CRE_Image_Classification.ipynb

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        "<a href=\"https://colab.research.google.com/github/TimTimich6/investa/blob/main/CRE_Image_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import tensorflow as tf\n",
        "from pathlib import Path\n",
        "import csv\n",
        "import datetime\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import seaborn as sns\n",
        "from tqdm import tqdm\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "\n",
        "import tensorflow.keras.backend as K\n",
        "from tensorflow import GradientTape\n",
        "\n",
        "from tensorflow.image import resize\n",
        "from tensorflow.keras import Input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import GlobalAveragePooling2D, Flatten, Dense\n",
        "from tensorflow.keras.metrics import Precision, Recall\n",
        "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
        "from tensorflow.keras.callbacks import EarlyStopping, TensorBoard\n",
        "from tensorflow.keras.optimizers import Adam"
      ],
      "metadata": {
        "id": "tGpT0PhIPSfG"
      },
      "execution_count": 1,
      "outputs": []
    },
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        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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        },
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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      "execution_count": 3,
      "metadata": {
        "id": "dhymwpo89Mt8"
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      "outputs": [],
      "source": [
        "# Function to return Dataframe having images path, class label and typs\n",
        "def file_dir_df(root):\n",
        "  path = []\n",
        "  label = []\n",
        "  type = []\n",
        "  for r, d, f in os.walk(root):\n",
        "    for file in f:\n",
        "      if r is not root:\n",
        "        path.append(r+\"/\"+file)\n",
        "        label.append(r.split('/')[1])\n",
        "        type.append(file.split('.')[1])\n",
        "\n",
        "  return pd.DataFrame({'path':path,\"label\":label,\"type\":type})"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = file_dir_df(r'/content/drive/MyDrive/Colab Notebooks/HackMIT/REI-Dataset')"
      ],
      "metadata": {
        "id": "JuHFER9DOxVE"
      },
      "execution_count": 4,
      "outputs": []
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      "source": [
        "saved_model_fp = r'/content/drive/MyDrive/Colab Notebooks/HackMIT/saved_models/resnet50v2_2.h5'"
      ],
      "metadata": {
        "id": "Djek0w_0KN_H"
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      "execution_count": 5,
      "outputs": []
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      "source": [
        "df.head()"
