tuXvDopF6lnxBKdKBDg9ShggigVPgGJdnEde9w8pW89AZLM+Pnz52jbtviy8/mO5eVlTCaT4jLJ4wv4svDpdDr3QpPz8/PCiJk3jyCga6jODa5MqKTYVnqkAln/eDzG/v4+ZrPr8+q1j3mWPw9y03Pu+WwA0zs+Psb29nZZfVxdXZVjIvb394srKd/hq/PdiQr7L1pNO9hz7ukqLFoJdM3BjEjqKjASV0I+d9yMo3XS+R4x/kwRdcmdAvvIzKKTU7WgNl7EGAnMdIPTpw39CGOmE5kNyF4U9J1JRkv/LmBfBOyjNCk14Pey6kDpA/wR0FOc1WculL4y0DL5g2ZRnXzC6ARR8GV6brqL6p8xOQKShiHIccOTAMv60otlaWmpMFSaVvjhEcdPnz4tjPbhw4fY3NwsxyLs7u6WM2e4qdm2LTY3NwEABwcHhbnzoDRu4BKsdSyyLiQ6vE+TCVk23TQJjDzPZzweY2dnB598cu13sbOzg8FggIODA8xms7I3oMdF0CyjPvIEcPrzE8jZXpPJBOvr62VTl/1YG4sqEfPndTffONPXseTpZv+1jSOikM3HDPQjhRCZilk/BfzP3YzzuyGcLNppPkl53T/68hGyGTIzP/jJQV+ZQwb2+hIHZ4XKanVjifciEwElGigqfQG5JjWw57U+EoE4Ad7NONlg13g6kCNWrmDtaTBf1k/DcQyxv7K0vTwACiBys5AkgoDO8+npgaIP37EcR0dHODo6KoeHbWxs4OrqCpPJBM+fP8ejR49wfHyMt956Czs7O4Wt7+7ulqMKeF580zR48OABdnZ2sLe3Vx6iYj2vrq7KZisfRFLFpyyagM76XlxcFIVEZs560NwDXAP8dDrF4eFheRvXYDAoJiYAc3OMedOEwzalguGc0xXGdDotb97iKxoHg0F5ITtXID6PsrnDPnf2z7TUSqDzIWLpEbDrPPYy1Ewrke3fcWE2m3c60XEelfeL2KD9XRGdnPpfJ6iy7QjsFdB5xjbP54jAXl/p5qsHZUWaH8Oo3zKXv5yE+nKGaHDW7PYqWpZsFaDXHMi6QD1bouqgddOalt1t9VkdvD5aL2VgrvS9/z0tXaJz4HNM+BucNE1Ng+CuG68+IfWgMgKX9ivZ7NXVVXk3LAGM58w8ffoUk8kE4/EY9+/fLx4u29vb2NrawnQ6La8c/Oyzz7C9vY0HDx7g9PS0rAY2Njbm0uXRA3qgmLJnrQvryXZSzx0qL64Y2C4rKyu4f//+nPvlaDTCeDwuHkH02vF+07LwCVn2NcfMYDDA8+fP516/yP0EPzpa+0PHYPTpy3iz+eRjm2MgSttXkJlEisCVhwK5jkFdfehc+UKeoP2ipWma0tlqF+dgcbbnYE/gVpMNQZ4fvaegr3k4CxwMBsXuqiyZ7JIPilAh6FOAeoY4JbP99THlZIpB/0fpR2yWEg0WBV9lHJk5xfPsKg8lamtfjitoRIDvSkHHgwK+ltnLzb7UB6RUaasicFMVvVboMtk0TWHfZMhHR0eFmS8vL+P+/fsF/AaDQTFhLC0tYXd3F9PpFOvr62Vj9/Hjx6VNuLkJXPvH68u927Yt+woEWMbT5yN0DGt78CXiZOjci+D5OyQznFsbGxtomqa8rYqMXZUwV7q6F0Bb/9LSEjY2NnB2doZnz57h/v37WFlZKasnnhGkfZ4BvROOSAH4ERiR+NjVsRK1oY9BfkfpR0TMH5hi2TwNB/pFQZ5y58CeE9Unun7rx5m6njOub/eJWH20aUv7o64Y3FzAznf7P/DyIHRAW4R1ZEw9+6/Xaoy/JjXl4OlHYN9XGI+KXdkMRZepbpLS+AQh9jH3apT1cvLS+0TTZdoELfavmuUAFOCjCYX3uUFJoGzbFoeHhwXsDw4OitcKgLIhy83Xtm2xu7uLlZUVHB0dlaMT+EKRra0tTCaTcu48z7thOd2Eo4BG10qOP4KMgrM+CMU60PRzeXk5d8wB22FzcxNt25aVBtta21fNRzTp0GREpbi1tYXDw0NMp9PSF9nKNzKNutlU6+9KIIofXXegj+ZSzTwYERMXArh66HjZnWDpuORYXkTuFNgr4OoAdqDXDVNO7Aj0dUPWgVsBmP916a92X9/QpWkjMgH58o+aWM0XvhzsYq21dvNlLr99AEWiJho3Y0VliPLqkkhpZfb4yNziE8rrTMbrZjoyyslkUh5QIjgzDG3j6ilC5uxtyPHAduMTrApOTFM3cxmGLpM8Y2Ztba0cekbzDdm1PiBFcwZZNE+8JIiyrNxw5Zhm3aKNVNaHphV9nSBBhiDE1auuCJqmKW/YUts6xzU3sWlanc1mRaFMp1M0zfU7d3VlcHZ2VlYpVDY6VyK2nt3T/ouUgYqvBnQPSuOwPdzK0GeeqkTA7rjgFgSOXR6tocdKLCJ3AuwBvATIbiuPNmU5iRTwo2V8BCJAf1BkPH4iNq/LPR0U/iYhflwJ9AXPTDFkHd81IHhfAdgBX+vj97tWK9reOkFqH1fKbsrRtNW0QI+Ps7MzTCaTucf4CfTcsGSanEQ8SGw8Hpc9HvYnXSl5Jg03OAmwBDO6JBIgycB4hHaF9wAAIABJREFUD7g+8IxjlW1JU87V1VXZ1OXTsrRf82XdTB/A3IanvheW4MAHsJgG54qaHqns9G1UnE80M5ydnRVFx3QBzG2oKggyfx6TsLS0VNxGubdApQugKBr2A+c/gDkl4iYZtp/f97GdzXFXBDo/dZzqOFagz0hLlJfnyTjRSkLNhuoQoMw+836ryZ0Bex0sbpOPQF6VgQO9m12A/iYQ/2iHOFj5sllNC5x8+k5U/xCkb2sKWZRl10TL4G0XgayKD3qWpy9Td7DwVZd/VNGoCYcmjqdPn+LZs2fFd7tt2+J5QrBnnTnR6BNOmzkfZGrb66dH+UCRn+Gir9CjAuASm4oeQDkiWBkr68BJzTJNp9Nij9fVxmQyKbZz9X6hIlHvFwVGzhEyfwVUtuloNCr2cj2zXm3+BBqyeSo+PkymSkhflq4gzjblKobp6T4Y6xuBMSVbxUYrZxdfGeixFJpeZqbxTX8d356P/9a+1/rxyWVl7url5+W9zZy/M2APvAwI+jsDfXc7c8CgeAfTfulLJ/0fdWAG+LxG4OFLmHUZ5h92rDOKjOln5pRshXJbJRKJt4Wy/WwlAMRPLjtwR0tiB383w/meDdnz3t4enjx5gsPDw8Jg6R0zHA6LyUMZqNo+9SwYfbBKV3PsV9q0eSwxQZagRe8cnj2jAM36EfAYRzf2eSja5eVlWakAmDONOCHiuKJ/P+eIv4aQfcj2oyso2TrbXgGJG8A8p0ePNfD9Ff7niomrL6alfa0buTQNKdPV3z4GI6CPwvjKWvuefcV2dNFVkNYxCuuSKRZl8DzRVJ/h8PrqCoDj8MeW2QM5Q82YoGrYPjY0BXIFdP2veWZSY6X6OLtv9HEyU4tzcqkNTs08Xe0S2Slr5a6Jb4BGH59YUbtofE/bTTKRe63W1xW976Xo3sxgMMD+/j4eP36M58+fFzMIWSg3UpmuMm6CsY4F2rMJ0m1783IQfRsUJyBPm1RlQNGVB0GQppPV1dWy4aovBWc6FxcXZQMTmH9ymwDaNDdn7PNF4MyHrqAEdYIFf7MOy8vLuHfvHo6Ojl4CPmXKZN0EKJaTKybuFXC8KLuniY2rFZadqxpdhTC+r5oXWc0yrs45gqkfusY20PblfNJxx7blPlEE+qqgqMBms9mcYlFsoOJkuyuwO7YpkfuxBXvXfjrQKBmYRAze2bsPnAhkPD9Pk3HdZk/g0fyA+adM3SefYMRBqKsAtz+yvL7E9DK6otNB6KwoMtV4m0TgH/WHKxlvG7e9Z6w+WpFEQK8mO7b7+fk5Hj9+jP39/WK+AVDaU71yaGfn8xgbGxsFrGgvpz2etnKtF/On0OOFdck2BZVRN01TzE66CgFuzDLn5+fl3a3+ABVBfDwe4+rqqtSJZ9yvrKwUWz890gis9Jc/Pj7G4eEh2rbFZDLBxsYGNjc3i7mK7Ud/fq6OWB4dW9wX0LGtRIerHAc6KgiOASomB3uVyISj81N/c3+FgMq89alhCs1u0TzS+cKNZ5aV41KVC5UJlVvT3JzZzzYjq1evL50bvkLW50oi1+4uuTNgT0AjYPp/71iPC8SbhRGL53UK01Y3KF0yEWz439N3lq/CtNnR6jWiAKZa3wE/+91HGE7bZhGbfHYvWsJ6+zqbz1i9l1cHegT06mXF8fHJJ5/ghz/8IQ4PD0v7sc66fGYe9Hkfj8dYWloqvu08tvf58+fFNsoDyWgOWllZKS/hYL8xTa0T60omywnLPuRegJ7/zvIOBgNMp1McHR0VwNC22NzcLC6aBF4yVlVSVApLS0vF5k+3ydXV1fJQE/cednZ2ANyc0ElFpGObYQeDQRnPwI1iZhvrXNZ9Cd5nOuqqSQBlGGf1igHKoHXeqIKnSU5f5MJ7FDehsp5uutF7ev4RxyLLpxv0TFdXOqyvrupZfyVQimVax+h+H7kTYB+ZV3QD05m6do5OLjd/6H19eCHK3xkC8yIwRcCvtlLNT/NVsCXbUTsrfZF1gKhZRwezmw5cCXhdtDwsg0ukBLvMMZ5ubSUQgb2Cvq8WvOwKcO5txfp89tln+O3f/m08f/58Dog0DCfJcDjEzs4O7t27h62treLKeHZ2hvX1dTRNU05w5MFlV1fXz1TwyVFnX8q4WDbG0XYim1xfXy/7CxzXXEmoJw5fVMIwbA+aEPQNbxwDfAE5T6BkGWez68PGyOrVX58PhdGMwDFJRcQ8uXrQuUeThrJNmnZms1l5uhbAS/fJpFk3KlNVGBz3vrpW8Mvmjh9jonNI203zYFvr/Ndwvm9Dt1RfRTvzZtq62arg7ZvhbrNnPFcAi8idAHsuP7mrD9wwcnaSM3Q2impo7xwVbl65B42Cj//X65wICvzK9MkCIvbP+qjJgv91c1GXf9FGjtr+1ESUTYquFUCX2YbtFoF4VxpaZ2XzDvQM54yeeauC9U3a2ez6fam/+Zu/Wd6qxAnAdnWT2+bmJt544w2Mx+NynU+I7u/vYzqdFls6gPJWKCoCtjnHkT5pqnlx8qr3CvuIm3F8Q9XS0lJxbxwMBoUxap9yzNKmrx4xtJNzhUL7vYLhaDSaY/p8Mxb7QVeVSqoYh774zI95sh/UC8nHJfuNoEw23DRNOVKZqxRXYM7cmYZ6temc8b0vjevmEl8tK244CVEQVgxRM5CKgnU2T3mf6XkY9oWSupqFo0vuBNhfXV2V4071PBt2vLN3Cq8T8DOgjSQDRjUxZKBP9haxACqDLhDkUpW/1d3Kl6TU6jpo9X40wKMB4oOji807MPdRBp62KkMHeS8L2zwDe343zbXt+enTp/j+97+Px48fF3bOeLSr6tPTo9EI29vbGI1Gc144bE8y2fX1dWxtbaFt2+KrD6DYo3XcMQ198pRjlH3DB6MGg2uf+KOjozkbP9PWp0xV6emY5D4DwZObpGTf6+vrxW9fCdLq6iq2t7cLaAMo5hyaF9u2nXMZJjmiguC80SdvuSrRY41p0lKmzdUD+2hpaam8LQu42WzmvOLYdqcG32hlOGB+j0z/q1Jgu+pK2OcCv5VUKMlTYhcBr5dDlYIDPUmBm2f57Sv2LgJXkzsB9rPZrJzjzY0yLjdHo9HcJhMHDUUB3sGZIOkdpcAcMXkF8Yj5c4LoE7cR6EdMmGVmGJabS38uiSMQd4birp3RSiAy87h4+RzkHfD9E6WhaXkaGj5bJUTKlu11enqKJ0+e4Ec/+hE+/fTTwr7Z3+PxGPfu3Sv2eAI/7dzcGNc24wYuAZ9tt7+/X8wq3PzUoxi0/1RRsd3psaH9zqMOCGwcV0xP9xc4TtSLh6xYTQUEVNq89/f3y1O6XEFubW2V98JeXV0VcxKfLeBcpFmRaVK0vVgWrkYJ9vo8AdOiMB7BO9pwZvvR84n2djo28DcBlOVxwMxWvq5kfazyWx/IVA8Zl2hl4KYuTVvD+jVVCLzvefqKYxG5E2DPpS0HPBv7+Pi4DNj19fU57wIAcx2tJhjdLGH6uvmqCkIHgAOTs0pXAkyXE9fvuemG39GSUZWPMgnfdFIwzz4O/Dogs4HiZXGAjwBfJTNdZUxe28R/e3upnXQ6neL58+d4/Pgxnv1/1L1biGzrdt/3n1XVt7p3V1/Wbe+zpK3NPjoELE6C/WCIlBgFrJicF8s2CUFSBOclNoYQIjlPISSgPMWCgJJDTJBCwrFjMNKDMQkOAglik1iRHqRzzNHZZ2uvtVevvlZ1VVdfqqtq5qH6N+o/vzV77W45gj4Tmu6uy5zfZXxj/Md/jG98p6eB8pCHjY0NPX36VNvb2wUDAHXhlIW0DGpS2oAgJob0+PhYWZYFl1yv1yNlVloqX4KUIGp3wUk75H8HK6D4tNQB6Nh5f5A99e693z5/4/FYp6en2t7ejoND8jwPb5l2kH9/dXWlZrMZgWLWIrLvHiZZQzyTuvupEQM9p+vAFRq1872PeAkoelfwzJtz8Mg333daN6V4uVjHqWz6Onb55ftlBsXXaNlcSOXn0PprqSeSXt52p84eej0aZc+kSUs+DTeQFLS1tbWoEw6/x4Lge66EmRQEFqH399xYuIuXcnpu5X3hOkJLFX6q7F2wfALf56J5f9IrRcAeAHZlX0brlM0B90zvnb52l/J+yJUKcNqv9ML7Y3csh3m4G1+v1/Xs2TPt7u5KUiiLarWqZrP5Tr0VaZlayMlQlESALjg9PdXm5mYEIj0nH0oFRc99PfCIHBPohNtGZjxoyRogi8THn6As6Bd0S3uq1WpBObI5jHYS98Ko+DhUKpXI96fujnsTjBVGEe+TTBcMEIbNvV5JBSVJn4lJsLeAOcDD8sPU7wIyqTzxfvq6GwNfR74+U3o4/T4yxjyl3LsrbK4yAHQXPePr09eYf869hT8Nwn8Uyl4qHsBNx0lJ5H2EeDQaqdFoRFCHw449Fc9pG6kYoHUvIKULpOLJ7yna5zU3GG710yDkXcrer9QLSX9cWX+Z0vZn+mvOMb5P2ft33tfWFJWXCfl9FXpqaNL7z+fzKINwfHys4XAY6ZDIR7PZVK/X0+bmpiQVECFcNpt4kAkP/FPYDBqHOi7T6VTdbjf4Za+g6TniZYuXcaedcO0oDvhoQAjPxQBhnFDGTnN4UJc+QZlAgSKHrJ0UfXr8B6+GonG0jwC0tDyEhP6B5B1UYMzcCLJmPJg5nU61vr5ekG033jwjDcT6PZxiSQOlZbKXrouUuk0/756TK3t/Xqrk/bnIl9/XlbbLiv/viv6ufj1U0UuPSNlLxfQmLudGWRAgCjaCdLtddTqdQiQ/va8r5zKE7graP8OidaRW9lN2/7tonLL2pX+7Unf+PV1cvjDSnxQN3Od6X/vomwukI4wUjTBeZfd9X3v8PRTfycmJjo+PdXZ2VlCwWZap0+lEzjzIFcNbq9VCRtj04wbaqRN2hfJzeXmpDz74QL1eL2JF3id+o7D52/vM97wOjVMe0vLkKZB6Kj/kyfNMlC7/ez54pbKokY8hAqW7QvGYDkiVNEzGh/LMxCvwTlh/nlqJsUoBG8bIz4RgrBmnlLf2scUYeSDc15ODHy43pilK5vtlcSBkjes+9Iq/7m13+U+BkPcv/W76XhkllNJjD7kehbJnUaSoFcvoEXqpaKERWnd3fQLTQeR5/l7q0qa/WeD+f5ou5d7Cl3H277tSa1+mxNOfMl4/HcvUc0iVtj/f/07RdoooyoQxndu7qKEU9fhn+P/6+lpHR0c6OjrSaDQqFAyDntna2op6Kh736HQ6oSxp63Q6jdx1aekxQt+w0Wk8HqvdbuvZs2dxdB9ABOXqspfKi7TMtIB+pA2Oct2Yp6ADZUr2EEoYXhu+v9PpREyB+3MQCGfaev6/K3zagCcEHcSmKt5ziojsJO6LoQN55/kyU8eTDnyumQuPoxDLSPPl+e18uuedp3oBgOjy6jSsU0y+JtxLSMcnNRypMvY1m8p0GcWTPrtM2adry/WKv37f61Eoe6l4zqJf7nI6gsE1ns/nGo1GsfioDc5FRgGLwH+7AmcQ36fwy5A9qCdV9q4A7sNzp65kGVr3hervp3n5X6bs73PdZQzuUsp+pQKZUmWu8P2ejAPfubm50WAw0GAwiMAhc16pVNRqtdRutwunk4Hs2ZqPghsMBgEe2DBUqSxTIcfjcaRZDgYD1Wo17e3tqdPpBEJ25F2pVAI9eyYOsuAHmXiN+pQikJaBRHLOiQ1gqHycoZGIMUhLmoS+IkfQng6G8EJYL25EkJmLi4soonZ+fh7pkvP5MrjsQVWAGgreUTjPcPlO17G33eka95JdaXPdpVx5RkqjpAHvu4Bemgnnsulzdxfi98/eheDTtXPXunQjxbN/6JW9B6rSDqZKhwXvOapnZ2fhYpJLTdqbu9nc7y5UX6bwU1TvyN6NhwdqU5SWoth0wsrc2DL0fheyT5FOmcL3/qfX+xB6GTr3q+y9VLE7Ik4Vfnp/FgoHdXtqJei62WxG3jgZWowF+dqz2SzQKMqM7BN2xk4mE/X7/dhQdXZ2pslkona7HQdsp9lNni0DMqUIGYqKOBKZOh5w9MJg5Lz7RiUuOG/QMcrai4nhiUgKdO+eDNlKeZ4HrYLM0haCo9Cj7FmYTCah7FlLjCHryfcG4Ikw/x6T8Cy7NMXUUXWZQQDguI5w5Q+gSsGNGwae7QXg/D3a49RRepWBsbJnpZ/l/3R9I/f+uq+fFBT551JweJ/r3so+y7KqpP9H0hd5nv+VLMt+RNK3JW1J+j1J/2Ge55Msy9Yk/Yakf13SiaS/nuf5Z++7NwOHgvdKhu5OgdRc+aFE5vNlTejr62t1u1212+3CmaBlKZkp2mdg040T6WTy/bJAbeo9uELzibsd13fu6V6HK23/nRqBlA4o+44/I30mv8sQO31PlXMZTcXnUuXuP4zvXUofBXt+fh5BQwKyKysr2tra0tbWVhTmqtVqwbHP5/OoX0M6JYHWSqUS+dtk3lAHB6XGBiNiQIwfIID/yWln7JA/nuNXqkB8py+y7dkvkgLhe90ZnoW81+v1iCOQj16v1+MwcPf6UPaejSMpvJzRaBQ1/RkL96YcLGHA+A3f7/PvCFkqAjSnbF1eaRfpobTZKTPGDV3h2WdcZfSg001cvs8hzfbxNZIq2y9D5Gk708+7wSprq68pf3YZtfOQ6yHI/m9L+o6k9u3//42k/zbP829nWfbfS/pFSb92+7uf5/mPZVn2N24/99fv84BUEaYKKh00XBrPqiB1DBSAm+/pcXcp5lTRpbyeX3e5Z2VUTorw/ccnL0Xf6Q9jUobgv+zHFXpq8MrmwX9YiI66nNYqU/RlyN4Vfarc09+Xl5eRXknueqvV0tbWlrrdbhzIjRKtVCpqNBqlChckPR6PVa1Wo8DY2dmZzs/PdXFxEYhZWnDRGxsbgU7T9q6sLA7+TgOI9BGlggFgTPEuAB7S0hB4ATBJkSrq6wBZd09yfX09slhoKyCI6+LiQp1OJ9rDs6+vrzUajcKrmc1mYQRB+ZICyTuFg/eEsmfTGV6Iy5bvbOb7zCljRx/dq/bvIBe87gaDyw2Gf6dsvTlIK0tySNdlmTdwl/K/D4J3b4Tn+O+y/qSfKwOg77vupeyzLHsh6d+V9F9L+k+yxRP/bUn//u1Hfl3Sf6GFsv/G7d+S9A8l/XdZlmV5mVaxywee/8uuslQkV0ggmdt2B9ohRxl3tCzX3oXD3yvb0szC9kWeeg4l4/jOBKUGLnXpylDEXYYg9QpciFNvIR3DsjalSo72p3MgFcsmeNvLPIX08gXD2A+HQw0Gg9jg02q11Ov11Gq1oj4MdeYJniIH5Gt7+8k4mc8X+fonJyfq9/uF9EFoFDZgSUXjmqb/sfOT73nRL5dn32SUGkz6Tq0eZBHU7DQH8oXylBYewHA4DPn3g7uhdVCslEJgrKbTaaRcYuwwsJeXl5FFBIpH1ukLfU03UZXJq8tP2VpJeXDec88EOeP7tMUpF/coUxnj3ox7ys0zx36551AGkNL1nK7/MkNTdp90naQAMgVGf5bI/u9K+s8ktW7/70ka5HnOKL+W9Pz27+eSXt02eJpl2dnt54/9hlmWfVPSN6XiRofb7xUUtl8pWvRF5ANCYaurqyutrKxE+h2IkOf45Ltrxd8EYJ3r5Lv+v7fd/04FI7XkZQgi7XeZ1S+b7NRIlXkAqYFI25zey70c94TuarsjFh/bFAH599M2TafTqEuf57kajYZ2dnbUaDRCVqDrUErcBw764uIiKAsvczudTnV0dKTj4+NArp6uBw/vqD7djEW/KCaGUnKlk2Zy+JwhtwSRydbB4LAnAI/UywivrKwE3+80ZrVajUwZYgF4C7TRx9y9La+97kfj+VpjXDBepLRKi3LItAm6BBnx+AZtds+DMfHAciqnyJTTif599IU/n/s4TeOfK0PsqYw6WPK2+3zSjtTLv0sxl7EVTuF5v8uU+p8G0XN9qbLPsuyvSDrM8/xfZFn2U7xc8tH8Hu8tX8jzb0n6liStrKwU3nee1BW4tamAJFOk6sYDRQ3354qee7jr6xPuG7L4jk+EC48bHz9Qmvb69T6l/2Wv0Xfa70buLpcvVf5lXkSZ25waPtBcmfdBO9JAW3p537lS48PpTPP5XM1mM0oO53keBsDn3o0+mTN+SDa0BFQO59OCfqFA0jZ6QNjbjbLjfZ8b/nYly8EjGBU3Co7YkR2MDegVysJTPynIBqVEH+Dw2dFKO+fzecGIsOnJd7I6ReTZRG6wKVTIGGAYsmxRPbTT6ajVasUmr/Pzcw0Gg8JcMz703