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      "execution_count": 7,
      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5860 entries, 0 to 5859\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   path    5860 non-null   object\n",
            " 1   label   5860 non-null   object\n",
            " 2   type    5860 non-null   object\n",
            "dtypes: object(3)\n",
            "memory usage: 137.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# because label column kept saying \"content\" for every image, manually set to correct label (folder name)\n",
        "for i in range(df.shape[0]):\n",
        "  df['label'][i] = str(Path(df['path'][i]).parent.absolute())[59:]"
      ],
      "metadata": {
        "id": "mvX4lAYB6vBx"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "sns.set_theme(style=\"darkgrid\")\n",
        "sns.countplot(data =df, x='label',palette=\"Set2\")\n",
        "plt.title('Image count by Label')\n",
        "df['label'].value_counts()"
      ],
      "metadata": {
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          "height": 635
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        "id": "NgfLJV95Pkqc",
        "outputId": "f0fcbd40-90b5-4a66-b751-46b12e0bc535"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "bedroom                                     1593\n",
              "kitchen                                      992\n",
              "frontyard                                    884\n",
              "livingRoom                                   852\n",
              "bathroom                                     793\n",
              "backyard                                     745\n",
              "backyard/logs/fits/20230917-032032/train       1\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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ZgLyri2vVqgWVSmVwtTCQN/L39ClilUqF1NRUg3IpKSkGp5ILQ6cToNVmFPn5RMVFoVBAqVSUdjVKnE4n5Ht3ASKiN5FKZQ4jo8KfgSxXIbBWrVowNTUtcP2TJ08AAA4ODnjw4IFBmHt2DqCDg4PB3L/U1FTcv3/fYK5gYeXk6F7p+USvKm9E2vylLrh60+h0OiQnZ0CnYxAkIipIuQqBpqam8Pb2RmxsrGT5iRMnAABOTk4AAB8fHyiVSuzfvx+dO3cGkDe6d+zYMQwZMkR8nq+vL5YtWyaZG7hv3z4olUp4e3uXRJOIXhulUgGlUokDx/5DsoxGpm1UFnjfRwOlUsEQSET0HGUqBGZmZiI6OhoAkJiYiLS0NOzbtw8A4OHhAVtbWwwdOhTdunXDqFGj8Mknn+D69euYM2cOPvzwQ9SuXRsAUL16dXTq1AmzZs2CUqlEtWrVsHz5clhZWaFbt27i/rp164YffvgBX375JQYNGoS7d+9i1qxZ6NatG+8RSG+MZG0GHiSlv7ggERHJikIoQxNnbt68iVatWuW7bt26deJcwNjYWMyePRuXLl2CtbU1PvzwQwQHB0tOFWdlZWHevHn4+eefkZ6ejsaNG2PChAmoV6+eZLtXr17Ft99+izNnzsDS0hIdOnQw2FZh5ebqkMQvXSplxsZK2NhYYkvUGVmFwCq2lugS6I7k5HROyyAiWbC1tSzSnMAyFQLfFAyBVBYwBDIEEpE8FDUEym/GOBERERExBBIRERHJEUMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQwxBBIRERHJEEMgERERkQyVqRB4/fp1hIWFoUOHDmjYsCHat2//3PIHDx6ERqPJt1xqairGjRsHDw8PuLu7Y/jw4bh3755BudOnT6Nr165wdXWFn58fVqxYAUEQiq1NRERERGVRmQqBly9fRnR0NOrUqYN69eo9t+zjx48xbdo0VKlSJd/1I0eOxPHjxzF58mTMnj0b