xWpK+dUJlMl7bLuiN9l3vdk+A/3dCRPmxzxA/h83fncMe+Mcxlav0v+0zXka4Fxd2PC/PC5h6L7+yD7vyjp38uy7GckrWvB2f9dSd0sy2q36P6FpDe3n38t6QNJr7Msq0nqSDq9T2NcsKTigSZuXVnYbgC88+4ZuAInWIfCdyXhij+lJFKUweVco2+aYTGRh4ySZOG6S8eVvlamUL0vKcr28aM/ZXSLf7bs8gXmyp4xcY+rDH1xb3fz0z76e6mhAolfX1+r2Wyq2WwGVYICdqTKnEnLDVgEZOHqOUqQlEJ21ZLWeHV1VeCD/ShDjADj6Ubdc8gdlTrKw/g44nSDSf47/HuWZfGbvQPsYk0L7zEXq6urhd2t/hkUfq1WK9SX8v7TP0fr9IXx9cJlbnzI1MEof/TRR3r+/LkqlWXcZX9/X69fv9ZgMCgYOh+TlDd3qszH2z/jslZGTTqa556kYqfr2z2MdGOcy69vUKMPLn/p833tMH/IhRu3Mo8hNV7++l1g6n3Xlyr7PM//jqS/c9uIn5L0n+Z5/h9kWfa/SfqrWmTk/Jyk37z9ym/d/v9/3b7/f+Z3aZby5xU4tRR9+qIrs25lhoFBh59MXT0mEX7SFXPqkpGuR/0OAnC+WaNWW5TTbTabsbBwg33BlLl5rkDLhu19it7RQIr4y8bFPRRHEPd1E13Jp8gkvUeZgXOB9qAvqBXaxg0Mz0k38KCAyCaRFKiVrBxqstA2arJ4uwk+eq0XuGtkEuXhm4mkZfpkOv68B+JGZqiR73EkDvCQpEajERu+UpBTq9XCiDjdkud5UDgoJrKKyCAiHuHGp1qtRp8ZCw9OVyqLALiPKbGAra0t9Xo9ffLJJ/rkk0/UarUKdNrz58/17Nkzffe739X+/n4hG6kM1btn73sCmCd+p5k+6S5bN8DMmwO2MiqoDG2nwIR7uleb8v0pM8HcUmmUMUAuGGdkj4v59cPtPQ32s88+032vf5U8+1+S9O0sy/4rSf+vpL93+/rfk/Q/Z1n2x1og+r9xn5vd5dqkSo9BLLNsXkvE369UKhGMOjs7iwXjbjqZBa6YPbVPUuy+9DQ99yAceTmS4j7shkwLuZUpffp6l+JPFT0XfcMoMh7+viPu9P6pV5MaDEcX72tvOv5+P5QLP6Aj6tH0+/07FxPcuxtZNxLn5+e6uroKdC4tUSMLxVPs/N7eFhQnXoSX1aVPvmvXaSTf1IdMobg8aEwNefYJQCsRJ3KZ9vzw1KC7R0kfeRZxBQLd3BPDhNLGsKX55cgz59NCIYFOe72e9vb29OLFC3388cfqdruFI/mq1WoUqGMuDg4O3onJ3QVskFnWmitk3uO3UzSOihmndK8N/XOvlNfuAjzISLrO6KvfB9AHiCQdfDabxS5k1hHz3m63I6U3pa3cSLEWfvd3f7e0nWXXg5R9nue/Lem3b//+VNKfL/nMlaSffch9pXdTkZyn80F0l8kn6S6hSTk6UjP5bEpV+CKB1wQZQQN5USlvJ8oVZXF9fV1Y4BgUFg6bv1Ij8T7l75ejOd73+ENKo/g4ve++6edTZU9fyl7nfmXPSd1Tz/RgAe3v7+vo6KhgZG9ubsLA+vFzHtiC5x+Px7Hw5vN51HeZz+eFE5q4D5cjWEd8TuU4ZecK2cfB3Xz3GHkfo+LeGYiPecvzPBIMQNIuY3gI3i68D7xHV/p+AAhGw2XG68bDvzMe9XpdWbbMFiJ7qVJZZEi9ePFCP/qjP6pnz56pWq3q9PS0EDjHUE6ni4JyH374YaR80ic/WYvXPGWV8cT7oP94fVAzrHXWGfflHAJPOfUS0egd37SVptNKintubGzEePE6HiMyScB9fX1drVYrKDIK6rERjvmh3XhXzANZZHiyyDdxlodcj2YHbRmCl5aW1BGc83VcKXpN//YJ9UBH+l3ykOF4QQQsqDIr65SPK9gsy0LQHRVyKlej0VCj0SgEvPyQFn58x2IZZeLKJv3xq4xKSe/J/+n7rtAd2ZchoLsMBH+zIECizO/+/r6+//3vazZblL2Yz+eFFEzm0+vDu5GF/mHBnJ+fRyohz+Pzjsacd/eNQOk4OhDxMXDe2NG3U1BQJ/Sd1xuNRsgYGTHMaWqUHOlS7AxlCJ2FrHjQlv0IyHO73Y7vQRn44SOgTLwh+kiAmyBws9nU06dPtbe3J0lxBjAxEx8X1t3m5qaePXumV69exbNarVY8kz0OKGMUr2ctoXAxEMQNHDC6QXClCIWSnjHAuKbPzLLiYTUYQNclvsEMDy3LsqB7MZiSYk+Ee4TIAzGl2WwW5ypkWVYYU5Q+accPuR6Vsve/WTCOlB3h36WgXNH7QKYBExaQI1S/3F10JM/l/CACw05Db6+nodEG+MzxeBwcMQbD4waU5m02m2q1WlEiN6VQ7qLAHD2mY5yOW6qUy967S+G7EUmpBsYhpYCcuqnVFjtgf/CDH+ji4kL1el0XFxc6OzuLcsbcH2OMAQb5orhJD6RqIxumkI30lCNJ8Xyv1c7nWex8z/vL/ykt5PLhYINFzn4P3HfkZz6f6+rqqvA+xod4g3PmkqIqJcqSH28jioTD0jkRzncMO+eN/CM/3IMdx6D0nZ0d9Xo95fnyEBPG2IOi3o88z9XtdmMzGxk87Xa7kC7qc8PYeUDavSNO4+L59AMD5RdzBk2ierzJAAAgAElEQVTr8+jHK/qeCX77oTaML2NNf1dXV8OgsI4pWXF5eVnQLTwTw0PGILvHMeo+rsRJyqjdL7sejbKXyuuxoPwYBOfmpHf5NR8AXGZQinT3+ZCeTcP/ZZ9Pkay7jiln6Arc3Xwmm4nGzZSWyI9+r66uqtFoqNPpBO0DQnUjlbYzRdd3XakSTpW4tyv9XOplpHOQGgYfY4wZ43N4eKjT01PVarVAOH7WqG8kAsV6tpOkKDgGpcNuUOekUY7MCfPh3hfKKqVb+PE6PB70K9tqz9ihDNzQuVEFrc1mi7o6rVargLyn02mc3UDNIBSCtDwDF8WdgpjhcBjtr9fr6na7BSXsNIoHDRlXSaFs1tbW1Ov1tLu7G2PhexqQqxRkYKSq1WqUjXa58sQJl00McKr03SCk3hUBbAc7Di48Wwn9AO0Cak539uJ10wZQOoaZYHo6r8w/gXZkDCPJ3JMV5tQstJmPLYaojCp93/UolL1bKe8A6WEgLwJDfMcvvucRcQbZ3W1fnCn1k3oPLngpYku/i6X2YB2ve8qeL8K0PdzXjZzXVh+Px+p2u5GO6AqDdvhzGRdHpD5erowdEXobHdGnit49jHT+/PtlxoT2kiH1/e9/PxZDGhOhbSgEqBrf/OQeEgednJycBO2AAqjX68GhsqhAf9Ad8LGpF4VSK1toKAe/PKbE/xRu87gGQGEymaher2t3d1e9Xi8MCwi00WhEGurx8bFevXqlL774ohBHYhxcyeDJcsznaDQKhYayR9YqlUqhSBtyCEW2trambrervb097ezsBKXhhhC6ATlGHn0fQLPZjO95KrQbGcaGeXXFB+3lZwlzb+YMJYwhQ7ZpT7rLmv66cXC6xtcGhoI4UkoBkhAC9YNOoH3IildC9bXp/WUu6Q+y9D4QV3Y9CmUvvYsMnYZwzqrse75wUiUnFTNQ3Bj458tedzfNrS0KlWc4oud7TERaTsERriPDlJZxV9TTPc/Pz2PjCny/IyjakxpQV8L8TqkWj4uktA2Cftd7/hz/jM8FaMh3ao5Go1DKzAGLzFMcvUQCSE5anp7Ed/r9vl6/fq0vvvhCg8GgUN4ARd3pdMI7gr8FveJV4AGkm/B8gbkh8NRA5gBFg+ylaYTIAO78fD6PuaX+vfcbZF+tVrW9va0nT56o2WzqO9/5ToHr3dzcDAQLrUBAeTKZaDgcFtYDCtAPWXH6hBTK2WwWReJ2d3e1ublZCBQjyx44ZRw8CMspXKSeAuq83o579sgE3/cfACBzkXo1GBk/q5g58Fo/GAz3vplP+pB6FPwNMEC+ubfX/WeOuT+UL33lwsvEILjOwaPNsizqPD3kejTKPr0YxDTQkyoY6W7PwHl/NyBOd5Q9l9+4t2kGQ/p9dyfT11KXLjVO7t470pOWW81RCgSNx+OxWq2WWq1WIZffDdxd/Lwr6ZRuSpX9Xei87D5l6B2jhWdC3XinaIhfuGvrc0ddI2gbnsUuWXeLLy8vtb+/r1evXun4+LgQlyHtFSqMxeknM83n8zjruNPphAFxJOaeEIuSZ8DP4gGknoinNnoMyQN9KHlShEnLJM1TWgR119fX9fTp01CS+/v7khbGCiWPcnB5pK/D4TBiEcgVconCn8/n8Tqov9FoqNfraWtrK4yEI2v6whpxr5a+HR0dvWMcaRfywtqB2nIKF7mj7S6jvgbX1tYiVuHlLByxYxwcWOBlVSqV8KqQsbIzE/BiiJtgGHmOA0bWvMcfMEisdxIKHFzg9TL3zN1Drkep7Jl8OvM+buoupZai5VSh8t1UQbqbJJWfUI+gpN6EGxxHn/4aSsGfIamAOrxdCI23D3eYIG+73Y4AbplL+D5KxhF7StOkRvUu78ANF0iaeARnujq68gOlvc8+Jiz0RqMRmRcsfFxfkByLczAYqN/vR549mRvUpvfDNRzx+34JSeFxsNBQtO41pVdqoFHyPvcoFjJEWNjQSO12O+gN6u0TxG82m9FOftfrdW1uburrX/96KGX6NhqN9Pnnn2s4HBZy1MnOgfqhHj5K070qxhhKYnt7OwKzZMxIy/N+PfaRxsX4u91uazKZ6OzsLOYd6oxx9b0GyCbyglHEK+A1l1NPkGA8GAMoLm+rc/bMr1N7GDXOKCZpAhqHOkxPnjxRrVbT27dvNZlMCsX03Mj7M90rgvahbcgvskm5C3TC+/Ri2fUolb30LgfM32UdLFt8/t1UkfBeSh05p+YUTRl9lHLsadtAGChouFe4ZgQVxed5u04JORfJ8/xyFEWRtzQ459+9C5X7OKVGMH3fMyxAb5QpQLG7G4/wovBB5XDooG5HeSzWer1eSAUEzTOWs9niFKq3b9/q9PRUGxsb+spXvhJ0wPr6epQLhkKi7g59on0gejZ3MW4obgysK++UQvT/UVAcK8hYpnPJdyirDA/s59RCpXA6FxulUq8V2ufZs2fa29vTH/7hH+rzzz8PGaFQGgYJenQ2mxXq8zttAR0BleJllflJDaijcagakPPW1lbsncDoOR9POxg/vgvqJ46H0mSNOAggc8kRfZpJ4/Pv8TXWIJ5Gs9kM9E+2DhTgcDjU6elp0IuNRqPggRAbIZUSD5W2oydms1kkF7AXCI9+PB7HRj0O1/Fzie97PRpln1IufrkA+md8sfAbwXdlzeVKjmf6e2V/l7WFZ/N9RxIpOnbEyOdBrLh81AxxNxNU4FSFI2gCOI5IJpOJNjY2IpB3FxVzFw3mBs+Di+7lsOgcKbIIQUqgNXfvUV4s7vX1dW1tbRXKSXhcBGTvi2M4HCrPc9Xr9YKLu7Kyok6nU0BtyAFKH15VUqBSL/6FEuOzvqXfuW+oG9pGhgZtdi8MGtDHmfcdNVcqlZgvz6eGY2fsyTDisBEAhMsFaZjQfD/xEz+her2uTz/9NNIdUbooQ09lpb/S8ljDyWQSnsV4PI5xcFqSeea1FD0j1yDeVqsVG+U8fbFSqUThNBS9c/S87pktLjPIAx4cz/fXfN27IUgTAxgTP3Qd4Iai930JKOo8z0Nx+xpyY8R4ILNssvKkFNc/nKIGO0CK7kOuR6PsucqQklTMo3dlfheVIt19XqMr6rKINu85CkMgHUWhGD1I6qjDgy2gB+6FgsuyLNK1UHhkjMCZUrWRReU0EMKLwCAAnlWQjm86nj4e6WedmuFvFoXzzb6wXfk7UsJwtVot7e7u3nlAh6fgoUAxFqurq2q1WsrzPALWLDqMB8HsPM8j6JfneSjJ9fX1OIqQxYNiXltbK5Trnc8Xm7MYVzfYqUzwvvPVnpLr48l3QG2gWTwfEKQj0el0GhvMUDBsyuLCcxyNRpGu+9WvflVbW1v67ne/qzdv3gQooC3EGBhj+spxhYAbpz14n0AjxsPBgOeQ+1pmTp12cgXotAW8OzGDarUaiB8DwXdQ6uvr6wWEzJ4CBwh818tFw7UjewSjobHwwjEi6CFqBknLg1qQd0AD65/aXIC16XQaxefY9OaUD7LG+kLmyMV/yPUolL0r1TLUedd3/O8y2ic1FiioFLXybAQVoShLR3RlLynoBBYywuanZc3n81BSBFhcEZOZgNFAyNNnsShS6sWR1Gw2C1Tnno0je/qaGkDu5fn/eA+ucFD+zpmDQvEwQKQoORTG1taWtre3I1gHynTqiXu4ghwMBjo/P9eTJ08KAWsQMGjbc65ZyHgP7XZbjUZDJycnwbW6e85Cp4QFKZL0hawNSbEByJGlKzqXrfX19fie94mYhqRw/1FkHsxzkADFg/HigvKi7Sj84XCoTqejXq+nr3/966rX6/re976n4XAYCBWFBDrnQgbm83nsun369Kna7XbEGtyTZp0gqxgDDKNnztBHxj+lWpB/9hKQauzACvn3cfdMIFeQjKMbGs+68o1aHqfg+w60kAeUNAYQ2XWDnnqCJycn4Q00m82QJQeHjClGGK8SGs4L1j3kejTKPq0BIhXr5bhQpUFGSYUJ4T3+Z6LLuPUU/TLIvIcCAI1j2SVFloTzrP48BM5RKgvaMzm8HfwGYUFZ+Fg4feVBPwTZFTPjkHpDKZ8vLbME2CmJwkXgJb1D2XCBdlJ3mGttbU17e3va29srFCPzBYzCgWJBmUJ1Eaj1i+AlgVwQF5tqvH4J6ZWDwSCoE1cKGGifLwLCjDVcMhvhnM5wA+rG8Pr6Oow53hxxC8YRg0N7nW7L82Ugz6kndgdfXl6qVqtpc3MzMkbYv3B+fl5A+R999JFWV1d1fHwcteZ90xl9ggPnyMGbmxu1Wi29fPlSKysr79Sb8ZgExgYqkn54lhDjhdc6m80CIbPW/McrRkKT4BWPRqOYN89ucS/bz9IF1TvlxJkDfB4Dna4V9/gkFfZkIJ+eyUMf8Win06lGo1GgfILTDhBA/vTVD3Nnnnzz1n2vR6HsfSHxv1+u5NP0wDTdEQFE6WFhUyQrvVvhMb3cE6B9rvhR9H6oNfd1g0VNERR/2h/fBk1OLhyjowJpSR84ClhbW1Oz2QylCSXgY+p0gsc9HP2jyKERUjTqioCgsY+fB+X8vhsbG3r+/Ln29vYiYEVfMWaeiokrDE86GAw0GAx0enqq0WgUOzezbFGgi3zvPM8Lhe46nU4hkIjyYnF5gIs59XxpvAICmRQSq1YX59imoMFl05U93gd54vzvmRe0zz0cZAWFxMKHmnC6i5TLWq1W2Fzk5+2Spluv1/WVr3xF8/k8uPi3b98GDYICQhY2NjYkSb1eLzJ5PNZB+3w9Y4SRb+hJvFqntODBKQvu6J21DHpmkxzjMhqNQmkit8wFYMepXNqJYfDnM38OGEizRMH7bnfmzb0Z10sALtZgrVZTt9stUFHES9yDZM3RJ5e76XQatFYZ9fq+69Eoe0+zdGXvaNwtLBMjLVFFiv59Y4jTGZJiIqV38+ZdWSMcoFD4VYSD9DUPsEpLjp+LRY2y9P65AErLKnq4jBiJPF/sosT9wwXf2NiIg9Vd0ECvKEbSIJ0v9fNbnWd1XpT28+O0gs+X7/jjO5VKRZubm3rx4kUgvRS14q4yzlAax8fHoWhIRaxWqzo4OFC32w3l4dUNLy8vo0TyV77yFfV6vQhsXl9fq9/vB/3h7n2WZUGfjcfjQGwE01EkjUYjFnAaWwBo0E5klM+wmJ12QzlBv3gtcwyYj6l7KxQNkxQyCC0BYLi6utJgMIgDRvCWVldX1e129fz5c21vb0cRusPDwygT7RRct9vV7u5uIS7DGHpwFkDAWgFw4cF5cTKMdbqmPDBKG0CzkiKm4JUrMZSgX4AY4KbRaMQadM8TL4uzDZBZMqOQD4/DEMxn3JlTAJ3Ltu+Q9b0KThFi9JE5xhA9hFFh7kH/P7TK3pF3mbJ3/h1OL3XHnOJgoaVV/Vh4fkgEA+nKmotFi3FAwYDAPQOFy6kTJox8b0nvbBYDKYOsoTnoH0LvrjBjg8vsiAKE5LEBBAXh428UkGcvoPBdmFgoLC6fE1dIqVe2sbGhzc3NwulL0F4nJycaDofa2tpSs9nU1dWVjo6OQiF8+umn2tzcVL1e19nZWSFTgb6ibMizf/XqlSaTSXDM6+vrury81Onpqc7OzqIKJv1kDFAU19fXoexw59vt9jsxHOgTL5yG8mcxMka0kUXL3ENhQMng4fgBFhgG0Ksrfrhf6AL3Erx4Gx7NdLooenZxcRGodnt7W61WS1/72tf00Ucf6fPPP9fv//7v64/+6I/Ce2k0Gvr444/V6/XeWZ/Il2ePOAft9CLAo1qtRjE2zxzzNERXdCBtN7Dc0/lr1rsreT5zdXWl0WgU6xhDgS7xC7AFUucQGcYPrxudgTHjuelGMI8DpBl7w+GwsBfC42roDTyBNDD8Q0nj3NVwV/7+g7VN+VtpmS3hATcPyrjyhbMtU/KSCsKCkPnnCdi5C+sT6W4g93K0g3IkXcuNFVQGgURpERDCTUfAKfdKGh4/pHa6IfHUMM8UKKNoPGfag7Cu7FHeHGqBsPpuXDb+uKBKi3zhwWCgw8NDvX37Vi9fvlSWZTFvBCDxUugzYwHnvLm5GYuVBdxut7W9va1msxnokTRX8p1ZPN7P4XAYO5RpK2jeUZ274Wz9d4WPN4nSgqIBEdOn8/PzuCfcOIiObBmnO9LCZ5VKJZAgcp4qWvc4oebwFs/Pz0PRViqL+vRf//rX9dFHH6nT6eh3fud3NJlM9GM/9mN6+fJlPIP+uReJUXPw5fEk5NQrvBKTQh6ReTwPaVluxON2Z2dnoRcwnKwv5oB5mU6nOjk5eYf2ZF2i2F3O2JSGJ+1IH6PrYwBgAwg4a+DeMH+Tftput9VutwvUIs9Dt3Ev5pK4CnL1kOtRKHupfJds+pqjfI9ap++zeJkAhABB9UCMZw3wPVe8KDoEx1E+6CjNyHDL60EVsj9ABWQzOMpHcKhfgpKFl2fnIKiTwmgsCPrpue1eiwbvYTwex85J+gAPmI6FC1W1WlWz2YzMj0ajEciXBY8icpQ1GAwKrjO0CUodw7GysqLhcChJkZoJVcW48ZnhcKgnT57oxYsXBS6aTJksy0Kp7+7uajgc6jvf+U7sa0i9F1f8KADus7KyolarFXVnoHPcKCMjnj3B/KZxHxScAwUHJIwdMotSwijgvflmJHaWouydPkA2vc94MSgXUOve3p5++qd/Wufn5/rss8/04z/+4+9Ut6Q/UFdu7DDMTmMBRiiUBg9dq9WiNAT9R26lpeJnDZPVk6YiuweLEfWic9SS8RgWXoj3hZiFx+cIsDLfeJcYEOge1hL0H3oC2q1arUYlVj93gPRZ9IyzDIwTY4tckCzykOvRKPv0YvJQbM7bM2EIcxkF5NRCijaZHFeGKDhHJrTDF4MjEybB+UuMAQvWOV23xm6o3AWGqmg0GnGMGUKwtbUV1BP9QrE6MkJBev/IMYePPj8/DwTLgvGYiFNVIDP61Gw2C/sE3CtjnMgDpyY9C4uNS9Ags9lMOzs7walKC4QN4p1Op+GNMH6TyaJW/Zs3b9Tv97W6uqonT54E780OWRY5bZYUVBr8tmca5XkeR0kyl75TEU9BUniFeEgoPKdiHIjQZ08pxUASdPNsDrJDXGGiZPCmnMoErbNGXHlCC3rgHO/SQRNtub6+Vrfb1U/+5E/q448/1osXL95Zcx5DYw06dZmuHzd0ZHyRwXJ6elowTJ6thUdDTZ5Go1HYQ1GpVIKi8mB1qqBRynjqBDmhkobDoW5ubuJAoZubm9j5jDyx/wXvj/sTeJUUFCBlqvv9fiB58v+5F3EJvzzeg5wCHqB0eK8MIL/vejTK3pV1GaIv+7/MADif6Fw6CxHFiSJBIaLsPRjDM9zYuFvlJVHZgLG1taV2ux3cPtkmoF6UtQeJCNSA1qFlyFZxa05GgNd8B8GyeN1DQLEPBgONRqMCwseA+IlZjBd9c4PJ5ajTDYUrGmkRVO33+xFYa7fb6nQ6mkwmOj4+Vr/fV6VSUbfbLSzK2Wym4+PjULTUqWm32yHo9I/j/Dhaj3pBGKerqyvt7e1FgPb58+d68uRJQV4wMlANKGy8FGmZeeGI03fQgiQxuu4RubdHqWrG3917TjXCeHsg3JEz+wZQXswNCoHn0C/3LqF9XJm4N7OyshJ9fPbsWWwqAmGzblgXUG3cG2AAwqdOEc/CAFF2Yj6fB4g5OzsL4ORtcoNESimF3KB7GCNoGsaUYymdHsNLw2vCWLKpCqCzsbERqawYH+iUfr8fc3F5eanhcKjNzc2gWqH+8Fgx6NyvUqlEf5EVxhXAATBAZ6H0AapprOHLrkej7KV3jyZ0Revvu7L3z6XoI708owGqwwssebDR+bj0HihYFjuChiGgFMDOzk6kkjmvyLM8p1rSO8/iArF4GinCwQ8ogdQ8kCtVJuGPO51OIEiQI4aLsUMZOC3mY5RmjbCA+QyoMeU/19fXdXBwEIi81Wrpww8/DC7Uzyy4ubkJxDYcDuPMXsYYJT+fz6N2ix/u7umbbGSp1Wra3d0N44tB4Ls+P2m/fJPZfD6PmAhyhBLybBnmyuupuBfBHPrGJpTKxcVFtM/pQygg5I+4Qp7nUQIZOUVeUGpra2sRFEbpIxe0iRRIkDHji1fBvaSloXAUjxwzNg6UeAZeHZ8H0NBWT/0l5oOix8Dg7UnLnba+flP0i2eEt+fBYKgfuHPGEQ+3Xq9HvAsaeGdnJ1C3GwyXIR9L0q/5TKPRKMQ6/CAUSbGOAakYbPr00OCs9IiUvQdgy97jSl3HFNmXXc6ht9ttffDBB5pOp/r8888LAuLI3t3sVGC5mGAEB/TEa91uNybIXVmnnaACPA0QxUSQDhTkaWYgRBQMaIn0QzyWlZUVNZvNoF4kBRXAAoJeAK27wvNFgRFIKamUk6Z/KH8U7/n5uX7wgx/o9evXgbLIkqAoFsrl+Pg4shQkBW3B80ajUXgroPevfvWrwalLC+N4enoaitDLM7AYWUweGPMgJH2EemCukCuC4JeXl9Ee39lKATPmkINTiA+g1DA2jJln9ThCz7IsDIRvQmKuMIIoc+aBvnAeKjTGysqKzs/Pw/DTRmg1xoF14FlcrBFk3wOQzrP7ugYscPQk3hzBcSiPTqej/f199fv9OJ0NFIwR4G/fZIT8epYU38UD9jLQjDlK1uNZ/X4/1g5eOc/kWXmeR7lnZD2NIzoNB7hqt9vvjB8BeM++Y+4xqhh56N2HXI9K2Zf9fR9eKhWm9HsolhcvXuijjz7S7u6uXr16pf39/dh955wmg+o8pN8bZZAGwlB0BNJwC5vNpjqdTnwHRIagkjkwm82iVC2CwessYGgcuHBSCRFC5/JbrVa43rikvsWd2ADtdAFOx9HHYT5fZpf4ZzwzAvoI1IqyHA6HIcyffvqpDg8PI47gc0Z1P989WavV4ki+g4ODQF6eYQWyRwm7J5Dy6CgjKJHUk8EQOJ3jG9Z4HWqj2+0WYid5vgjCnp2dhdGZzWY6OzsLIwVt4GAHuUBp+pjjLeGRdrvdONAEZY1yYS6csvQ+oVj6/X54ArQfKpKyG6enpzHnXo6Ytnmig6f80meUYeoNUPtmMBiEJ0UZB3YAX15eqtPpxH23trbiuXDi9J/+eZ0eaBXmkBgLa4v1iIebZcsdzihwqFanEUHia2trheQGkDgUHxk8eGoeI1ldXY2YAAo83ThHppRnj/kGrPtej1LZ++UBH0fYLHD/nCtrJr1WW2wjf/r0qV6+fBk1p5vNpnZ3d3V2dhYua5nB8aATlyPz9O88X+RPHx0daTKZqNFoaG9vLxQ6bZWWlfaYODZoSctql2R5IGxkUDiKhE6Ch2QxOyfsHDLPZiEQvHYk64rb2+wIPk1rdQ/JC0dJCqOGJ4Jbe3p6GugFb4HF5uM9mUx0dHSkarWqVqulXq+n9fX1ONkJw0XbMXBeIRMUJyn6DMfsfLy3xWkz2uYKy4ORZJs4Lz6bzbS9va3d3d3Y/bm5uRl5/8QS8MY8TVZaxkeYFzhwgvDX19fq9XoR43FlV61Ww2BC9+D9YQihoQhQYoB4HhlgFIijnxgO/w1QQWYwPh5UdNrEaUxiM9CiR0dHevPmjcbjcWTUeBAUWopEBOJbyKpXjfX0VFA68+oB01arpeFwWNjrQB8wWg742A1OvMm9Wu5BijdB5mq1GjEb5ov9O8g68guFtLW1pefPn2t/fz/AEd9/yPWolH1KlaTcPH/74nWU7ehFWqAG6nqTCw7a/OKLLzSZTMJiUvrVFSXBGF73tjo6xiK7gQH9+CIlmAOH6y52mo+LEmZho/AdHfmxhCxcSQU0KClQjtMvKHs8CJCdK28WgyMJ0JG0VJjpQsBLcF4dCoA5cmPi1IWPubSMU7x9+1YXFxfa29vTRx99pL29vaBlQFLQEz6PKBv33giC0TbPkEnRsdNtvr+BPvsW+VSxOXKENri+vlaj0VC9XtfOzk5w/QcHBxHbcJToY8HcQwNhJCaTiba2tgKFVyqVyD7yQK8bY/dkMVAoRRQf4IJzZ1HQ9NXTOFF+tNFBUlonnzmDuwd1Q994thFF687OzgrtwnN1qgmFS1osnpdnN3kbMBJuBMgEQ+6d7gFMQH1eXV29k1rptf69kGCtVosNdHixrid8vXpaJudNM3/S0rt8KG9/L2WfZdlnkkaSZpKmeZ7/G1mWbUn6+5JeSvpM0l/L87yfLVbHr0r6GUkXkn4+z/Pfu+dzCgq/jI9PEaojfBQKNMiHH34YhZ8QxpubG/X7fR0dHUURKIQApNjtdkMJQjGgJAkgkQnS7Xb15MmTWECe0w+/7bQCyBwlgeCDtqQiekah0z4i9SBUXFuQ23Q6LeRC855H/fmuVNxyzji64YDjnM/nsVNTWmY/eJwBZcnczefzQM24rBg84g0+x4wH9+d/dgOvrKzEcYFuiDCaICna7lRMaowJYvNZH0OMlWeduJJ06o4ibMiYo343fvTP6QLy5bvdrra3twtBUYyox1/I367X6/riiy/09u3byLC6urrSs2fP4qQrkL4rYB8H5hylw/rhwpA4Sq/X6+EBeKqnx7jcsGMs3VNAvlH0V1dXOjg40M3NTcztwcGBZrNZBEHxRtiPMZvNwkvy1FjaBKcPVw+oobiZgwz6T0mNfr9feMbKykrsACebBgNOf0H43Jf/SY5wxc7ces16L9LG+GComs2mRqOR9vf3CwYIcPuQ6yHI/t/K8/zY/v9lSf80z/NfybLsl2///yVJf1nSx7c/f0HSr93+fu/lylFSYXGw2BwdlFE5XCsriyPCPvnkE21ubka1PGnh4vd6vVCkLLBarRbZMyAkzwiRFMLlSNlTMX2y+Jssk/Pz81CGBOYIEnr+PkqABUJ2BYYAjpdFzudxtaEVQCd+L9+WDi3EwueztAEkAvLAWGH4yJCA0xwOh4GCfOwQWtxZttyjoKBZUEa44Li1BLtB7p5VgQIB0fmBLXggKB3mB3mCcvBNNaTfebnL+OoAACAASURBVLaWo3hpebAMirjdbmtnZ6fAB2P04crpKwbG+XkUOCdQsRZ8DweeEO/NZrOgsgaDgcbjscbjsV69eqWdnZ1Q8pSaQAmydpxb5jViI3gf/X4/jB1z4ug93RB4V2aOr11krd/vxxnB4/E4DitBOSPDrG/oMS9CRkac64xmsxltI9DK/ZBTeHTPkMKQ5nke5ZvPzs5C5vG4UOyrq6tRi4r5IS2SGk54Nqxr95DRGQSqvXImF1To4eFh7FMBbDE36Ib7Xv8qNM43JP3U7d+/Lum3tVD235D0G/li1v9ZlmXdLMue5nm+/76budvti8GVvVQ88Bn0Iy2pFdDfj/zIj6jdbse2ZNA0nCCKByvrpVadBgANeBpUu90O/hM329EiE0p6FQoA5e6BPV5HIbqgZ9kyXxrFQcSerBsEiw1FKC3neX3nIsqbz87n8wiQSVK32w1PAGEF6aZUDQscDt4pL9AJAt3pdAq7eldXV2NHqtM4jDuHkIDMCMB6XITv0F/QnwfNoE04stFpEBS0c+bO8TqFg3LBwGFA2MmLUUVOONTcZYLF7MjOi965gpSWBt4pS2SEmMSTJ080Go10cnKig4MDvX79Wpubm0FXEceBZ3fKjf+hCwlkY3yRcfpK+1LPwA0Yr/nYcT8U43A41NHRUdBi1KxxOSSWkWYiMR5QP+5Z0A7mir6i6Pm8r3HSdEmr9PIJvradisMbgxJeWVlRr9eL2ku0hb03eZ6H8SCYzvwix67oGXs38swffSOr6yHXfT+dS/rfsyzLJf0PeZ5/S9IeCjzP8/0sy3ZvP/tc0iv77uvb1wrKPsuyb0r6JpPnaVrOrTpHivJP3Ue/2u22Xr58qV6vFwiV4BGoFM62Wq3GoPJsFq1TGygFhIH33H3jcoXswkqaGZuq6Au5w/DKcI2dTif4PdAW3xkMBoVAG/QDSgAEBHLF4KDYyUxBmOEWaQMoj81MKFmUNf0BIVLEi3ayYHwRgm76/X7sXHRviHFyDyzP80KNGObcM0m4P69haJERFA2uMsrA4x6gN/dkUHz1er2gsLhAeRg9xpGdlZVKJdILHYXiVflC9uwsNssh55IiUOcBOUp1DAYDzWaLekBkdtRqtchSef78eewEdc8RhUz+uMeFkGtJMZ8gVC997F6orxnu74geOrBSqYRSZLMd40BWEMib7+IZQWuk1I1vFuN4StaDfxfwRTtZ+xgxaBv3cJl3vG88RfQKfRgOh3FOAhQPMs864d7EZfA0MSToGMbDkwlms1nMNZ6vxzLvc91X2f/FPM/f3Cr0/yPLsu++57NlLXgn1ebWYHxLklZWVnIGwd3Bss4gAAyIK3xQpKcZgtRQAihUXDzfas3ge8ASdwrXDVeNyXQ3H2WI4vEsn5OTkzg2EKRNP2iLF29jgXF/ULgHGumzb+ySlsWjKpXlZiWUP328ulqcoYnxA2WT8eDoFVeT57JwoQvcTaa9LAbKFUARUEvHN67wWfoGWr+4uAiEnMYaXNE6wuE5LFavxYIH4LLU7/dj0fsRfNISeXmcAo+I9D34YLwN5Ix0VklR1wiDKS1LEiOTZJycn5/r7OxMV1dXEfyH/+VzBPo8AA+KJDj55s0bHR4eqlqtand3gcMw6NBQGBR+MDbsOZjP54X68ShRKBzWXbW63BSEvHNP91YoU3B4eBh0B3PEmPo4E7wGDAESoFKZK3QHCtnLhiCbeBQYYuIsHrBHN6ADWEser2H3N8YK+cAAIh8YDHh+5on3Wq1W6AuMApl4UJU8F2VP+/I8j/pRD7nupezzPH9z+/swy7J/JOnPSzqAnsmy7Kmkw9uPv5b0gX39haQ377u/K1hH8a7w/bcr2ujILW3Cbrd+vx/1pRFwFJLniEvLWjAoJzI1PD9cUuxIZSFyT4qTgRDpB0LHJFF3ntouHhykDSz+4XAYVt936XlaoVQ83CFdvChSd8MJFDktRUE1z/P1mATuN58F1dP33d3dAt+NEfXNYp7p43QAHg99gV4B0bHYnJ5ywwxl4t4NRlpSoECMmme2oHxdmSNfnh7q2VmSCruS8R4BKvTTjb+kKOGMQsPz4BBwR3rIAXsLUASVSiWMAvO/tbUVMu2xikajoePj41g3ZK54QBHUiqFFhlH42W120+HhoY6OjrS3txdyz/rz8WTNOmBjLt2TIMiLDHgMAYWJoWVOeRa1+fHc+Y6fV4Ani0Fm0xW6AFkhYwn9gyHFUwAwzOfLs2OZN+frnabCE0I30E82qfm6w8NzEMP4odPwCnmWB+sfen2pss+yrCGpkuf56Pbvf0fSfynptyT9nKRfuf39m7df+S1JfzPLsm9rEZg9+zK+nk6mtEyaleP/g+YIcOLeTSYTvX37NgJe0CYMGovbrSZ0CkE7z6hhIjAEvluVxY5SQ/A8t5zJ9BxsECACQvs93xaFTO4xtMrV1VXwgGQdpJkxIB9QGYsZdIkCZexubm50cnKiwWCgPM8jt5qMBMaducG4cS/G1WkOFgXjiYLHCOG1cIoSAShHzrjMUvEwGIwEf7PQiV1gYFZXV4MS8cMxnGdmrB1d0SdHuOfn56EQMBwsOGr88Drt9OAs7WWH6LNnz7S2tqbPPvssng9yBhg4FeWKtVqtBoWHvHD4NBTa1tZWnO41m820t7cXdA7eJ6AGjwZZOTk50Xg8jrEkOHp4eKi9vT01Go3guVmP7nnxN/Pu3inrzFN4kVmMJt9njqBDsyyLZADGgQA/dWZQ3JzQJSn6wbzgATgvLy2BAXqE/nlygseNmBuoJ2QCsMX7bhiofItspCAEQ8l64jdj4+OTUthfdt0H2e9J+ke3A1WT9L/mef5Psiz7vyX9gyzLflHS55J+9vbz/1iLtMs/1iL18he+7AEorlTZe6CyDM1DrTCZbNqZzWaB9pyHx6JyOa+G5QZVuKDyWUmhkFFsCJFz/JJiUllE7l5iZHwCUewICeeKMuGO6r0/cJuz2Sw8DoQdBcV3nSqjzCqpkBgUhBMFBYpxNJ6OHQaU1xgTlC2Kl9RXhJ5249JSBpnxZR8EFwuZMfGYCAaVtjEexE86nU6UggaNgXAZRwpsIVt4Ed4vEC9IGo8mVR7MD8qe9vMzHo81HA51eXlZqGnjcQPGEPlsNBrqdDqq1+tRKRI6wQ0tlMbHH3+s7373u3rz5o3m87k++OCDyDuHEul2u4XNPXgOlLEgfkNMwOlGnpWuLZSdG3mA0PHxsa6vr9XpdKKwndOXgKr0/ugCqElKaayvr0ewkjni2U7v0Y5araZWq1XwxldWViLAzTpgLllTjC+GjqA8MgII5AJ8eLaXU1xpuitepit4dAGfw6Pi/f/flX2e559K+nMlr59I+kslr+eS/uOHNAKFyN+pwvfXWZzQNrhikgIdQ7uAIrrdbgiSZxHAtdVqi+qPbN12C+41o3FHJRUUBcqT5zmP7sEeD8g51+vtwdoTmPWyCVye4wsn6FQNi+rm5qZQ+AweNPUy2C8AOnGe0QNgPAcembFBUZDVxAaYRqNRyG2mfy7MeBy+AxT3e2trS6urq4Xt5rjKzqOy4Hnf6QM8CK89wzPcuJ+enmplZUXb29sFztYpMknRJ4Ka8Mp4URhQN0h4Xs61f//73w8qgSBwt9uNsXGum/8J0nnVTMYDGZOW1SXX1tb0/PlzHRwc6O3bt9ra2tLe3p4qlUrUf8GAIIMe2/H+E/uCuvNMMo/TAGDw4Dz+QfvxQvg+ypNAJePmAMK9GzziNFvLN1rN5/Oo949XT5DbX2Ndjsfj8NBI4eUzfjH2Tuega5hbR97Mk9NayBcxQI8LkUjgqcIugx4/qVarcZDLfa5HsYMW4eBKB9gpHga43W5HWpm0SBmkFgxbnnHXUdJMtJ9BigL2icLKkpKH24yiQ6Gw2I6PjwtInHvjYroVd37ajZwHFH2LfLpdnrFyvhoE7hRIykM7Lwv3y+Jzg8WY+ULwTBbGE5TumTc3NzeFWjUeNCY4CAJmnkCYZBl4sJn7wqUyjxhaELpTKFBPeBLUDQeJMxe+qQVk6Ma1UqnEd0kVvLq6ippEjA/eBWmMuPy+pR36DHnydsAps8EMIMNc00coJcbBjTFGk/iOe8itVkutVkuvXr3SwcGBer2eut2u1tbWNBgMdHFxEevIqzJCBfoh4QQXUV6eooqy9b0FyBUyCxWCgt7f3w+jhLdEv7nq9XqsOfemPDeddeIlilm/zn/ztxtQqBrWIEDLg+usTc+O8f9dp3hlTdpP39BjUEW7u7uFw9Lh+gEnlFmQFGsRueY+D7kehbKXiqiZy/8HPTBQ7rqR+8o268lkEmlQ3JcsGBYobhhoaDabxQEfTol0Op1AU56PTK48LhlpehcXF1Hsi2ANKB63kKwUFKW7wp7VAIJw7wBUD6XgxgTDRzlgnk9/QCEeJPJsgDxf5q87fQbFgOFBCflCxfUmf573WYzu1TgiB7HyA39LUM2RJShZWta/oWbL8fFx0BCME8Xn8LhY4J4dhBHa3NyMMXQagoVIcO3w8DDQO16gu+oEIKEFoX1QdIwtdc/dwNIHr3N0enoaBpa+pzEswI+XvkDeT09P1e129Sd/8id69eqVOp2Odnd31ev1lGVZ1FfCG8ErhodGSXs5Bj8IB4TJGqY9eFS+z4Cia6PRKNYxxg4Ei8KnRABzimwRy3KvwjdbsW4JwiKzjBsehtOyng7tBflYr5eXl7FWoYzX19ejLb7nBhBIn9zoScUqsqenp4XCgLVaLTaGbWxs6Pnz53r9+rVOTk5CRmgn8vmQ61Epey5HC/zPb9IS4bBAXwR/oAr8aDvn7XGlnG/b2NhQs9ksoHznqBl80gYxICAoJqharQZ/Ky1LEaBYyJd2jh/uEMuO6wYdQxtYOPQ7zSbiAnXwXfpObSAPUhOExLUExeDiI+CgabKCnOZxY4Ny9AAY7cZIkLYJn8kC8aApSohUOdAkC43sB/eeqGnC3HkAEMPY7XYjnkGA0mkHFh0ZV7jIKEDmDRmDWgLdI1/MtXtyzskji240PAvJaQDP9GAMs2x5wAVGeTpd7O+gINzq6mrssobiGg6H+vTTT/Xy5Us9f/48gpBHR0fB3XMS1Gg0KgQuJ5OJtre3tbq6Gt4NyJl2OXCh/ePxuJD+jNHFaLiiBfkCRNJMJGmZk49xgHZi7om9wL3779lsFlVu+d+V7+bmZlTa5L4oYRQrbcY7YB2z5j2WhAwyf053TafLw0yQT8aoUqloNBrp1atXkamFEXIv9ofy8BKnJqQid+gBnvX1dfV6vUA9UCedTifoCwzCy5cvQ0hAXF4MTVqieSbNUwpRdGnAESWMssQTSLM82LQ1m80KWTDcAyHB/abvzk06vSIpEAZ0FQrfeXDGCbcXZI0R8NiB1wRHUaNcOYLPU+cQWrwBaCOUo6e3sgGGvniBMsaYIDZKA4OS8puOyPCScNuRH8+Y2NjYiBPDpGVNeVJDGXtSdT2lFjlC+TOvGGG20pOixw5gLjfufIa4EIuURZtlWdAsBPEpfUCKIXWQnLdH0WH48AiZd7w0R8nr64uToE5OTvS9731PL168CGPBlvzd3d2InwyHQ52cnMR4DgaDOPPXKRKQOQCKOBAyRvok1CBrDeTr6Nt/UhDgFKt7myhI5II1BSXqn2ONMB4gcgLiXvvG6Vjm3tcKfZIWAKvb7YZ3S/s9X971jHu2PMPlgn1C7NBFrngWngvfv+/1KJS9VAzMupuKwq/Vatre3taTJ08kLSePwAiLCmvKqUbOBzLZUAV4A6QXOp0yny9yeqETcNcmk0lhAUrFI+t8Gz/PlpboHEEHUcKZeo0Yp248yENmCq8jpB5IQglICsWBC5y66vCKIHpHlU7DeHYLC5DPooiZC6iLSmWRDseY80zKFmDIuBeLEIoI5ELbQDgsJrw3R4cEhZvNpra3t7W2thZ9dCNDuiLZJihIZMgpHPqGt0BMAINP2igyRv0VPCbQJHQRShElcnZ2Fh4dKZOOKPntuefQNh5bgbZh/SDTyAp0y83NjX7wgx9od3dXH374YcwfG7k45xUvjjG4uLjQwcGBpOWh4lLxUA3WEMbAaRx+4KgxXvQROfD4lAMidANyyZzyfQCdJy8APLhAyB7/Y23yHYwDewHShA6/J/3AkAMOOGYTI+jeCGuU9Qfl6LWTvB0OFDzoixw95HpUyl5aHjHIa66E4dAc7UvL81xXVlYiLx2EiiVGefvWZISKBcSEw6OR9ga6px1whEyEu80oKi+IhbCygEBeztPzPu26ubkJXpfsAegY5xRB4Sgr7otxYEcl6Bclg2DhUvI+446Sk5aLGwUMGmfMcd/djff67AgpgT3STCeTSZSN9cAf1Brfc0NNDXPKEoDEiaWgYN++fRvvM67IEnVK6vV6HJLiNCIIzxE9Z4oihx7wxwMCKUsLRdVut7WxsaGDg4NQMlAl3JvAL/EhUhK5PA0RQwXXjdLCyHjqLd4pRr7dbuvk5CTWwR/8wR9oMpnErtvRaKSDgwNdXl7GgSjtdjuMFmvAEx6QS+IMyDIeDes1z/Oo3wO96l6bK01XsCkAcWCArHvmm88X40Xcxz1cgBIGEC+Y+zmQ4XIQ5WVXpGXlWW+jtDz+FEUOHUQ7MdDIORefY355lqey5vnDd9E+GmWfBme5GITpdBqLdH19PdIsQaGkTJGxgDAMBoMYXASXUgG4zqRresoT7pSnR9JOn3QUDRcbVdxdQ8AQLFwzz17gNdAZG2bgdHkm+dagfBR1WuNnOl2WeYWDT7MnPKsA1Oi781iQGB84a5SJxwbwqKBSEEyehfLBUBCk9AXMGIDkUSTIBodFM7+85x4T403hO9A+//M5r16K10Gw15Ur9z4/Pw/lyZg7SpdU4OoZbzJeWNyMCZ4iyhPAAcfOTnBXHNPpNA5qwUsgA4XUX9pFthTzyUY5FOXbt29VrVb1ta99TZ1OJ/L1j4+PIyWUIoKkhHL0IkoKzt+DtAR8XSlfX19rf38/uHDWkRtG92JRtIAhR+ooQJ7HXFED3hG7X3jzrBkMPN4H/5d5VB4XoQ0pQJKWMSsCqtKy3g9ABcBHIkOv19NoNNLx8XF4j8S13CvhWbTNM37uez0aZe8W664LAeDHLT9/832OM0PZs/kKVOgHELDwmVgmGTTvbiITzyJ0NOOurbua3n7nG7H20rsnR11fX+v4+Fi9Xq+QagiipuATtIIHd2mvtNyJi/vLYvJ0SUlxgDOcf+pCYyQ8Tx+aCgXqdAecqReg4zkEpBi/m5vF2QBs6PHMHFIY4WQ9myL1AqEcJBWoD4J2BJ3r9XqhtCx9wBshS4v54HXGmnF2SgBFzzz72DMfDhI87pPWZaHglXO9ABHPAuNe0BcoLQzLxcVFKG8PpKI43759q42NDT179iwMKWf7kk10cnKinZ0dtdvtCJr3ej31ej1dXV3p+Pg4MtHyPI9xZr4pYeB1X3ydSioobufBU2rXvWFAF/Sp388z5gCHyDKygtwhN4wvlBXKHgDA/QETfH86nWowGASQpI3Sckeuywy6ir0VBwcHsUZoC/LBXCPTrifox0OuR6Hs3cp7B3yiQRmkCfqJTwxgt9stuFPSMvgB+oXvRbh95yxCAEXgtIovRgI/ngbo0XdH9J7+KRWj9hgUgjUYGkeQKGYoHK/d4UFJBIXxQVBABB4/cOQqLRUjQs24e0YSAkZgGJqKRYJHw3h6/AEkhRews7MTyB9OE++CYJ2ntTHuIB0CzShMPDo3TMgNytBT6ugTiJ4du9AclETAkKPoMRR4gvTN59G5XebY5YK+uPFmLEDmWZbp+Pi4cMwkc0RA9/DwUIeHhwWemn0O7uEwbniheCv0n3INGHinDoi1vHq1KGLLeiN7jSJ77JQGXDmnfH19HcYLwMBYIX9p6iayTDtdJnkfRc36cxoROXP9AM2EXCPTpM1yX4wH//s6dJTvtC5z68kgvjZYY16tk/UEEGGeWd+MkcueJ4HQpodcj0LZS8sJkN49d1ZacmZesN+34sNl4nLjOntqoE+a5/jyHkqRKDgo9/z8PGIBGAui4e12O3ZR+rM8lx3BYrcqyJ3FCAp2HtMVFXwpSBVrzwLGmHj2jFTcto4S9WAZvDWK1ZW7owl48clkeTYnSJuYANvXGUu4ZBS9KwroCRQTRsapOjcS8/lc29vbqtVqOj4+LmyYY+FxmATZUWQDoejg1B2huWFYWVnsnoVfdi+NtnCEHrEFlDQKg/FxZeDeodMRnu3lnhiZYqA/gr2gQ09DpfSAZ8cw7nwGGfZ8b/dKx+OxXr9+XUClrBsACMAKdD4ajVSv1yN2RRyK3dPIITQqMspYpPwz8u0b55w6AcSwRjzLCX3Bd3gGsohydbqF8XZP0D12Hx82HXp7pSVA4r4eh2PN0m7Ai7T0OiaTSVCMjtbRDcQQkDO8cuTVY2r3vR6Fsnfl7pcHM1CSVI/zwSPt6fDwMM6U5X2Cp7Xa8rxHkLFzmxiM+XxeONuU7yNEkiIizyRhlNyNRgDxEHC9PYjnwuMcMFkt/I+yAUWC1jx+AEpFwYCInKridRajZwK4ckahsDHMkb4LsKSIB9BPd98d0a+vrwdVA1ePcQPZ81woNgwqz/ZUQ8ZCUuTEk6vMXEHR0CcQE4YU5QLvvLW1pQ8//FCvXr3SF198UQAYgAw22bjRpC0sTkfiyItnk1xcXISn6uCFOAXj5Ts53SBigChQRruohU/Aks1LrDHkCePDhfJ0moGgJXIE9XV+fq7Xr19H6WGnXqCAWLvX19dhvJg790D5gW/nXk7z0G5pWbKYWEVKrzj1AYgi7sYubKfDJEV2EHLhe1Bc8SObXMTaaLMrZtrpa9c9BqekSNNlTKBj8bKgkhyEepsecj0KZS8VT7fx7AaCVUwYtVJwt+GtqXXCRMHRe7YJgzafz2PTCIOLor+5uQnh8wDUxsZGBFuw6FTWo928zqQhQE7VeP69o0s2nMBlc7k3Ampw145gDs9nHFwZkk7I/07peE657/jFSIDeWHAexHIFhFH2DCmUBTtC8RDo43A4jBRYD866EWBhojSgGJh736mY0mie++/BTkmx8Bg3SXrz5k2MCQFRMk1Aql6xlMvnGwPnLryjesYF7/Pp06ehGCUVdkV7xhBj3O/3dXl5qZ2dHW1sbATiZ3cp3iCZZPzP91Pagrl1CgRFz/yurKwE7YkRGY/HMdekwE6n0yhE6EqO9YRyTymcPM8LZSrcoAH0XFnimYOCeY6vN2Io6ARk0b1aLl+HTimnnoiDO9ZPSj9j7P1v6ne554un6HthXL4xjNBzznI8lKsPOf9TfevP8MIt3tzcjFx5rLYHJrCEHsTa2toqUBqeoYLFZWJR4uR9S4qzJuHVKRbmaNWzUUBIoHXKLBMld/qIiUMBMaE8G7eUXcCdTieMDc/3DSosEOfdUeIIMpkanonBokL5OYomQEpRLxasB7hArFBKBLfxDkD4TpmgJFB2BwcH7ywI52CdW6cNjDvGGgXiSIccd8+iQXl4xgWeniMxNlVhgPDmPFiM0mS8kTWQNHLmniWghblCgRwcHIQC4lnMDePNXDsnfXl5qdevX0ta7sr1eIpUjLHgubCuUIz89rF2ZQeyxYgzNwRi8SCn02mhJo2jVJ7PPPr9HRHTHo93sF64B697cgSepBty59TZOZzG9lyXYBicLsGAgMABI77PBMOSeun0iTVC3/w5rH33CJw64rkOsqCs+dwPLY2TXlAqLGY/AYhTf7IsK6C9m5tFHW4G2bNTSI30oC0o34Mx/HYXEwWLgmESmFAQKMpDUuF5TqsgsASGPOiIAQNNYYxQXkTs3TV0mkNSGBLfGVupVAq5+VBQLnSu2Jw3ZS4wemmwDPQJKiSlcmdnR5J0cHAQiM4pCQKXZG+kfC3jlsZVoGXcaNJOFi/lLAh2Mp6kIYIU2+12gIharRYxHuaN2AQK3o0rAX7myWMazD3jBQoFeWOkMDCnp6dR1E9alsZ22pFsIZT/zc2N3rx5U/Ck3HtwThrvwGV/Y2ND5+fnhX0Z0HUu3xietIIr7XGFD3qm7WnQGtnz4D0G3msLISN4N24A6CMX8ogM8mw8FeaBe/J9FD5UC3sooHR8zFnPKFdkwwO0tIE+whhsbGyEkWDsUyXOxVpNPX4oNGIibmgeivAfhbL3qDtKYDgcajweRz0ZAlagF2mJrhgkXB/f4OL8ORNIDjQLE6FnwV5cXEShKDhFH3yQjAdOoR7I+kDg/LQdp3Q8Gu/uJveDmkAIfBER4EHAyL7A8HCcnweDUT7pTk4v5wqV4+OLIi5D3Sx0xpkFcXBwEAqX4KmkQkDNKSZHNR70gpd2Xv/q6iq47m63q6Ojo6Ac4HF9+70vWBYYfed/EDLy4/ESnkssgHkEFEAx8RnQZJZlEcxFGZDvP5vNouIh/aES5dnZ2TtVWRkj4jRQbSkVhcxjWBhDR5G+twO0iQfpQWeXYZ+PNNjswWiMqmeDMd+AD57rRsmVsNMZyIOjX/53ncHaBiT63Lkn51QQ/YHT53XkxTl75Am5pQ3IPv1jfN17cuPkihzwQ9uc/qQN6Afk38eV/jDP97kejbJPeV8omFarpe3t7VD4LGDQIXy7pIhiO0JDIBA8Alm49SiONPuDSLm76yA7XFba7ht0QM1eqwQeXVJBgSA0UASOalHOqcAQVAUNuUvoFAOfmUyW5R7wTjAu0nKfAIaFefC6IiAeanL4AqUOTbVa1du3b3VxcaHT09MCSkN5efscQaHgfVcvNdb9MyB9PJezs7MCEqStKDz6iWKGbnIDOx6Po9YNY01hO56PsuUZBCspLe2xJg/YO9XIogRROp87nS4qXrphY64AJIw5ciIpgrjT6bLeC88j0J/uOGd9dbvdML685gFJnuH0iV+u4HyducymdBBj6UYkpY1S+ia9F9+h3ciq0yieYZfneaSKqJa2zQAAIABJREFU8lqlUlGn0ylkzRDncBpJWihoj8sBjOg3AMFRP2CMw9/9s64n8Co9PuH6hvF1T9zH04Hvfa5Hoez9wk1msZE6Bzp01w3h9UArgQ84OyYbV90VJIFe531x/U9OTiSpQLUgUK6cPA6AoHgqnrQMiOKag1gpOHV6ehqei+cD0x5pqSiZaLILcPPwPli4KHpOQ0L5ubGkT1AcHnSUlhk+lUqloEhYICgHxgdFCuLl3myyQVj9OcQPMDQYXbwODy6j3EajUYEPZY7r9boGg0EEvqBbymIAIDZ21noQjoqO7jkx79A7ZFe5N8rlaXPknxNkxFMk6Iqyxxgjcx4v8B2qvO6bp+gnc+AB6TRmRdAZ5YPxYces0xKeAogSdorBL2SB+6KUed2zVGq1WqQc0ja8bdC7Z7ug6DxtEcNPnj+yAmom9gU94zIIreVpxHg27n0AiDwm4LqHeUp3pkvLA4rcU0BHMC8+Jk7N+tpxyhgZclD8kOtRKHu37qkLT/aK70QE/aF8fODg9d29kpYpb5JiJ2attkjzQ3kQJ0Ahwa1LxQi45zXTDs+rl4obN5hMFK67wSADd3Wd13OhZhGD5Jye8o1FjlJpnyMVDJwLLpkUzmWSBw6yY3Gg/G9ulpX53L2l7alicM4f5eGoBw9tMpno7Oys4H6jBBh/0BkLlfsjB1AT1EBh8UBVsLPaPYtOp6OLi4sYB+eXpWX64GSyOC8BWXGDxdg5TzwajXR+fh5Ggvui8ByEeDyEILdnrzB/VP1kvnyfhPPEKB1Hn8gIFAHxB0f6pB+7t+LzmLbX1zAZTFmWRWBxNptFbIe2YMRTL8IVJvPvvLjHAOivl0tANqUlSAJAMofOe7O2UnTtVB3yxxwCGPgMXi/tSuktAJh7eVmWxY5yPgvIAUQyzrzuBvGh16NQ9lwIAYPHluLxeBxuGBYWFObcb8r3evaGtExrY8LJfgHFsfid5gARIKhZlsVGJCZcKm4Kk1QwAnDmbsl5j5Q2BAKXEmMHRdRsNqNCJ23D2vt4sAAoa8B9nftEoD3dEVoKlxXDAyXE/4wH5aIxrtwX4+txCQ9GOU+LsmE+VlYW5ZvZls8i9rRP+u3tdg+LZ+Kh8T/BfEdoGDAWJBvDvI08x+MobnSkYvE+PsvrLGJkrVpdVEUkQNzv9ws7KDFMoHfPIAKATKfTQpVUaE/a60bWA5U+/igw/66PL+hWWtIhHqTlswAC5ANZRDagwJzWw+vFmNPHlNpB2TOPjoxB9G6MK5XlyVOgYPrvHL+norLefK5poyt7fjsg43NOpyCDfh+n+miLe19lMu60NnPCTm/Og3jo9SiU/V1BH95DwBE4ELgXXDo/Pw804gLj6FUq5q3DOfNMJsrv0el0gvJBcD1DAUHzyorS8lxW0q2YOBQyE+5pkU5voAwxdNTEd7TDvaCaPLsC9xUF4sE+V0jO0aPYeQ+DhjfiG6iur6/jtDDpXTom9QRSiogxQ8l5ABaDysWcIANQdeyMzbJM3W43vk8/fMEyb+See0BfKpbTlZZK23/g8vlsGiDFIIC4MaCOyqE2iOtMp9PYtIVCZ9Mf33FkSdEyjJfLKh4n64mML9bT6uqyxr6PP7KP3Mzn89jYR3vxCsgswkAiq6wjBzOSArC5AXIjhbHwdrrxZJ1Ru4nXkXOnW7xWlHuDyCtzThDZDYnfE6+KvtFuZxgYF8YY2fHg7Hw+j0PuAVbIpreHOXT5cOMLWHAalDY85HoUyt4vlASBKd+9CGJMA0IIF1abASXzhjQoFpl/L+VeHbWxgCQVdpNija+vrwMJcxA1RsrdLOdrq9VqICnfTcvEsgBRzkwuNWf4rKNNlBmIyTloaASEmMXkXomjX1x5csPd0K6srKjRaISnwslI7imwycljKsxNq9UqnMzE66TAEkxm/FJhdk/P4waSdHJyUnDzMdxORbnnw/2d/8ZNZxG32+3Yae1BXhZ/eq4BtIUjcMbXaYCbm8UB5142goNeUD4oLNB5lmVqNpuBhrk/nhVzSnkPpwahA73vKEnmj4QID0yjjDDaHg9wg+6UKigVWSQVlv8dFWPYnKP3QKjPG387zVOtVoOCYm4Bg4Cl6XRa2JuCTCKnvv6QJf7udruaTCYR2/PxY3wZd6cknb9PlTZtgJ5OKU9AE5fz9069+rq97/VolL0vJneLQL7QIAhpnueF8z0RBqwf1/r6uq6vr0NBeN1td6/cg+B1FKVnY4B8qccuKay7CxOBJNxhlIq3i4AjwuQKx3lDFBbuG30A3XqeckqFSYogJAaJ/rqRox+MgfPwLrAgRBYQfUIpuNtJrAH+nzovkgq1dGg3vCqLwjNq4NL9sygKXFsfB9rp9/Pgsc87Bpw2Y9g5FtFRO/XfK5VKHG4jLRA440z/XbE5UnTOnPGibyBHDDVtxLiSAeSG2OkR7pcqRjdKBPM9PVFalrQGRfrlFCSAAkMDyKEt9N1lGRkh3oFBoG389vRSsqdQpnigfN6pKEmhDClkhyfnRow5Zl16KQZkJ/U23eiQuuxy5EF/p2s9HsN3PB6BcaIffAZAm3qXyA7K/od2UxUCTEErR8Jutd0QsJA6nY7W1tainMF8Pi9wyyAXBo9Jm06ncVAz6YlQH5x3Su0cBlpSuLKOyqSlGwdq8IANi5XveBogSNIn1JEVRsbzqMnikRT8bRpwJTiGwYDaAQEiqNTO5hkpmnfKyJ8DNcViR0glFRY5G5xOTk6iAiLKdzZb7rzEiHtdHcaVMsPu3YCCUBpeRMxzvKViyVwQV7rY6TvtQ2G54mNBO8WBgoZG8cuVL78xLg5K0hoyabsxQChYFry0jCtJy9RO7gMF596He0CuSFPgw9y7TPJ9/x4IGxDlCJn9LC4bxOPcwKV0Gvy489YYAAw9P14/iR921RPXYuMU/UuDxm54fL4BaU7n8OPcu9PE/jnWC8/wjXJOxzgV5ADD5d31RNrO+1z3UvZZlnUl/Y+S/jVJuaT/SNK/lPT3Jb2U9Jmkv5bneT9btPpXJf2MpAtJP5/n+e992TPq9bp2dna0t7cXwTLODaVTuI2SCsgoFQxy6Rk4DnxIXbqbm5tAGdRNhwJBGeCW+sDyXPg4aSmcrixSioigJp93jpPgHwLhvDpoAkTmz0fxuEJm8bCYpCVywRhKihhHnudRudOpJVAE6Bpk6UaXxcVuQb7jyBuD6ZkMzo17GQKMMPenAqV7bSw0V9goCTewtMUDpqmX4gaAw7bTVFD/Drs+mUfPgmJs8Dw8Q8xli/F0mgsQ4MYuReaurAhmOmJ0Lhx547u85ujV5YK/3Wg43cLa8viOc9uOMv3sZ3Z9YggoaeE8ORfz6PNaFp+ir3gg3MsRsO+xAFB47SDWqicTeDwAwMblQWOehezQPzwSjycACl3nuBHAILL+WRMYI/d+6T/PRebve90X2f+qpH+S5/lfzbJsVVJd0n8u6Z/mef4rWZb9sqRflvRLkv6ypI9vf/6CpF+7/X3nRUBua2tLtVpN/X4/tp7XarUIKjGg0BippXROHmWKQDiac8sOsqUwGhMER0YQ0BeTtNiWTy47m3TglD0/GQXl+wBQ2l7LBb4bNOaCxmcd9YDwYiJv781zeQZuJPdx48i93HX3hcP4YlgQWpA8iN2NihtF9xBAQB6M8v65UuV9nueoFHTjCMuRrgc+PTDvHocHZkFJk8kk4i/MlRfPAs1yohSLHdlADqGf2PjFmHhJCuQT5e7eBp/3wny0HQXsiQHSQhH4Ri+UgnP6nu7nfad/eAMeK0l5f6/z754mihHDDrBiHfF51pofSO4cPeAHfYAMuRImpsM4EcPxWB/xDcCQAwkAiaN690rTDCQ3xow1eiiNz6F/nAr2OITrKRS90za872vIY4dOIWE4HnJ9qbLPsqwt6d+U9PO3DZ5ImmRZ9g1JP3X7sV+X9NtaKPtvSPqNfDFD/yzLsm6WZU/zPN9/zzM0nS5OfOHsUDYKebQbDhBFQFSbgCGKqdVqFTIGCFCS7sVGFhcCFyh4QxYAwoCnUKlUNBwOA/V7qqZnayBQLE7n9RypuiBNJpPoO4g4ddlqtVpBiB15SopTc0DTvgfABW44HMZYouRQ+BglglnU9Xa05AEoaAjGAmTuMQu8EqcRGCf6lAaQQUg8izlBiSAfHqz2tmGknaelbVdXV+p0OuEJQuExxk5pSSq0zQ1Yo9GIHZmz2SzmDhTpLjrf4X7uZcCZM/+kBef5YuMcXo/TTcylp15yX0CIyy/jw2fcwHjbJEWchbEAkXuMg9RhZN7jUh57AKx4xtF8vjwoxmMMadukpQeFvPI5B20YWkAQYI1+eyqr0zLOmyP//I3SRp+kbAKXA0qXOY/l+Vp1/QKd6rw98sycEydxatEB4X2u+yD7H5V0JOl/yrLsz0n6F5L+tqQ9FHie5/tZlu3efv65pFf2/de3r92p7KXl8WUrKyvq9XrBAboAw83f3NxoPB4H4vfsE9CKH7PnCw6U6guRjAvPbEB4PTvGhaHMhYIKcT4W5E6d7zTdEEGkbSxS0CFGzOv4OM+HhXceGHTL4kE5OFJjbFMF4QsB5TifL2IgnrkhKfhgSYFE8U7gV3kWSI3PezDcqSMWpSul9NCYdDF63xhHN0gsVP7n8xgAD7KixGir01/w8RgbN5xOcaTKKi1gh3HDEPucIiMoRS+Nwbg5zYcick53bW0tdoRiLFK6h3aBwD010NE1NZaQZbxFj1fwHY9t+a7jVK6gQjg6lLmWljEFR9+UOk93aCMfrHPOCKCdKEoMmI8DBsI9d6ft+DxoGoDocuTzn+5UdtDHmDMWGBene3xdehsYU8bF1/mfBWdfk/R1SX8rz/N/nmXZr2pB2dx1lZmbd3YAZFn2TUnfvP27QI8wsOxkdHTlGSbUNJGWQUoQPArBA3UuCCgRFiouKAvHi00xgZQw9va4G+67KJm8dKetC6q0DKilHD059p6dxL2kZfALYeaeKGQUhaRImfSgUpom6a4lAugbX1wJ0n/6xWLwAKnNc4wfxhk07gFS3wLPPUjFRAm60FMGAYNUhtKckptOp4WUPIwFAes0w8MNAn32IxcdlRGgc4UMGoZmcETKHHlAl/660kbhI9c+9ysrK9rc3FSttji9i/IAyCOyiUL3diOfrjykZZls6Av6goddr9cL1Jaf1eqGG84+vc98XjwK0qtBOjpnLL2AX4q4nQ5zhA2dhpeSct/+XbxZ5JGxd2PPb8qfp2ueZ7GWfJMZoMO9CNYG4M03nQGw3Ngg84DEFBg85LqPsn8t6XWe5//89v9/qIWyP4CeybLsqaRD+/wH9v0Xkt6kN83z/FuSviVJ1Wo1r9frUfyKo+kI0DKpZG94wI1Fh0J0pMnCkpbKhIlwqwzVAqcIZwpqRinhynL5Bh6v4eK1U1DGuOhp8DW9QKKVSiXQNClkzl+y4MjHpjYM3DrCx2adlAeE33fukb9dGSDAvscB/hkkCwXn1JK0RHXONaN0GHPmxrlbR/me144CZW4lhTFw78i3s0vLLA7GDzABGvMccgceeEnO+4PoGQM3kCky8zRBvEjPsCApwIPdyOR0Og0DyolTKFOu4XBY6BeGj4sx5bWrqyu1Wi01Gg1NJpOgmlAgztcjY66o+v1+gd5zio45cMqLcQAhS8vMNA9Ys34xHnitrmw9s8y9E+5L/zEg3nbkgtfdK2fMATiMs3uOzGvqsdFO5hVdRP8dzADU3Lt2g4HRSHUTf6d7Z2j7Q64vVfZ5nr/NsuxVlmWf5Hn+LyX9JUl/dPvzc5J+5fb3b95+5bck/c0sy76tRWD27H18PQPDQSUoBypOgiy3t7cDieEWs2GDe+Duo8B9uzX57K5UUD4sNmrnIEy+fRthYlKkZVAUIfUMFCYa7wCkzX0QEl/EBGFcyAj+8R0Q4cXFRaEsrStS51Q9A0Za1gjynH3a6wuBcUK5eUAJdOQZCSAwis5xobjgmhF+FAFzA+IGGUHPgbygjED/vhhory8gxpPAc6ooXKFjwKEU/DwAFpvLi9MHIMeUY/ba9+yHYKzh37kHfeh2u2E4PQcfOWQsoTkYNxQtcsxzMbBOb0nLjKfb9R1jQ389DuWeEEY5z/PY7EjWGzJWrVaDcqHfjCFeCs+Er5dUiC35mDva5XVff/QBWXNPiXvwv1MrvO7y68YWnYJhByDQX18TrF/XDW4kvM+O4Jk376+DKfrN6w5+kIWHXPfNxvlbkv6XbJGJ86mkX5BUkfQPsiz7RUmfS/rZ28/+Yy3SLv9Yi9TLX/iym6+sLA+H9qqCuJW4kR4kdYSEEuTgDlDDfL7MKycjAe7clRIDLi1REAWKeA6Knc8xqV5SQFKhiBGIB+4dpF2tLo+hQ/mBtN1lhUphYuk/n6W9fN6Dn9wT4+MLx4WIPrpR4p70M3VJ4cCdynE07ly0tIxTIJzcy8sWQIOlPDm53lmWxX4GLhS10yeMkVeK9BpAqateqSwqPrK43FCiwGgXBhxE7IvOqT3awvPdG/RAHXLLPHBP5oODXTDweFcoDhQA8obX6GPNxRwRV/K9Ap7l4ZcbFtqCd5oCIZTn/9fe2YVWdlVx/L9yo21yk0yTGUfGttgOFrUI2lJkRn0Qq7Utoi99cBAsWvClYBVBLD4UHwWxVZBS8QtEqliLlnlwkLHPoy2WOjqtnVKho6NNcIzJFJJMsn04+3fu/55mOrklmZvk7AWX5J57PvZee33+1977OGzi8IkbZs/GidrRI+cNvCCydXl3R+4BHM/wgi/Bg0N9XhNgDH08OI8+0D7Oox0uAw5FQYyfBzE4zea2Ezzbs17kzvnifR6ENmTsU0rPSLplnZ9uXefcJOnegRqR8fClpaXac4IdplwQwmj5zBKptxERH4RpdLTa0ZKom5QaAV5bW6sjY6AAF0ifluYDQBrqXpuXqnDd+Ph4Pb0MR+HYIoIDHLG8vNyHf0q9KYSLi4s1BOSFIN9bXlJt3HAYPAulQAhxol47ILLBWPgMjmYUjVEie2I1qeOjKBbTZ5sCSoTnqf7IyEj9Mnme5TOZzp8/37dtBuM1Pj7eNxcf3iEzbmxQVhwSygXPmm3FMTjU5cU+jEXTuCIjnU6nfn8y5/A83w6B7O3cuXN1FOl1HTeoPm9bUp9cutH1GkIzGkZ3vD7ixz0Y4Dz44XCNB0Hcmzeq0UZkgXvDF/R6PfydWTuuM56dsL03W3dLPSjQAzd/rsN89INMwIu86B+84V6+3bdH4F538GKqZxvIpkMwLl/IJHbCYVWCWHQAmXPocqO0LVbQrq2t1Zs7EcFI6mM8Kb2kvojLI2OEyyMczwLYEc8hFp5PxIJiM30O4eNcXxLPvYhcGTj2Kgfq8EH27IBZN44ve7GWYw6L+AvJPbJyhXdDQFTUFDLHnRE2X09AnxFqhzQcAnHnSxuaGLA7HpQR/mB0yUpwos1pfktLS5qbm6v54VPxnNfAB034hf5j0N1geiToRs95iFHz6LoJEyBLnuKn1CvSe/RPm2kfdRECBsaF/fDdWLkBQ07cUAMrYJA903LZcNlPKfVtfubQVrfbVbfbrbMBIDIPTlhQ5rrSLIzSXpcJj5aRKeedQ4cuC2S2nOdjwjg27yn1JkQ4ht/kH5mnB4cuExhw1yumqXJdc1WwZxkcc/jTM1jOob/wzzMBl72N0rYw9uCUKVX73bC9LRE+io9wEcEyMJLqxRpu8DDEDjkQhbqXZMaGwxpSL0Ljr9SDBMbGxvraheIy0JDDLR7hj42N1bi0Gxk2uwJ+coMMYeSJMhyCccfjBkhSXRPAiK6urtYQmaR6XjdGGaGHL6T7XgDFsboD9mf6FDw/7vUK2oahgzyT8ejZjYnPL6d/KBrYs0dKZHjsbeTTGh0Xl3pOgOfDbyBF3//dtxbAMXiG4vsgcQ1Z3dLSkrrdrqampvpkGmVHdsDNHVJDhuiDR/e0m8wTHXPDyDijLzg2tiVw5+2QCEYXx0kgRb+4X3P3Ur4TlXs07B+HQzC2zObyyBb++KpcN+4EIThdZBs5JpD0SJy/ZJk4T/TZ+despXg2xNgTlHi7vEbW1I0mRIeBJ5tHtnZkZE8KhdGAsaQ0jokTuSOIMJLvKPfKykq9nwpRCkyUelOeXGhoB8oMpOTL3H1/edrjUQuCzkCgBN5Px/Y4hhITTfAbWQlQEvdxg93MCrwIitA5hsozPXXle9NoO/zR6XQ0PT2t1dXVeh8ix6OJuNxhAr2xrwuGxMfSIzEvdjp+3u1260gI58t5kMNu3OvVV1/VxMSEJicn63v7DBQ3ep4pSf3vynVn6+1jjIAnIH8+QQX84C/tZXxw7PAeR+PBCu3mnh4d0g8v+qVULSrEGbkOALWha9RR2CuK5yPPzmuPtHmuyyLkuuhOlet9RTPjNjExoZmZGc3NzdWvuMQWkMV6QZRAxNvhY4jsu0P0fjiUQluaDsTH250FtsEDA/TJEQAfbxylt5F2eNBDfcjl3GdCbfoK2stBDDCDzeIplI1o2g2LMw3DjWB5dOfGjWdRxJX6I02Y7Smzf/bs2VPPsiCqwhExWKzEJH2WekbQsV+MMrUGFmNxHtNQifQwRA4DeZroUQoRFILC8zB0Fy5cqDMTqTeF1IthGH2PnNnW2SNrj9y5H+11pxYRNb6PcQQSwGmAw8IbN/z+8cgPw+XKjJIQ+c/Pz0uSJicn6z55Gu0RvxfmHI5xHkJNuAZ5QjZJ9ZeXl+sXpHc6nb7tAnACzDaibz4Liu9ElUR1BABuyPnNs1N/IQ2GyH+H/0CZHsBwDKfrjg4ojQyaa71+wjPQQXji2YrUW8U+OTmp/fv31zN6WNXsjhdj1zzmbzrzMXHIjHs5BMjvEAELzsWhS2xFc24+8uG2yWsR6JY7Aod2/d7NgAhZ47xmPWGjtC2MPYQBQGH4n+Ih+5KwRNs9m0fqCLUbcgaZGTsoFRGrzyPHUDfxZAYM799M19mczVNI2ul4NZGNRwJTU1M1dMMHY8Q1jmcy4I4101ffcdIhCSAtqX+LZRyE84GokWdjAFk96bUS2tLpdNTtdusZMhSe4Xtzyp+nvCjHyEh/gRjjjNIDgTGeGEKPzrm3FxDn5+c1MjKiffv2aXR0VLOzs32LqzBYyCFK5u1qwgZExu4g/DznHYEMW25jmH22BvzwqbYu08gNcoWR55hf59gwbQG2YifYtbXeexmabUd3HDKC/+ipO1SyAvTVsXrGn6zX941ijLjvysqKZmdnNTc3V7+LwmE4+o1Bp40+S8dlHegRhIC+uN5yrdeMgIppuxfM0QvPnjxK94zPJw14JuT98oyfIKxZ/4CX9MmDvo3StjD2MAED4ors0IP0Ws+HkBJZeMGRVAthACICNnFhw+A47uYCCS7J/GPfDgDhRpk9k2D2gLcXIi334qIrlWP/XtxzXNshE08nfVaTCx18pYjpzsIXfiGUGEuvDzjk4ilvM1qUelsdYxg8BXWslDGBmqm4QxdS/5S09YwRkSgR3+LiYp1xOZzA/T2F93ni3J8xcBnxjMvhLvpDXcZXfbth8gyAWVfIr0NCEE4FGfO6Fs/EYLkxoq8YQ9/fhb4jDy7/9Nlly1+CQo2H8SE6b2ZE09PTfVsa00cyXodekAmOufPxbHlsbKxuB7ATNQnIdxHF2fo4IdP022c2eXbp8CI8pb+e6TA+XnfBfvk5TC33rJDfqKF57QDHgew6jwehbWHspf5XgkEYZE9nGSTwNN+7hgiBOcG8WKEZrToeTHqEskMw3LdrZVoZRWPgCIqUvACb7z5Nr2mY3Cg4juy1BwRU6gkNPPH93zEcZA/gmKurq69ZnYkh9KgBJdq7d69GRkZ09uzZehdQBN2NPfdzvN+jaKay0navYXCtO2W2cmCzNfoIOfzF/ZpKQr9RJodC4Js7UM7zDIv2eOTtUS4RmRfNvI9S/1vEGFuMDobdAxZmd5EpYAx5rmPj3JOxpeC4srJSBzGeJdE2HyscvU9V9gDE+enZocNfPg7AmD7GPiceHUSfveCO7K5ntNarD/AMr215m5tZPfUSnoNOejbGvRy390zVdc+DHOeXjxH3Zvw8GHVIxp2YZ+04J/QS3Xa+cM2ONPYppXp2hOO9jlFhHEgbWTbPC6cxTMAGCB4rGBlEX+jj90cx2HbYp0R6IVHqCTmKxbUoLgVJFB3BwsBI6otqETYiSfruhgQB8fTUjTDGD6FGYFZXV+tFXcAjCwsLNd9duJtFVOelPwM+eDTsr2zknjgHBNsxXwSWKEfqFXMxiFzrRtUjfp7vjgEZ4jqyPXcujANbSZAFofzg485jjA2L8YC83HgxPvRlYmJCCwsLfUafPY+kXsbShE5cHjG4BAOebbKewx0kvPCxaRp/ImifteT1naaTAxZhMZkbXTdGvgqcfi0tLWl2drbWE9pHAMSmhC5b7mjpB+PnBWqCmvn5+b7ImaCOMfSCsEMxzhP6zdoQqTe3vgkj0WdkxidOuHw67OYBKzUSMmmHeHwVvy/89AzCkYNBKAb1DltBEbGg6mUobad9kuaG3YghU+FB4QFU+HBpHrw9pfSWjdxoW0T2kp5PKa23QrdVFBFPtZ0PhQeFB1Dhw+byYPBcoFChQoUK7Tgqxr5QoUKFWkDbxdh/f9gN2CZU+FB4IBUeQIUPm8iDbVGgLVSoUKFCW0vbJbIvVKhQoUJbSEM39hFxe0Q8HxGnI+L13m27oykiro2IJyPiVET8JSLuy8dnIuJ3EfFC/judj0dEfDfz5dmIuHm4Pdg8iohORPwpIo7m79dHxInMg19E9ZIcRcQV+fvp/Pt1w2z3ZlJEXBURj0XEc1kmDrdNFiLiy1kXTkbEoxFxZRtkISJ+FBGvRMRJOzbw2EfE3fn8FyLi7ks9d6jGPiI6kr4n6Q5JN0o6EhE3DrNNW0gXJH0lpfRuSYck3Zv7+jVJx1NKN0g6rt7L3O/3LmaYAAADc0lEQVSQdEP+fEHSw5e/yVtG90k6Zd+/KenBzINzku7Jx++RdC6l9A5JD+bzdgt9R9JvU0rvkvReVfxojSxExNWSvijplpTSeyR1JH1a7ZCFn0i6vXFsoLGPiBlJD6h69ev7JT2Ag7go+T4Ll/sj6bCkY/b9fkn3D7NNl7Hvv5H0MVWLyQ7kYwdUrTmQpEckHbHz6/N28kfVC+iPS/qIpKOSQtWikdGmTEg6Julw/n80nxfD7sMm8GBK0kvNvrRJFiRdLellSTN5bI9K+nhbZEHSdZJOvtGxl3RE0iN2vO+89T7DhnEYcOhMPrarKaegN0k6IemtKb+QPf/dn0/brbx5SNJXJbH5yV5J/00psQGI97PmQf59Pp+/0+mgpFlJP85w1g8ioqsWyUJK6R+SvqXq/dVnVY3t02qfLECDjv3AMjFsY7/ee7V29fSgiJiQ9CtJX0op/e/1Tl3n2I7mTUR8QtIrKaWn/fA6p6YN/LaTaVTSzZIeTindJOm8emn7erTr+JAhh09Jul7S2yR1VUEWTdrtsnApuli/B+bHsI39GUnX2vdrJP1zSG3ZcoqIN6ky9D9LKT2eD/87Ig7k3w9IeiUf3428+aCkT0bE3yX9XBWU85CkqyKCrTu8nzUP8u97JP3ncjZ4i+iMpDMppRP5+2OqjH+bZOGjkl5KKc2mlFYkPS7pA2qfLECDjv3AMjFsY/9HSTfkCvybVRVonhhym7aEotpe74eSTqWUvm0/PSGJSvrdqrB8jn82V+MPSZonzduplFK6P6V0TUrpOlVj/fuU0mckPSnprnxakwfw5q58/o6P5lJK/5L0ckS8Mx+6VdJf1SJZUAXfHIqI8awb8KBVsmA06Ngfk3RbREznLOm2fOzitA0KFXdK+pukFyV9fdjt2cJ+fkhVmvWspGfy505VuONxSS/kvzP5/FA1U+lFSX9WNWth6P3YRH58WNLR/P9BSX+QdFrSLyVdkY9fmb+fzr8fHHa7N7H/75P0VJaHX0uabpssSPqGpOcknZT0U0lXtEEWJD2qqk6xoipCv+eNjL2kz2d+nJb0uUs9t6ygLVSoUKEW0LBhnEKFChUqdBmoGPtChQoVagEVY1+oUKFCLaBi7AsVKlSoBVSMfaFChQq1gIqxL1SoUKEWUDH2hQoVKtQCKsa+UKFChVpA/weKOKfFR8906wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2UfNc6zeJ1J9",
        "colab_type": "code",
        "colab": {},
        "outputId": "12fcb2fd-91b9-4824-cddf-2337cc828ea9"
      },
      "source": [
        "cv2.imwrite('liongary.jpg',img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PNAimBnjJ1KJ",
        "colab_type": "text"
      },
      "source": [
        "# Loading Video"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ml4TbhurJ1KM",
        "colab_type": "code",
        "colab": {},
        "outputId": "de8e02f0-f8cc-4adf-eae1-5b8fd7f8ef40"
      },
      "source": [
        "# Start capturing using 0 and 1\n",
        "cap=cv2.VideoCapture(0)# can also 1 fro capturing 2nd webcam\n",
        "fourcc=cv2.VideoWriter_fourcc(*'XVID')\n",
        "out=cv2.VideoWriter('output.avi',fourcc,20.0,(640,480))\n",
        "while True:\n",
        "    ret,frame=cap.read()\n",
        "    gray=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
        "    print(gray.shape)\n",
        "    out.write(frame)\n",
        "    cv2.imshow('gray',gray)\n",
        "    cv2.imshow('frame',frame)\n",
        "    if cv2.waitKey(1) & 0xFF==ord('q'):\n",
        "        break\n",
        "cap.release()\n",
        "out.release()\n",
        "cv2.destroyAllWindows()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n",
            "(480, 640)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0CZpyNmpJ1KX",
        "colab_type": "text"
      },
      "source": [
        "# drawing on the images and videos using OpenCv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sH1XfCIeJ1Kf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img=cv2.imread('lion.jpg',cv2.IMREAD_COLOR)\n",
        "# Drawing the line on the image\n",
        "cv2.line(img,(0,0),(1500,1500),(255,255,255),15)# for black it would (0,0,0) and white(255,255,255) 15 stands for line width\n",
        "\n",
        "#Drawing a rectangle\n",
        "cv2.rectangle(img,(15,25),(200,150),(0,255,0),5)\n",
        "\n",
        "#circle\n",
        "cv2.circle(img,(100,100),78,(255,0,0),-1)\n",
        "\n",
        "#drawing a polygon we need to pass a list of points and it is going to join all the points int the list\n",
        "pts=np.array([[157,99],[766,97],[335,98],[237,98],[36,12],[100,100]],np.int32)\n",
        "pts=pts.reshape((-1,1,2))\n",
        "cv2.polylines(img,[pts],True,(0,255,255),2)# true is for connecting the points\n",
        "\n",
        "# For writing the text on the image\n",
        "font=cv2.FONT_HERSHEY_SIMPLEX\n",
        "cv2.putText(img,'Hey!guys I am Lion',(0,130),font,2,(200,78,255),2,cv2.LINE_AA)\n",
        "\n",
        "cv2.imshow('image',img)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_VDw4FhJ1Kr",
        "colab_type": "text"
      },
      "source": [
        "# Image operations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBjbSIXOJ1Ku",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img=cv2.imread('lion.jpg',cv2.IMREAD_COLOR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5TzpVRuJ1K2",
        "colab_type": "code",
        "colab": {},
        "outputId": "6f86ffd2-97e9-4a39-b0ab-68f323242e99"
      },
      "source": [
        "# Printing Color Value for the pixel\n",
        "pix=img[55,55]\n",
        "print(pix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[95 66 45]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPEOR7z_J1LD",
        "colab_type": "code",
        "colab": {},
        "outputId": "d48ff51c-4f65-4d7d-c65e-79c5413fa2ee"
      },
      "source": [
        "#actually we can modify the particular pixel using \n",
        "img[55,55]=[255,255,255]# specifying color\n",
        "#Checking\n",
        "pix=img[55,55]\n",
        "print(pix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[255 255 255]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sjlTOrI7J1LQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ROI-region Of Image It basically Stands for image inside an image\n",
        "roi=img[300:400,300:400]\n",
        "# we even can basically convert all the roi in the specific color\n",
        "#img[300:1500,370:1500]=[255,255,255]\n",
        "#print(roi)\n",
        "cv2.imshow('image',roi)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJclTIIWJ1LZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We can actually move the roi from one region to another\n",
        "img[0:100,0:100]=roi\n",
        "cv2.imshow('image',img)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pAdaHxtKJ1Ll",
        "colab_type": "text"
      },
      "source": [
        "# Image Arithmetics and Logic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RsAc6HrJ1Lp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image1=cv2.imread('lion.jpg',cv2.IMREAD_COLOR)\n",
        "#image2=cv2.imread('lion.jpg',cv2.IMREAD_COLOR)\n",
        "image2=cv2.imread('airplane.png',cv2.IMREAD_COLOR)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELjMZTshJ1L0",
        "colab_type": "code",
        "colab": {},
        "outputId": "1fd7e0d0-3444-48c7-c185-e9c5087fe174"
      },
      "source": [
        "image2 = cv2.resize(image2, (1000, 667), interpolation=cv2.INTER_AREA)\n",
        "image1.shape,image2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((667, 1000, 3), (667, 1000, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vKlmn-CAJ1MC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#add=image1+image2 Using this command images will hardly lose their opaqueness\n",
        "add=cv2.add(image1,image2) # using this image will fit to their best combo\n",
        "\n",
        "#we can also add images weightedly\n",
        "add=cv2.addWeighted(image1,0.8,image2,0.2,0)\n",
        "cv2.imshow('add',add)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEjBLoNMJ1MS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "rows,cols,channels=image2.shape\n",
        "roi=image1[0:rows,0:cols]\n",
        "image2gray=cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "# this operation will basically convert all values above 60 to 255 and below to 0\n",
        "ret,mask=cv2.threshold(image2gray,60,255,cv2.THRESH_BINARY_INV)\n",
        "\n",
        "# very similar to the logic operations\n",
        "mask_inv=cv2.bitwise_not(mask)\n",
        "\n",
        "image1_bg=cv2.bitwise_and(roi,roi,mask=mask_inv)\n",
        "image2_fg=cv2.bitwise_and(image2,image2,mask=mask)\n",
        "\n",
        "dst=cv2.add(image1_bg,image2_fg)\n",
        "image1[0:rows,0:cols]=dst\n",
        "\n",
        "cv2.imshow('image1',image1)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "80ue8EBYJ1Ml",
        "colab_type": "text"
      },
      "source": [
        "# Thresholding\n",
        "Idea of thresholding is to hold pixels between two values 0 and 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Vlv6CA0J1Mq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "retval,threshold=cv2.threshold(image1,100,255,cv2.THRESH_BINARY_INV) # pixel above 12 will be 255 and less than 12 will be 0\n",
        "\n",
        "# best of these is Gaussion threshold and is adaptive\n",
        "grayscaled=cv2.cvtColor(image1,cv2.COLOR_BGR2GRAY)\n",
        "gaus=cv2.adaptiveThreshold(grayscaled,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,115,1)\n",
        "\n",
        "retval2,otsu=cv2.threshold(grayscaled,125,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
        "cv2.imshow('original',image1)\n",
        "cv2.imshow('threshold',threshold)\n",
        "cv2.imshow('gaus',gaus)\n",
        "cv2.imshow('otsu',otsu)\n",
        "\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kt2Nd6SlJ1M6",
        "colab_type": "text"
      },
      "source": [
        "# filtering of Images using Open CV\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0-ACb1GJ1NE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "2c353c49-65a2-4097-ecd0-2de65f66c9c4"
      },
      "source": [
        "cap=cv2.VideoCapture(0)\n",
        "while True:\n",
        "    _,frame=cap.read()\n",
        "    #type(frame)\n",
        "    #frame.shape\n",
        "    #frame = np.array(frame, dtype=np.uint16)\n",
        "    hsv=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)\n",
        "    \n",
        "    lower_red=np.array([150,150,50]) # first value-hue second-saturation third-val\n",
        "    upper_red=np.array([180,255,150])\n",
        "    \n",
        "    mask=cv2.inRange(hsv,lower_red,upper_red)\n",
        "    res=cv2.bitwise_and(frame,frame,mask=mask)\n",
        "    \n",
        "    #for blurring we need kernel\n",
        "    kernel=np.ones((15,15),np.float32)/225\n",
        "    \n",
        "    smoothed=cv2.filter2D(res,-1,kernel)\n",
        "    \n",
        "    #more better type of blur\n",
        "    blur=cv2.GaussianBlur(res,(15,15),0)\n",
        "    \n",
        "    #median blur\n",
        "    median=cv2.medianBlur(res,15)\n",
        "    #bilateral\n",
        "    bilateral=cv2.bilateralFilter(res,15,75,75)\n",
        "    cv2.imshow('bilateral',bilateral)\n",
        "    cv2.imshow('smoothed',smoothed)\n",
        "    cv2.imshow('frame',frame)\n",
        "    cv2.imshow('mask',mask)\n",
        "    cv2.imshow('res',res)\n",
        "    cv2.imshow('blur',blur)\n",
        "    cv2.imshow('median',median)\n",
        "    k=cv2.waitKey(5) & 0xFF\n",
        "    if k==27:\n",
        "        break\n",
        "        \n",
        "cv2.destroyAllWindows()\n",
        "cap.release()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-330c231bb6ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mVideoCapture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mframe\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m#type(frame)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m#frame.shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'cv2' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W-WUrluPJ1NM",
        "colab_type": "text"
      },
      "source": [
        "# Morphological transformation\n",
        "It is used to remove the white noises in the background"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8xbIZcciJ1NO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cap=cv2.VideoCapture(0)\n",
        "while True:\n",
        "    _,frame=cap.read()\n",
        "    #type(frame)\n",
        "    #frame.shape\n",
        "    #frame = np.array(frame, dtype=np.uint16)\n",
        "    hsv=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)\n",
        "    \n",
        "    lower_red=np.array([150,150,50]) # first value-hue second-saturation third-val\n",
        "    upper_red=np.array([180,255,150])\n",
        "    \n",
        "    mask=cv2.inRange(hsv,lower_red,upper_red)\n",
        "    res=cv2.bitwise_and(frame,frame,mask=mask)\n",
        "    \n",
        "    #kernel\n",
        "    kernel=np.ones((5,5),np.uint8)\n",
        "    \n",
        "    erosion=cv2.erode(mask,kernel,iterations=1)\n",
        "    dilation=cv2.dilate(mask,kernel,iterations=1)\n",
        "    \n",
        "    #opening will remove false positives i.e. noises in the background\n",
        "    opening=cv2.morphologyEx(mask,cv2.MORPH_OPEN,kernel)\n",
        "    #closing will remove the false negatives i.e. noises in the object\n",
        "    closing=cv2.morphologyEx(mask,cv2.MORPH_CLOSE,kernel)\n",
        "    \n",
        "    grayscaled=cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)\n",
        "    #tophat-differnce between original image and the opening image\n",
        "    tophat=grayscaled-opening\n",
        "    \n",
        "    #blak ht-It is the differnce between the original image and closing image\n",
        "    blackhat=grayscaled-closing\n",
        "    \n",
        "    cv2.imshow('mask',mask)\n",
        "    cv2.imshow('res',res)\n",
        "    cv2.imshow('erosion',erosion)\n",
        "    cv2.imshow('dilation',dilation)\n",
        "    cv2.imshow('opening',opening)\n",
        "    cv2.imshow('closing',closing)\n",
        "    cv2.imshow('tophat',tophat)\n",
        "    cv2.imshow('blackhat',blackhat)\n",
        "    k=cv2.waitKey(5) & 0xFF\n",
        "    if k==27:\n",
        "        break\n",
        "        \n",
        "cv2.destroyAllWindows()\n",
        "cap.release()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sp1O0hJ5J1NV",
        "colab_type": "text"
      },
      "source": [
        "# Edge detection and Gradients"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nk6xt61LJ1NZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cap=cv2.VideoCapture(0)\n",
        "while True:\n",
        "    _,frame=cap.read()\n",
        "    \n",
        "    laplacian=cv2.Laplacian(frame,cv2.CV_64F)\n",
        "    # detect in the horizontal diection\n",
        "    sobelx=cv2.Sobel(frame,cv2.CV_64F,1,0,ksize=5)\n",
        "    \n",
        "    #detect in the vertical direction\n",
        "    sobely=cv2.Sobel(frame,cv2.CV_64F,0,1,ksize=5)\n",
        "    \n",
        "    # for detect edges \n",
        "    canny=cv2.Canny(frame,100,100)\n",
        "    cv2.imshow('original',frame)\n",
        "    cv2.imshow('laplacian',laplacian)\n",
        "    cv2.imshow('sobelx',sobelx)\n",
        "    cv2.imshow('sobely',sobely)\n",
        "    cv2.imshow('canny',canny)\n",
        "    k=cv2.waitKey(5) & 0xFF\n",
        "    if k==27:\n",
        "        break\n",
        "cv2.destroyAllWindows()\n",
        "cap.release()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x5c3QYuoJ1Nm",
        "colab_type": "text"
      },
      "source": [
        "# Template Matching"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNsSaswEJ1No",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_bgr=cv2.imread('template.jpg')\n",
        "img_gray=cv2.cvtColor(img_bgr,cv2.COLOR_BGR2GRAY)\n",
        "template=cv2.imread('maintemp.jpg',0)\n",
        "w,h=template.shape[::-1]\n",
        "res=cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)\n",
        "threshold=0.73\n",
        "loc=np.where(res >=threshold)\n",
        "for pt in zip(*loc[::-1]):\n",
        "    cv2.rectangle(img_bgr, pt, (pt[0] + w, pt[1] + h), (0,255,255), 2)\n",
        "\n",
        "cv2.imshow('Detected',img_bgr)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b6YXvnzNJ1Nv",
        "colab_type": "text"
      },
      "source": [
        "# GrabCut Foreground Extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTXg7HEWJ1Ny",
        "colab_type": "code",
        "colab": {},
        "outputId": "f5139378-de15-48d9-cae7-dcbb959a1b4f"
      },
      "source": [
        "img=cv2.imread('background.jpg')\n",
        "mask=np.zeros(img.shape[:2],np.uint8)\n",
        "\n",
        "bgdmodel=np.zeros((1,65),np.float64)\n",
        "fgdmodel=np.zeros((1,65),np.float64)\n",
        "\n",
        "rect=(50,50,300,500)\n",
        "\n",
        "cv2.grabCut(img,mask,rect,bgdmodel,fgdmodel,5,cv2.GC_INIT_WITH_RECT)\n",
        "mask2=np.where((mask==2)|(mask==0),0,1).astype('uint8')\n",
        "img=img*mask2[:,:,np.newaxis]\n",
        "plt.imshow(img)\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkevTTx0J1Oc",
        "colab_type": "text"
      },
      "source": [
        "# Corner Detection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VMkzM2NJ1Oe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img=cv2.imread('corner.jpg')\n",
        "gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
        "gray=np.float32(gray)\n",
        "\n",
        "corners=cv2.goodFeaturesToTrack(gray,200,0.01,10)\n",
        "corners=np.int0(corners)\n",
        "for corner in corners:\n",
        "    x,y=corner.ravel()\n",
        "    cv2.circle(img,(x,y),3,255,-1)\n",
        "cv2.imshow('corners',img)    \n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vuCSanF_J1Oq",
        "colab_type": "text"
      },
      "source": [
        "# Feature Matching (Homography) Brute Force"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LA-6zkYiJ1Os",
        "colab_type": "code",
        "colab": {},
        "outputId": "05852546-d7c1-4208-d29b-a138fe2c5d5a"
      },
      "source": [
        "import numpy as np\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "img1 = cv2.imread('opencv-feature-matching-template.jpg',0)\n",
        "img2 = cv2.imread('opencv-feature-matching-image.jpg',0)\n",
        "orb = cv2.ORB_create()\n",
        "\n",
        "kp1, des1 = orb.detectAndCompute(img1,None)\n",
        "kp2, des2 = orb.detectAndCompute(img2,None)\n",
        "bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n",
        "matches = bf.match(des1,des2)\n",
        "matches = sorted(matches, key = lambda x:x.distance)\n",
        "img3 = cv2.drawMatches(img1,kp1,img2,kp2,matches[:10],None, flags=2)\n",
        "plt.imshow(img3)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j4MAIQqwJ1O8",
        "colab_type": "text"
      },
      "source": [
        "# MOG Background Reduction "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0W6RCoPjJ1PD",
        "colab_type": "code",
        "colab": {},
        "outputId": "b09ee343-c6dc-425a-f063-cb6525fe5b63"
      },
      "source": [
        "cap=cv2.VideoCapture('people-walking.mp4')\n",
        "fgbg= cv2.createBackgroundSubtractorMOG2()\n",
        "\n",
        "while(1):\n",
        "    ret, frame = cap.read()\n",
        "\n",
        "    fgmask = fgbg.apply(frame)\n",