8fHxCAoKQk5Ojljm+vXrGDBgAOzs7LB8+XL06dMHCxcuxKpVq4q1XURERERljXFpV+Bp/v7+aN26NQAgNDQU586dK7Ds8uXL8dZbb6FmzZoG5c6cOYNjx44hMjISPj4+AAB7e3sEBgZi//79CAwMBABERkbCxsYGc+fOhampKby8vJCUlIRly5ahV69eMDU1fU0tJSIiIipdZWokUKl8uercuHEDq1evxoQJE/JdHxMTA5VKBW9vb3GZg4MDHB0dERMTIynXqlUrSdgLDAyEVqvFmTNnitgKIiIiorKvTIXAl/Xdd9+hQ4cOaNCgQb7r4+LiYG9vD4VCIVnu4OCAuLg4AEBGRgZu374NBwcHgzIKhUIsR0RERPQmKlOng1/G4cOHcebMGezbt6/AMlqtFlZWVgbLra2txVPHqampAACVSiUpY2pqCnNzc6SkpLxSPY2Ny2W+pjeIkZG8j0G5t5+I6EXKVQh88uQJpk2bhmHDhsHW1ra0q1MgpVIBGxvL0q4GkaypVOalXQUiojKtXIXAtWvXQqlUol27dtBqtQCA7Oxs6HQ6aLVaVKhQAaamplCpVLhz547B81NSUmBtbQ0A4kihfkRQLysrC5mZmWK5otDpBGi1GUV+PlFxMDJSyjoIabWZyM3VlXY1iIheO5XKvEhnP8pVCIyLi8P169fh5eVlsO7dd9/F5MmT0b17dzg4OCA2NhaCIEjmBcbHx0OtVgMALCwsUKNGDYO5f/Hx8RAEwWCuYGHl5PDLh6g05ebq+D4kInqOcjVpJigoCOvWrZP85+Pjg7fffhvr1q2Dv78/AMDX1xcpKSmIjY0VnxsfH48LFy7A19dXXObr64tDhw4hOztbXBYVFQWVSgV3d/eSaxgRERFRCStTI4GZmZmIjo4GACQmJiItLU28AMTDwwP16tUzuIn0Tz/9hLt378LT01Nc5u7uDh8fH4wbNw4hISEwMzPDvHnzoNFo0KZNG7HcgAEDsHv3bowaNQrdu3fHpUuXEBkZieDgYN4jkIiIiN5oZSoEPnz4ECNGjJAs0z9et26dJOi9yPz58zF9+nSEhYUhJycHPj4+mDBhAoyN/7/JderUQWRkJGbMmIGBAwfC1tYWw4cPR//+/YunQURERERllELgD+UWu9xcHZKS0ku7GiRzxsZK2NhYYkvUGTyQ0fFYxdYSXQLdkZyczjmBRCQLtraWRbowpFzNCSQiIiKi4sEQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSERERCRDDIFEREREMlSmQuD169cRFhaGDh06oGHDhmjfvr1kfVpaGhYtWoROnTqhadOmaN68OQYPHoz//vvPYFupqakYN24cPDw84O7ujuHDh+PevXsG5U6fPo2uXbvC1dUVfn5+WLFiBQRBeG1tJCIiIioLylQIvHz5MqKjo1GnTh3Uq1fPYP2tW7ewefNmeHt7Y/78+fj222+RmpqKrl274urVq5KyI0eOxPHjxzF58mTMnj0b8fHxCAoKQk5Ojljm+vXrGDBgAOzs7LB8+XL06dMHCxcuxKpVq157W4mIiIhKk3FpV+Bp/v7+aN26NQAgNDQU586dk6yvWbMmDhw4AHNzc3FZs2bN4O/vj40bN2LixIkAgDNnzuDYsWOIjIyEj48PAMDe3h6BgYHYv38/AgMDAQCRkZGwsbHB3LlzYWpqCi8vLyQlJWHZsmXo1asXTE1NS6LZRERERCWuTI0EKpXPr46FhYUkAAKApaUlateuLTnVGxMTA5VKBW9vb3GZg4MDHB0dERMTIynXqlUrSdgLDAyEVqvFmTNnXrU5RERERGVWmQqBRaHVanH58mU4ODiIy+Li4mBvbw+FQiEp6+DggLi4OABARkYGbt++LXmevoxCoRDLEREREb2JytTp4KL4/vvvoVAo0L17d3GZVquFlZWVQVlra2vxFHNqaioAQKVSScqYmprC3NwcKSkpr1QvY+Nyn6+pnDMykvcxKPf2ExG9SLkOgdu3b8eWLVswY8YMVK9evbSrI1IqFbCxsSztahDJmkpl/uJCREQyVm5DYHR0NMLCwjBkyBB88sknknUqlQp37twxeE5KSgqsra0BQBwp1I8I6mVlZSEzM1MsVxQ6nQCtNqPIzycqDkZGSlkHIa02E7m5utKuBhHRa6dSmRfp7Ee5DIF//fUXRowYgY8//hgjRowwWO/g4IDY2FgIgiCZFxgfHw+1Wg0g7yKTGjVqGMz9i4+PhyAIBnMFCysnh18+RKUpN1fH9yER0XOUu0kzV65cwaBBg9CsWTNMmTIl3zK+vr5ISUlBbGysuCw+Ph4XLlyAr6+vpNyhQ4eQnZ0tLouKioJKpYK7u/vrawQRERFRKStTI4GZmZmIjo4GACQmJiItLQ379u0DAHh4eEAQBAwYMABmZmbo06eP5D6CFStWRP369QEA7u7u8PHxwbhx4xASEgIzMzPMmzcPGo0Gbdq0EZ8zYMAA7N69G6NGjUL37t1x6dIlREZGIjg4mPcIJCIiojeaQihDv5F28+ZNtGrVKt9169atAwD07t073/UeHh744YcfxMepqamYPn06Dhw4gJycHPj4+GDChAmoVq2a5HmnT5/GjBkz8O+//8LW1hY9evRAUFCQwe1lCiM3V4ekpPQiP5+oOBgbK2FjY4ktUWfwQEbHYxVbS3QJdEdycjpPBxORLNjaWhZpTmCZCoFvCoZAKgsYAhkCiUgeihoCy92cQCIiIiJ6dQyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQwyBRERERDLEEEhEREQkQ2UqBF6/fh1hYWHo0KEDGjZsiPbt2+dbbuvWrWjbti1cXFzw0Ucf4ciRIwZlUlNTMW7cOHh4eMDd3R3Dhw/HvXv3DMqdPn0aXbt2haurK/z8/LBixQoIglDsbSMiIiIqS8pUCLx8+TKio6NRp04d1KtXL98ye/fuxcSJExEQEICIiAi4ublh6NCh+OuvvyTlRo4ciePHj2Py5MmYPXs24uPjERQUhJycHLHM9evXMWDAANjZ2WH58uXo06cPFi5ciFWrVr3OZhIRERGVOuPSrsDT/P390bp1awBAaGgozp07Z1Bm4cKFaNeuHUaOHAkAaNasGS5duoTFixcjIiICAHDmzBkcO3YMkZGR8PHxAQDY29sjMDAQ+/fvR2BgIAAgMjISNjY2mDt3LkxNTeHl5YWkpCQsW7YMvXr1gqmpaQm0moiIiKjklamRQKXy+dVJSEjAtWvXEBAQIFkeGBiI2NhYZGVlAQBiYmKgUqng7e0tlnFwcICjoyNiYmLEZTExMWjVqpUk7AUGBkKr1eLMmTPF0SQiIiKiMqlMhcAXiYuLA5A3qve0evXqITs7GwkJCWI5e3t7KBQKSTkHBwdxGxkZGbh9+zYcHBwMyigUCrEcERER0ZuoTJ0OfpGUlBQAgEqlkizXP9av12q1sLKyMni+tbW1eIo5NTU1322ZmprC3Nxc3FZRGRuXq3xNbyAjI3kfg3JvPxHRi5SrEFheKJUK2NhYlnY1iGRNpTIv7SoQEZVp5SoEWltbA8gbxbOzsxOXa7VayXqVSoU7d+4YPD8lJUUsox8p1I8I6mVlZSEzM1MsVxQ6nQCtNqPA9QqFAkqlosD1byqdTuDtd0qQkZFS1kFIq81Ebq6utKtBRPTaqVTmRTr7Ua5CoH7+XlxcnGQuX1xcHExMTFCrVi2xXGxsLARBkMwLjI+Ph1qtBgBYWFigRo0aBnP/4uPjIQiCwVzBwsrJyf/LR6lUoJKNOYxecBHMmyhXp8Oj5AzodAyC9Prl5uoKfB8SEVE5C4G1atVC3bp1sW/fPvFWMgAQFRUFLy8v8SpfX19fLFmyBLGxsWjevDmAvHB34cIFfP755+LzfH19cejQIYwZMwYmJibitlQqFdzd3V9LG5RKBYyUSmz4+zfcTXu1eYflSbWK1ujRqAWUSgVDIBERURlQpkJgZmYmoqOjAQCJiYlIS0vDvn37AAAeHh6wtbXFsGHDMHr0aNSuXRuenp6IiorC2bNnsX79enE77u7u8PHxwbhx4xASEgIzMzPMmzcPGo0Gbdq0EcsNGDAAu3fvxqhRo9C9e3dcunQJkZGRCA4Ofu33CLybloJEbdJr3QcRERFRQRRCGZqkdfPmTbRq1SrfdevWrYOnpyeAvJ+Ni4iIwK1bt2Bvb4+vvvoKfn5+kvKpqamYPn06Dhw4gJycHPj4+GDChAmoVq2apNzp06cxY8YM/Pvvv7C1tUWPHj0QFBRkcHuZwsjN1SEpKT3fdcbGStjYWGLu8T2yCoFvq2zxlXd7JCen8xRdCdEfa1uizuBBAcfjm6iKrSW6BLrzWCMi2bC1tSzSnMAyFQLfFAyBhhgCSx5DII81IpKHooZA+V2dQEREREQMgURERERyxBBIREREJEMMgUREREQyxBBIREREJEMMgUREREQyxBBIREREJEMMgUREREQyxBBIREREJENFDoE7d+7EzZs3C1x/8+ZN7Ny5s6ibJyIiIqLXqMghcOzYsThz5kyB68+ePYuxY8cWdfNERERE9BoVOQS+6CeHMzIyYGRkVNTNExEREdFrZFyYwhcvXsTFixfFx6dOnUJubq5BOa1Wi02bNsHe3v7Va0hERERExa5QIfDgwYMIDw8HACgUCmzevBmbN2/Ot6xKpcLMmTNfvYZEREREVOwKFQK7dOmCli1bQhAEdO7cGcOHD4evr6+kjEKhgLm5OWrXrg1j40JtnoiIiIhKSKFSWtWqVVG1alUAwLp161CvXj1Urlz5tVSMiIiIiF6fIg/VeXh4FGc9iIiIiKgEvdL52t9++w3btm1DQkICtFqtwRXDCoUCBw8efKUKEhEREVHxK3IIXLlyJebMmYPKlSvD1dUVGo2mOOtFRERERK9RkUPgunXr0KxZM6xYsQImJibFWSeifCmVCiiVitKuRonT6QTodM+/LycREVFhFTkEarVatG3blgGQSoRSqYCtjQUUSvn93LWg0yEpOYNBkIiIilWRQ6CLiwvi4+OLsy5EBVIqFVAolUg5+CNyk++VdnVKjJFNVVi37g6lUsEQSERExarIIXDy5MkICgqCs7MzPvzww+KsE1GBcpPvIedBYmlXg4iIqNwrcggcOXIkcnJy8PXXX2Py5MmoXr06lM+cqlMoFNi1a9crV5KIiIiIileRQ2ClSpVQqVIl1KlTpzjrQ0REREQloMgh8IcffijOehARERFRCeKP+xIREVG5wduFFZ8ih8A//vjjpcq9++67Rd0FERERkUipVMDGxsLgGgQ50Ol0SC7m24UVOQT26tULCsWLk/i///5b1F0QERERifJGAZW4czEa2RkppV2dEmNiYY3qDd4r9tuFvdIvhjwrNzcXiYmJ2LJlC3Q6HUaNGvVKlSMiIiJ6VnZGCp6kPyztapR7RQ6BHh4eBa7r2LEjPvvsM/zvf/+Dl5dXUXdBRERERK/JazmprlQq0a5dO2zduvV1bJ6IiIiIXtFrm1mZkpKC1NTU17V5IiIiInoFRT4dfOvWrXyXa7VanDp1CpGRkWjatGmRK/Y8hw4dwrJly3DlyhVYWlqiSZMmGD16NGrVqiUpt3XrVqxcuRK3bt2Cvb09goOD4efnJymTmpqK6dOn4+DBg8jOzkaLFi0wYcIEVK1a9bXUnYiIiKgsKHII9Pf3L/DqYEEQ4ObmhilTphS5YgU5efIkhg4dio8//hjBwcF49OgRFixYgP79+2P37t2oUKECAGDv3r2YOHEiBg8ejGbNmiEqKgpDhw7Fhg0b4ObmJm5v5MiRuHLlCiZPngwzMzPMnz8fQUFB2L59O4yNeRtFIiIiejMVOeVMmzbNIAQqFAqoVCrUrl0b9evXf+XK5Wfv3r146623JPu3tbVFnz59cO7cOXH0ceHChWjXrh1GjhwJAGjWrBkuXbqExYsXIyIiAgBw5swZHDt2DJGRkfDx8QEA2NvbIzAwEPv370dgYOBraQMRERFRaStyCOzYsWNx1uOl5eTkwNLSUhJAraysAOSNQAJAQkICrl27hjFjxkieGxgYiFmzZiErKwumpqaIiYmBSqWCt7e3WMbBwQGOjo6IiYlhCCQiIqI3VrFcGHLlyhVER0cjOjoaV65cKY5NFqhjx464evUqNmzYgNTUVCQkJGDu3Llo2LAhGjduDACIi4sDkDeq97R69eohOzsbCQkJYjl7e3uDEU0HBwdxG0RERERvolea9Hbw4EHMmDEDiYmJkuU1a9ZEaGgoWrVq9UqVy0/Tpk0RHh6OUaNG4ZtvvgEAODo6YuXKlTAyMgKQd2UyAKhUKslz9Y/167VarTiK+DRra2ucO3fuleppbJx/vjYykt9P3TytqO1nvxW+/ewzebef6E0k9/d1cbe/yCEwOjoaw4cPx1tvvYXg4GDUq1cPAHD16lVs2bIFw4YNw7Jly+Dr61tslQWA06dP4+uvv0aXLl3QsmVLPHr0CEuWLMHAgQOxceNG8cKQ0pT324aWpV2NMkmlMi/tKpRL7LfCY58R0ZumuD/XihwClyxZAo1Ggw0bNsDCwkJc3qpVK/Ts2ROfffYZFi9eXOwhcOrUqWjWrBlCQ0PFZW5ubmjZsiV+/vlndO3aFdbW1gDybv9iZ2cnltNqtQAgrlepVLhz547BPlJSUsQyRaHTCdBqM/JdZ2SklPWXk1abidxcXaGfx34rfL+xz4p2rBGVJIVCAaUy/zttvMl0OkGcx18Y/FzL/3NNpTIv0ihhkUPgf//9h+DgYEkA1LOwsMAnn3yCefPmFXXzBbp69arBaebq1avDxsYGN27cAJA3pw/Im/On/7f+sYmJiXg/QQcHB8TGxkIQBMm8wPj4eKjV6leqZ04Ov3zyk5urY98UAfut8NhnVNYplQrY2lSAQmlU2lUpcYIuF0nJmdDpCh8E5ay4P9eKHALNzMzEuXX5SUlJgZmZWVE3X6C33noLFy5ckCxLTExEcnIy3n77bQBArVq1ULduXezbtw+tW7cWy0VFRcHLywumpqYAAF9fXyxZsgSxsbFo3rw5gLwAeOHCBXz++efFXnciIiI9pVIBhdIIyVdnISczobSrU2KMzWvBpt7XUCoVDIGlrMgh0NPTE+vWrUOLFi3g7u4uWff333/jhx9+kNx6pbh069YN06ZNw9SpU+Hv749Hjx5h6dKlqFy5MgICAsRyw4YNw+jRo1G7dm14enoiKioKZ8+exfr168Uy7u7u8PHxwbhx4xASEgIzMzPMmzcPGo0Gbdq0Kfa6ExERPSsnMwHZGVdLuxokQ0UOgWPGjEG3bt3w2WefwdXVVbwdS3x8PM6ePYvKlStj9OjRxVZRvd69e8PU1BQ//vgjtm/fDktLS7i5uWH+/PmwsbERy7Vv3x6ZmZmIiIjAihUrYG9vj/DwcIPAOn/+fEyfPh1hYWHIycmBj48PJkyYwF8LISIiojdakZNOrVq1sGvXLixfvhwxMTGIiooCkHe6tnfv3hg4cCAqV65cbBXVUygU6N69O7p37/7Csp07d0bnzp2fW8bKygrTpk3DtGnTiquKRERERGVekUNgTk4OzMzMMG7cOIwbN85gfVpaGnJycjiiRkRERFQGFfmug1OnTkW3bt0KXN+9e3fMmDGjqJsnIiIioteoyCHwt99+Q9u2bQtc37ZtW8TExBR180RERET0GhU5BN67dw/VqlUrcH3VqlVx9+7dom6eiIiIiF6jIofASpUqIT4+vsD1V69eRcWKFYu6eSIiIiJ6jYp81UaLFi2wadMmfPjhh2jYsKFk3fnz57FlyxZ88MEHr1xBIqKSplTK96e8ePNeIvkocggcMWIEfvvtN3Tu3Bn+/v6oX78+AODy5cs4cuQIbG1tMWLEiGKrKBFRSVAqFbCxsYBSWeQTJeWWTqdDcnIGgyCRTBQ5BFarVg3bt2/HnDlzcOjQIRw4cAAAULFiRXz44YcIDg5+7pxBIqKyKG8UUIk7B/9FdnJGaVenxJjYWKB6a0f+lBeRjLzSTfyqVq2KmTNnQhAEJCUlAQBsbW2hUMjvNAoRvVmykzPw5EFaaVeDiOi1KZY7OSsUitfy6yBERERE9HrIb9ILERERETEEEhEREckRQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDDEEEhEREckQQyARERGRDJXbEPjTTz/h448/houLCzw9PfH555/j8ePH4vrDhw/jo48+gouLC9q2bYvt27cbbCMrKwszZ86Et7c33Nzc0K9fP8TFxZVkM4iIiIhKRbkMgUuXLsW3336LwMBAREZG4ptvvkHNmjWRm5sLADh16hSGDh0KNzc3REREICAgAOPHj8e+ffsk25k6dSq2bt2K4OBgLFq0CFlZWejbty9SU1NLo1lEREREJca4tCtQWHFxcQgPD8eSJUvw3nvvicvbtm0r/nvp0qVwdXXFN998AwBo1qwZEhISsHDhQnzwwQcAgDt37mDbtm2YNGkSOnXqBABwcXGBn58fNm3ahKCgoBJsFREREVHJKncjgTt27EDNmjUlAfBpWVlZOHnypBj29AIDA3H16lXcvHkTAHDs2DHodDpJuUqVKsHb2xsxMTGvrwFEREREZUC5C4F///031Go1lixZAi