        " \n",
        "    cv2.imshow('fgmask',frame)\n",
        "    cv2.imshow('frame',fgmask)\n",
        "\n",
        "    \n",
        "    k = cv2.waitKey(30) & 0xff\n",
        "    if k == 27:\n",
        "        break\n",
        "    \n",
        "\n",
        "cap.release()\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "error",
          "evalue": "OpenCV(3.4.1) C:\\Miniconda3\\conda-bld\\opencv-suite_1533128839831\\work\\modules\\highgui\\src\\window.cpp:356: error: (-215) size.width>0 && size.height>0 in function cv::imshow\n",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31merror\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-7-f0ff377cffff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mfgmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfgbg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m     \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'fgmask'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m     \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'frame'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfgmask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31merror\u001b[0m: OpenCV(3.4.1) C:\\Miniconda3\\conda-bld\\opencv-suite_1533128839831\\work\\modules\\highgui\\src\\window.cpp:356: error: (-215) size.width>0 && size.height>0 in function cv::imshow\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wXgXzToZJ1PO",
        "colab_type": "text"
      },
      "source": [
        "# Haar Cascade Object Detection Face & Eye "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H4wu6YWVJ1PR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
        "full_body=cv2.CascadeClassifier('haarcascade_fullbody.xml')\n",
        "eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n",
        "\n",
        "cap = cv2.VideoCapture(0)\n",
        "while 1:\n",
        "    ret, img = cap.read()\n",
        "    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    faces = full_body.detectMultiScale(gray, 1.3, 5)\n",
        "\n",
        "    for (x,y,w,h) in faces:\n",
        "        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
        "        roi_gray = gray[y:y+h, x:x+w]\n",
        "        roi_color = img[y:y+h, x:x+w]\n",
        "        \n",
        "        eyes = eye_cascade.detectMultiScale(roi_gray)\n",
        "        for (ex,ey,ew,eh) in eyes:\n",
        "            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)\n",
        "\n",
        "    cv2.imshow('img',img)\n",
        "    k = cv2.waitKey(30) & 0xff\n",
        "    if k == 27:\n",
        "        break\n",
        "\n",
        "cap.release()\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eli2TFdIJ1PZ",
        "colab_type": "text"
      },
      "source": [
        "# Gathering Images "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wUZM-Ze-J1Pb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import urllib.request\n",
        "import os"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4Jnj04OJ1Pj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def store_raw_images(link):\n",
        "    neg_images_link = link   \n",
        "    neg_image_urls = urllib.request.urlopen(neg_images_link).read().decode()\n",
        "    pic_num = 1\n",
        "    \n",
        "    if not os.path.exists('neg'):# neg is name of folder\n",
        "        os.makedirs('neg')\n",
        "        \n",
        "    for i in neg_image_urls.split('\\n'):\n",
        "        try:\n",
        "            print(i)\n",
        "            urllib.request.urlretrieve(i, \"neg/\"+str(pic_num)+\".jpg\")\n",
        "            img = cv2.imread(\"neg/\"+str(pic_num)+\".jpg\",cv2.IMREAD_GRAYSCALE)\n",
        "            # should be larger than samples / pos pic (so we can place our image on it)\n",
        "            resized_image = cv2.resize(img, (100, 100))\n",
        "            cv2.imwrite(\"neg/\"+str(pic_num)+\".jpg\",resized_image)\n",
        "            pic_num += 1\n",
        "            \n",
        "        except Exception as e:\n",
        "            print(str(e))  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5AKT-fWJ1Pr",
        "colab_type": "text"
      },
      "source": [
        "# sketch of a image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVrnzNJTJ1Pt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        " \n",
        "img_gray = cv2.imread(\"image1.jpeg\",cv2.IMREAD_GRAYSCALE)\n",
        "#img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)\n",
        "img_gray_inv = 255 - img_gray\n",
        "img_blur = cv2.GaussianBlur(img_gray_inv, ksize=(21, 21),sigmaX=0, sigmaY=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "biC7swyZJ1Pz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dodgeV2(image, mask):\n",
        "    return cv2.divide(image, 255-mask, scale=256)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pvWqozZ_J1P6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_blend = dodgeV2(img_gray, img_blur)\n",
        "cv2.imshow(\"pencil sketch\", img_blend)\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOrLZOW_J1QD",
        "colab_type": "code",
        "colab": {},
        "outputId": "7b658a3f-51a8-4ba4-d49a-37f324fd2f36"
      },
      "source": [
        "cv2.imwrite(\"pencilsketch.jpg\",img_blend)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYe7UlVEJ1QS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2 as cv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubWm3knAUoSr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "class ImageDenoising:\n",
        "    '''OpenCV provides variations of this technique.\n",
        "        -'MD'-cv.fastNlMeansDenoising() - works with a single grayscale images\n",
        "        -'MDC'-cv.fastNlMeansDenoisingColored() - works with a color image.\n",
        "        -'MDM'-cv.fastNlMeansDenoisingMulti() - works with image sequence captured in short period of time (grayscale images)\n",
        "        -'MDCM'-cv.fastNlMeansDenoisingColoredMulti() - same as above, but for color images.\n",
        "        \n",
        "        default variation used:\n",
        "        '''\n",
        "\n",
        "    def _init__():\n",
        "        self.variation='MD'\n",
        "        self.src=src\n",
        "        self.dst=dst\n",
        "        self.templateWindowSize=templateWindowSize\n",
        "        self.searchWindowSize=searchWindowSize\n",
        "        self.h=h\n",
        "        self.temporalWindowSize=temporalWindowSize\n",
        "        self.normType=normType\n",
        "        self.imgToDenoiseIndex=imgToDenoiseIndex\n",
        "        \n",
        "    def denoising(self,path,dst=None,templateWindowSize=7,searchWindowSize=21,h=3,temporalWindowSize=3,normType='NORM_L2',imgToDenoiseIndex=2):\n",
        "        self.variation='MD'\n",
        "        self.dst=dst\n",
        "        self.templateWindowSize=templateWindowSize\n",
        "        self.searchWindowSize=searchWindowSize\n",
        "        self.h=h\n",
        "        self.temporalWindowSize=temporalWindowSize\n",
        "        self.normType=normType\n",
        "        self.imgToDenoiseIndex=imgToDenoiseIndex\n",
        "        self.src=cv.imread(path)\n",
        "        \n",
        "        ''' Common-arguments-\n",
        "            src-Input 8-bit 1-channel, 2-channel, 3-channel or 4-channel image.\n",
        "            dst-Output image with the same size and type as src .\n",
        "            hForColorComponents : same as h, but for color images only. (normally same as h) \n",
        "            templateWindowSize-Size in pixels of the template patch that is used to compute weights. Should be odd. Recommended value 7 pixels\n",
        "            searchWindowSize-Size in pixels of the window that is used to compute weighted average for given pixel. \n",
        "                            Should be odd. Affect performance linearly: greater searchWindowsSize - greater denoising time. Recommended value 21 pixels\n",
        "            h-Parameter regulating filter strength. Big h value perfectly removes noise but also removes image details, smaller h value preserves details \n",
        "                            but also preserves some noise'''\n",
        "        if self.variation is 'MD': #works with a single grayscale images\n",
        "            return cv.fastNlMeansDenoising(self.src,self.dst,self.templateWindowSize,self.searchWindowSize,self.h) \n",
        "        elif self.variation is 'MDC':\n",
        "            self.hForColorComponents=self.h\n",
        "            return cv.fastNlMeansDenoisingColored(self.src,self.dst,self.templateWindowSize,self.hForColorComponents,self.searchWindowSize,self.h)\n",
        "        elif self.variation is 'MDM':\n",
        "            '''Additional Parameters \n",
        "                imgToDenoiseIndex-Target image to denoise index in srcImgs sequence\n",
        "                temporalWindowSize\tNumber of surrounding images to use for target image denoising. Should be odd.\n",
        "                                    Images from imgToDenoiseIndex - temporalWindowSize / 2 to imgToDenoiseIndex - temporalWindowSize / 2 from srcImgs will be used to denoise srcImgs[imgToDenoiseIndex] image.\n",
        "                normType\tType of norm used for weight calculation. Can be either NORM_L2 or NORM_L1\n",
        "               '''\n",
        "            return cv.fastNlMeansDenoisingMulti(self.src,self.dst,self.imgToDenoiseIndex,self.templateWindowSize,self.temporalWindowSize,self.searchWindowSize,self.h,normType)\n",
        "        elif self.variation is 'MDCM':\n",
        "            return cv.fastNlMeansDenoisingColoredMulti(src,imgToDenoiseIndex,temporalWindowSize,templateWindowSize,dst,hForColorComponents,searchWindowSize,h)\n",
        "\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2uT-qbQ3UprB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 183
        },
        "outputId": "cea58760-4ebd-4f05-9e11-4de200549de5"
      },
      "source": [
        "c=ImageDenoising('MDC','/content/shivani_photo-min.jpeg',templateWindowSize=7,searchWindowSize=21,h=3,temporalWindowSize=3,normType='NORM_L2',imgToDenoiseIndex=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-5e52a7f9335d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mImageDenoising\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MDC'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'/content/shivani_photo-min.jpeg'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtemplateWindowSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msearchWindowSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtemporalWindowSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnormType\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'NORM_L2'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimgToDenoiseIndex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: object() takes no parameters"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1S1tlhlTVSXD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Ti3n68AzeVu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "img=cv.imread('airplane.png')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GmW-D9vnyKk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "idn=ImageDenoising('MDC','airplane.png')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jj76AfCO0FmB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "66a8dc0b-3f53-49e6-fe5e-3dbbce46e56a"
      },
      "source": [
        "idn.denoising()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[215, 131,  24],\n",
              "        [254, 254, 254],\n",
              "        [254, 254, 254],\n",
              "        ...,\n",
              "        [254, 254, 254],\n",
              "        [215, 131,  24],\n",
              "        [215, 215, 215]],\n",
              "\n",
              "       [[215, 131,  24],\n",
              "        [254, 254, 254],\n",
              "        [254, 254, 254],\n",
              "        ...,\n",
              "        [254, 254, 254],\n",
              "        [215, 131,  24],\n",
              "        [215, 215, 215]],\n",
              "\n",
              "       [[215, 131,  24],\n",
              "        [254, 254, 254],\n",
              "        [254, 254, 254],\n",
              "        ...,\n",
              "        [254, 254, 254],\n",
              "        [215, 131,  24],\n",
              "        [213, 213, 213]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[215, 131,  24],\n",
              "        [125, 125, 125],\n",
              "        [ 34,  48, 120],\n",
              "        ...,\n",
              "        [  0,   6,   5],\n",
              "        [215, 131,  24],\n",
              "        [206, 206, 206]],\n",
              "\n",
              "       [[215, 131,  24],\n",
              "        [124, 124, 124],\n",
              "        [ 43,  54, 124],\n",
              "        ...,\n",
              "        [  3,  10,   9],\n",
              "        [215, 131,  24],\n",
              "        [208, 208, 208]],\n",
              "\n",
              "       [[215, 131,  24],\n",
              "        [220, 134,  26],\n",
              "        [190, 114,  16],\n",
              "        ...,\n",
              "        [215, 131,  24],\n",
              "        [215, 131,  24],\n",
              "        [213, 213, 213]]], dtype=uint8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6ytffIVy7Cy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xxb6MZrVy651",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S6c8nc5N0KRj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "a1cdd972-8660-4c3d-fbcf-3fa49e6a5311"
      },
      "source": [
        "import numpy as np\n",
        "converted_img = [np.uint8(img)]\n",
        "cv.fastNlMeansDenoisingColoredMulti(img,imgToDenoiseIndex=2,temporalWindowSize=3,templateWindowSize=7,dst=None,searchWindowSize=21,h=7)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "error",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-108244eb9f27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mconverted_img\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mcv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfastNlMeansDenoisingColoredMulti\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mimgToDenoiseIndex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtemporalWindowSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtemplateWindowSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msearchWindowSize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m21\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31merror\u001b[0m: OpenCV(3.4.3) /io/opencv/modules/photo/src/denoising.cpp:409: error: (-5:Bad argument) Type of input images should be CV_8UC3! in function 'fastNlMeansDenoisingColoredMulti'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rX_1MM181YA_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Idea of thresholding is to hold pixels between two values 0 and 1\n",
        "import cv2 as cv\n",
        "'''type:binary,binaryinv,threstrunc,threshtozero,threstozeroinv,adaptivethreshmeanc,adaptivethreshgaussian\n",
        "Simple Thesholding:\n",
        "    cv.THRESH_BINARY\n",
        "    cv.THRESH_BINARY_INV\n",
        "    cv.THRESH_TRUNC\n",
        "    cv.THRESH_TOZERO\n",
        "    cv.THRESH_TOZERO_INV\n",
        "Adaptive Thresholding:\n",
        "    cv.ADAPTIVE_THRESH_MEAN_C \n",
        "    cv.ADAPTIVE_THRESH_GAUSSIAN_C \n",
        "    '''\n",
        "class threshold:\n",
        "    def __init__(self,path,vtype='binary',thres=127,maxval=255,blockSize=11,C=2):\n",
        "        self.vtype=vtype\n",
        "        self.path=path\n",
        "        self.thres=thres\n",
        "        self.maxval=maxval\n",
        "        self.blockSize=blockSize\n",
        "        self.C=C\n",
        "    def thresholdimage(self):\n",
        "        img=cv.imread(self.path)\n",
        "        img_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "        if self.vtype is 'binary':\n",
        "            return cv.threshold(img,self.thres,self.maxval,cv.THRESH_BINARY)\n",
        "        elif self.vtype is'binaryinv':\n",
        "            return cv.threshold(img,self.thres,self.maxval,cv.THRESH_BINARY_INV)\n",
        "        elif self.vtype is 'threstrunc':\n",
        "            return cv.threshold(img,self.thres,self.maxval,cv.THRESH_TRUNC)\n",
        "        elif self.vtype=='threshtozero':\n",
        "            return cv.threshold(img,self.thres,self.maxval,cv.THRESH_TOZERO)\n",
        "        elif self.vtype=='threstozeroinv':\n",
        "            return cv.threshold(img,self.thres,self.maxval,cv.THRESH_TOZERO_INV)\n",
        "        elif self.vtype=='adaptivethreshmeanc':\n",
        "            ret,th1 = cv.threshold(img,self.thres,self.maxval,cv.THRESH_BINARY)\n",
        "            return cv.adaptiveThreshold(img_grey,self.maxval,cv.ADAPTIVE_THRESH_MEAN_C,cv.THRESH_BINARY,self.blockSize,self.C)\n",
        "            \n",
        "        elif self.vtype=='adaptivethreshgaussian':\n",
        "            ret,th1 = cv.threshold(img,self.thres,self.maxval,cv.THRESH_BINARY)\n",
        "            return cv.adaptiveThreshold(img_grey,self.maxval,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,self.blockSize,self.C)\n",
        "        else:\n",
        "            return None\n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bCj4CBLny6DI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "109f79d2-42cb-473e-f5e5-6fa9f474a837"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "thres=threshold('airplane.png',vtype='adaptivethreshmeanc')\n",
        "plt.imshow(thres.thresholdimage())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0854fff208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1kYccef2o4rTqj8hFanat61G0HDYj8nLW/HiUe15XCwdHhuae1E0pr6aNhyQY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\nYcOGY4WN9DZs2HCs8P8BbxI1rc4lu7AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qqt38hTFzdML",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "47b259e8-de56-43bb-b51f-a37fc65d27ad"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127.0, array([[[  0,   0,  24],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,  24],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[  0,   0,  24],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,  24],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[  0,   0,  24],\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [  0,   0,  24],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[  0,   0,  24],\n",
              "         [  0,   0,   0],\n",
              "         [ 34,  48, 119],\n",
              "         ...,\n",
              "         [  0,   6,   6],\n",
              "         [  0,   0,  24],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[  0,   0,  24],\n",
              "         [  0,   0,   0],\n",
              "         [ 38,  48, 112],\n",
              "         ...,\n",
              "         [  3,  10,  10],\n",
              "         [  0,   0,  24],\n",
              "         [  0,   0,   0]],\n",
              " \n",
              "        [[  0,   0,  24],\n",
              "         [  0,   0,  24],\n",
              "         [  0,   0,  24],\n",
              "         ...,\n",
              "         [  0,   0,  24],\n",
              "         [  0,   0,  24],\n",
              "         [  0,   0,   0]]], dtype=uint8))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-SzZQ6Xrzj3q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "outputId": "fef10a12-8e98-4c7e-d66f-d04db739f882"
      },
      "source": [
        "import cv2\n",
        "image=cv2.imread('airplane.png')\n",
        "cv.threshold(image,127,255,cv.THRESH_BINARY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127.0, array([[[255, 255,   0],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255,   0],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255,   0],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255,   0],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255,   0],\n",
              "         [255, 255, 255],\n",
              "         [255, 255, 255],\n",
              "         ...,\n",
              "         [255, 255, 255],\n",
              "         [255, 255,   0],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        ...,\n",
              " \n",
              "        [[255, 255,   0],\n",
              "         [255, 255, 255],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [255, 255,   0],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255,   0],\n",
              "         [255, 255, 255],\n",
              "         [  0,   0,   0],\n",
              "         ...,\n",
              "         [  0,   0,   0],\n",
              "         [255, 255,   0],\n",
              "         [255, 255, 255]],\n",
              " \n",
              "        [[255, 255,   0],\n",
              "         [255, 255,   0],\n",
              "         [255, 255,   0],\n",
              "         ...,\n",
              "         [255, 255,   0],\n",
              "         [255, 255,   0],\n",
              "         [255, 255, 255]]], dtype=uint8))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYc0N2L60YBw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}