8vLzg7O6Nbt274+++/AQA3btxAdnY2HBwcJM+rV68eAIhz/uLi4lC5cmVYW1sblOO8QCIiInrTlbvTwffv38e5c+dw6dIlTJo0Cebm5li2bBn69++P/fv3IyUlBQCgUqkkz9M/1q/XarWwsrIy2L5KpRLLvApj4/zztZFRucvdxaqo7We/Fb797DMea0Uh9/aXJLn3NT/XCq+421/uQqAgCMjIyMCCBQvQoEEDAECjRo3g7++P9evXw8fHp5RrCCiVCtjYWJZ2Ncoklcq8tKtQLrHfCo99VjTsNyopPNYKr7j7rNyFQJVKhUqVKokBEMiby9ewYUNcuXIF7dq1AwCDK3y1Wi0AiKd/VSoV0tLSDLav1WoNThEXlk4nQKvNyHedkZFS1ge+VpuJ3FxdoZ/Hfit8v7HPeKwVRVH7jQqPxxo/1wqroD5TqcyLNEpY7kJg/fr1cePGjXzXPXnyBLVr14aJiQni4uLQokULcZ1+np9+rqCDgwMePHiAlJQUSeiLi4szmE9YFDk5/BDNT26ujn1TBOy3wmOfFQ37jUoKj7XCK+4+K3cn1/38/PDo0SP8+++/4rLk5GScP38eTk5OMDU1haenJ3799VfJ86KiolCvXj3UrFkTAODj4wOlUon9+/eLZVJSUnDs2DH4+vqWTGOIiIiISkm5Gwls3bo1XFxcMHz4cAQHB8PMzAwrVqyAqakpPvvsMwDAF198gd69e2Py5MkICAjAyZMnsWfPHsybN0/cTvXq1dGpUyfMmjULSqUS1apVw/Lly2FlZYVu3bqVVvOIiIiISkS5C4FKpRIrVqzA9OnTERYWhuzsbDRt2hQbNmyAnZ0dAKBp06ZYtGgR5s+fj23btuGtt97C1KlTERAQINnWhAkTYGlpiTlz5iA9PR2NGzfG6tWr871qmIiIiOhNUu5CIADY2tri+++/f26ZVq1aoVWrVs8tY2pqipCQEISEhBRn9YiIiIjKvHI3J5CIiIiIXh1DIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyRBDIBEREZEMMQQSERERyVC5D4Hp6enw9fWFRqPBP//8I1m3detWtG3bFi4uLvjoo49w5MgRg+enpqZi3Lhx8PDwgLu7O4YPH4579+6VVPWJiIiISkW5D4FLlixBbm6uwfK9e/di4sSJCAgIQEREBNzc3DB06FD89ddfknIjR47E8ePHMXnyZMyePRvx8fEICgpCTk5OCbWAiIiIqOSV6xB49epVbNy4EcOGDTNYt3DhQrRr1w4jR45Es2bN8M0338DFxQWLFy8Wy5w5cwbHjh3Dd999h8DAQLRq1QoLFizAf//9h/3795dkU4iIiIhKVLkOgVOnTkW3bt1gb28vWZ6QkIBr164hICBAsjwwMBCxsbHIysoCAMTExEClUsHb21ss4+DgAEdHR8TExLz+BhARERGVknIbAvft24dLly7hyy+/NFgXFxcHAAbhsF69esjOzkZCQoJYzt7eHgqFQlLOwcFB3AYRERHRm8i4tCtQFJmZmZgxYwaCg4NRsWJFg/UpKSkAAJVKJVmuf6xfr9VqYWVlZfB8a2trnDt37pXqaGycf742Miq3ubtYFLX97LfCt599xmOtKOTe/pIk977m51rhFXf7y2UIXLp0KSpXroxPP/20tKuSL6VSARsby9KuRpmkUpmXdhXKJfZb4bHPiob9RiWFx1rhFXeflbsQmJiYiFWrVmHx4sVITU0FAGRkZIj/T09Ph7W1NYC827/Y2dmJz9VqtQAgrlepVLhz547BPlJSUsQyRaHTCdBqM/JdZ2SklPWBr9VmIjdXV+jnsd8K32/sMx5rRVHUfqPC47HGz7XCKqjPVCrzIo0SlrsQePPmTWRnZ2PgwIEG63r37o1GjRphzpw5APLm/Dk4OIjr4+LiYGJiglq1agHIm/sXGxsLQRAk8wLj4+OhVqtfqZ45OfwQzU9uro59UwTst8JjnxUN+41KCo+1wivuPit3IdDR0RHr1q2TLPv3338xffp0TJkyBS4uLqhVqxbq1q2Lffv2oXXr1mK5qKgoeHl5wdTUFADg6+uLJUuWIDY2Fs2bNweQFwAvXLiAzz//vOQaRURERFTCyl0IVKlU8PT0zHedk5MTnJycAADDhg3D6NGjUbt2bXh6eiIqKgpnz57F+vXrxfLu7u7w8fHBuHHjEBISAjMzM8ybNw8ajQZt2rQpkfYQERERlYZyFwJfVvv27ZGZmYmIiAisWLEC9vb2CA8Ph7u7u6Tc/PnzMX36dISFhSEnJwc+Pj6YMGECjI3f2K4hIiIiejNCoKenJ/777z+D5Z07d0bnzp2f+1wrKytMmzYN06ZNe13VIyIiIipz5H3DHSIiIiKZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZYggkIiIikiGGQCIiIiIZKnch8JdffsEXX3wBX19fuLm5oUOHDti2bRsEQZCU27p1K9q2bQsXFxd89NFHOHLkiMG2UlNTMW7cOHh4eMDd3R3Dhw/HvXv3SqopRERERKWm3IXANWvWwNzcHKGhoVi6dCl8fX0xceJELF68WCyzd+9eTJw4EQEBAYiIiICbmxuGDh2Kv/76S7KtkSNH4vjx45g8eTJmz56N+Ph4BAUFIScnp4RbRURERFSyjEu7AoW1dOlS2Nraio+9vLzw6NEjrF69GkOGDIFSqcTChQvRrl07jBw5EgDQrFkzXLp0CYsXL0ZERAQA4MyZMzh27BgiIyPh4+MDALC3t0dgYCD279+PwMDAEm8bERERUUkpdyOBTwdAPUdHR6SlpSEjIwMJCQm4du0aAgICJGUCAwMRGxuLrKwsAEBMTAxUKhW8vb3FMg4ODnB0dERMTMzrbQQRERFRKSt3ITA/f/75J6pVq4aKFSsiLi4OQN6o3tPq1auH7OxsJCQkAADi4uJgb28PhUIhKefg4CBug4iIiOhNVe5OBz/r1KlTiIqKQkhICAAgJSUFAKBSqSTl9I/167VaLaysrAy2Z21tjXPnzr1yvYyN88/XRkZvRO4usqK2n/1W+Pazz3isFYXc21+S5N7X/FwrvOJuf7kOgXfu3EFwcDA8PT3Ru3fv0q6OSKlUwMbGsrSrUSapVOalXYVyif1WeOyzomG/UUnhsVZ4xd1n5TYEarVaBAUFoVKlSli0aBGUyrx0bG1tDSDv9i92dnaS8k+vV6lUuHPnjsF2U1JSxDJFpdMJ0Goz8l1nZKSU9YGv1WYiN1dX6Oex3wrfb+wzHmtFUdR+o8LjscbPtcIqqM9UKvMijRKWyxD4+PFjDBo0CKmpqdi8ebPktK6DgwOAvDl/+n/rH5uYmKBWrVpiudjYWAiCIJkXGB8fD7Va/cp1zMnhh2h+cnN17JsiYL8VHvusaNhvVFJ4rBVecfdZuTu5npOTg5EjRyIuLg4rV65EtWrVJOtr1aqFunXrYt++fZLlUVFR8PLygqmpKQDA19cXKSkpiI2NFcvEx8fjwoUL8PX1ff0NISIiIipF5W4kcMqUKThy5AhCQ0ORlpYmuQF0w4YNYWpqimHDhmH06NGoXbs2PD09ERUVhbNnz2L9+vViWXd3d/j4+GDcuHEICQmBmZkZ5s2bB41GgzZt2pRCy4iIiIhKTrkLgcePHwcAzJgxw2DdoUOHULNmTbRv3x6ZmZmIiIjAihUrYG9vj/DwcLi7u0vKz58/H9OnT0dYWBhycnLg4+ODCRMmwNi43HULERERUaGUu7Rz+PDhlyrXuXNndO7c+bllrKysMG3aNEybNq04qkZERERUbpS7OYFERERE9OoYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAgFcvXoV/fr1g5ubG7y9vTFr1ixkZWWVdrWIiIiIXhvj0q5AaUtJSUGfPn1Qt25dLFq0CHfv3sWMGTPw+PFjhIWFlXb1iIiIiF4L2YfATZs2IT09HeHh4ahUqRIAIDc3F1OmTMGgQYNQrVq10q0gERER0Wsg+9PBMTEx8PLyEgMgAAQEBECn0+H48eOlVzEiIiKi10j2ITAuLg4ODg6SZSqVCnZ2doiLiyulWhERERG9XrI/HazVaqFSqQyWW1tbIyUlpUjbVCoVsLW1zHedQpH3/6CmrZAr6Iq0/fLISJH394a1tTkEofDP1/dbpXYDIOhyi7FmZZtCaQSgaP2m77P2/k7Q6YrQ6eWUUpnX8Fc91mq0cwFk1G94xX4D/r/v5KSofQX8f3/Zar4FhJziqVB5oMiLHq/yuVbD+X1ARt+heMF3qP5zr7BkHwJfB4VCASOj578gVmbmJVSbskWpfLXBZ6VFxWKqSfnyKv1mUcG0GGtSfrzqsWZswX6jkmFkUqm0q1AqXuVYMzbld2ixbK9Yt1YOqVQqpKamGixPSUmBtbV1KdSIiIiI6PWTfQh0cHAwmPuXmpqK+/fvG8wVJCIiInpTyD4E+vr64sSJE9BqteKyffv2QalUwtvbuxRrRkRERPT6KAThVaa1ln8pKSlo164d7O3tMWjQIPFm0R9++CFvFk1ERERvLNmHQCDvZ+O+/fZbnDlzBpaWlujQoQOCg4NhairPieFERET05mMIJCIiIpIh2c8JJCIiIpIjhkAiIiIiGWIIJCIiIpIhhkAiIiIiGWIIJCIiIpIhhkAiIiIiGWIIJCIiIpIhhsAyIDQ0FO3bty9wfa9evTBo0CDx8cmTJ7Fs2bIi7Uuj0SAyMrJIzy0ta9asQcuWLeHo6IghQ4a89v3dvHkTixYtwt27d1/7vl7Gv//+C41Gg5MnT770c150TL2qDh06IDQ0tMT297TXdQy/ahu+++47+Pv7GyxPS0uDs7MzTp06hZMnT0Kj0ZT6+3Du3Lno06eP+HjWrFnw8fGBRqNB48aNsWPHDmg0GiQlJQEAtFotFi1ahCtXrhR5n926dcPixYuRlpaGRYsWoVOnTmjatCmaN2+OwYMH47///jN4TmpqKsaNGwcPDw+4u7tj+PDhuHfvnqTMpk2b0L9/f3h7e6Nx48bo0qULDh48aLCt0aNHo02bNnBzc8O7776LHj164NixY0XaJwBs374dH3zwAZydnfH+++/jhx9+MCgTFRWFYcOGwdfXFxqNBp9++qnBMbZo0SLxmHj2v4J+terp74CsrCzMnDkT3t7ecHJygqOjI+Li4iTlo6Oj0bNnTzRr1gzOzs5o1aoVpk+fjtTUVLFMbm4uIiIi0KNHD3h6esLDwwO9evXCqVOnDPb/9D7d3NzQr1+/Ytmns7Mz3NzccOrUKYNjEAAEQUCLFi2wc+fOfI/JmzdvQqPRYN++ffn228vIb7/PrtP/16RJE3Ts2BE7d+4s8v5KG0NgOTBp0iSEhISIj//3v/9h+fLlpVijknPt2jXxZ/w2bNiAMWPGvPZ9JiYmIjw8PN8PfqLCOn78OCwtLeHu7l7aVREdOXIEfn5+AIATJ04gMjISn3/+OQICAlCxYkW0bNkSmzdvhkqlApAXAsPDw4scApOSkvD333/Dz88Pt27dwubNm+Ht7Y358+fj22+/RWpqKrp27YqrV69Knjdy5EgcP34ckydPxuzZsxEfH4+goCDk5OSIZZYtW4a33noLkydPFgPVl19+iZ9++kmyrezsbPTt2xdLlizBrFmzUKlSJQwcONAg5LzMPqOiojBu3Di0aNECy5cvR/v27TF9+nSsX79esq19+/YhISEBLVu2LLBvOnfujM2bN0v+Gz16NIC837bPz9PfAVOnTsXWrVsRHByMgIAAAEDfvn0lYevRo0dwdXXFlClTEBkZiX79+mHnzp0YMWKEWObx48dYsWIFnJycMHPmTMyePRvW1tbo3bs3YmNjJft/ep+LFi1CVlZWseyzadOmUCqV6N27NywtLSXHIACcP38eDx48wHvvvZfvMVm1alVs3rwZzZo1K7C/X+TZYz8/K1euxObNmzF79mxUqlQJISEh2Lt3b5H3WZqMS7sC9GL169cv7SqUmvj4eAiCgC5duqBWrVr5lnn8+DEqVKhQwjUrXllZWTA2NoZS+Wb8XSYIArKzs9+4n14syrF29OhR+Pj4wMjI6DXVqnDi4uJw6dIlMZjoR3B69+6NxMREGBsbw9bWFra2tsW2z+joaNjZ2aFhw4bIyMjAgQMHYG5uLq5v1qwZ/P39sXHjRkycOBEAcObMGRw7dgyRkZHw8fEBANjb2yMwMBD79+9HYGAggLzRmafr6u3tjcTERKxatQqffPKJuHzBggWSOvn6+qJVq1b4+eef0bRp00Ltc+HChWjTpg3Gjx8v7lM/MtW1a1eYmJgAAObPny++pzdv3pxv31SvXh3Vq1eXLNu0aROsra0LDIF6d+7cwbZt2zBp0iR06tQJt2/fhpmZGVJTU7Fp0yYEBQUByBu5f5qnpydMTU0xceJE3L17FzY2NjA1NcXBgwdhbW0t6cv27dtj7dq18PLyynefAODi4gI/P79C7bNatWqoUKGCZJ/79u3D/fv3odPp8NNPPxmc8Tp69CgaNWoEGxsbpKenG/SHqakp3NzcDJYX5n37Mse+k5OTWMbT0xMtW7bEjh070K5du5faR1nyZnzjvGF0Oh3Gjx8PT09P/PPPP5LTwYsWLUJ4eDgyMjLEIelevXqJz7169SqGDh0KDw8PNGrUCB999BH27NljsP1FixahefPm8PT0xNixY5GRkSEpc+fOHYwePRqenp5wdXVFjx49cO7cOUkZf39/fPPNN9iwYQP8/PzQpEkTDBkyJN9h9KIIDQ3F4MGDAQCtW7eGRqMRh+OPHj2K4cOHo3HjxuJflomJiRg+fDiaNGkCNzc3DBgwwOAU04vqfPLkSfTu3RsA0KlTJ7GPs7Oz4e3tjXnz5hnUc+TIkeKHYUZGBr755hu0bdsWjRo1gr+/P8LCwiR/IT9dj4iICPj5+cHV1RWPHj0CACxZsgTe3t5wd3fH0KFD8fDhwyL3YXR0NNq3bw8XFxd07NgRf/31l2T9jh078OGHH8LFxQUtWrTAvHnzkJubKylz+vRpdOzYES4uLmjfvj2io6MN9vPnn3/i+vXrCA8Ph4uLCxo0aICAgAD89ddf2L9/Pzp06ICGDRvCyckJLi4uaNq0KXr16oWzZ88CkL52rq6uaN68OZo0aSI5hvV9BgBLly6Fu7s7QkNDERcXB19fXzRt2hQuLi7QaDQ4cuSIpH4pKSlwdXXFhg0bAOR92Q8ePBg+Pj5wc3NDhw4dcOPGDUmfOTk5QaPRYNWqVeKx1rVrV3Tv3h1NmzYVT701a9YMERERkv3p34fvvvsuduzYgT///NPgfaj35MkTjBs3Do6OjmjQoAFatWoFjUaDa9euScqtXr0aGo0GTk5O6Nevn/heaNq0qdiGadOmoWPHjnB3d0fTpk3Rtm1bg/fL8OHDYW9vD3Nzc3h6euLbb78FADg6OmLdunV48uSJ5JTYzZs30apVKwDAiBEjxPfEzZs3AQArVqzA+++/DxcXFzRr1gx9+/ZFQkKCpO5HjhwRQ6eFhYUkAAKApaUlateuLRl9j4mJgUqlgre3t7jMwcEBjo6OiImJEZfl94Xt6Oj4wpF8IyMjWFlZITs7u1D7zMzMxLVr1yRlAMDHxwePHj2SvMcK+qPuee/LrVu34ueff0ZmZia8vb0l7xNA+h3w3nvvITc3V3I6UqFQwNXVFcuWLUOjRo3Qvn17/Pbbb5L9+/v7Y//+/QDyPudcXV2RmpoKKysrLFmyBP7+/nB2dka7du1gbm4u6ctjx44hNzcXW7duhaurKzw9PTFz5kx4eHiIfaQ/Lbtz506EhYWhadOm8PLywunTpwEAv/76K9q2bYt3330X48ePh1arldRfo9Hgv//+k5yW9ff3x4YNG+Dv71/gMXnq1Cnx3zt27MCECRPQoEED+Pr6YsOGDfDy8kLDhg3h7OwMd3d3dO7cWazz5cuX0aNHD/F9/+OPP2LIkCHo0qULNBoNfv75Z/z8888AgPbt22PmzJnIycmBhYUF6tSpg1u3bkn6+I8//kC3bt3EPho7dqz4Ga/36NEjjB07Vvye7datG/744w9JGf33/549e9CmTRs0atQIgwcPRkpKChITEzFgwAC4u7ujXbt2hZoypMcQWMbk5ORg9OjRiI6Oxg8//AAXFxfJ+s6dO6NTp06oUKGCeOpg0qRJAPJOnXbt2hXXr1/H+PHjsXTpUnTs2NHg4NywYYN4mvXLL7/E7t27sWTJEnF9SkoKPvvsM1y8eBETJ07EokWLYG5ujj59+hgEksOHD+Pw4cMICwvD+PHj8ccff4hfKq9qyJAh4mmR8PBwbN68WfzymDhxImrVqoXFixejf//+SEtLQ69evXDhwgVMmTIF33//PZKTk9GzZ0/cvn37pevs5OQkzsOZPn262McmJib45JNPsHPnTuh0OnFbjx49wqFDh8QQ+PjxY+Tm5iI4OBgREREYMWIE/vjjj3znMu7fvx9Hjx7F+PHjsWTJElhYWGD9+vVYsGABPvroIyxcuBC1atUSRxsK6/79+5gyZQoGDBiA+fPnw9TUFAMGDBBfw9WrV2PChAnw8fHBsmXLEBQUhHXr1kmC7v379zFgwACYmppi/vz5GDBgAKZMmZLvfMns7GwsWbIEH330EUaMGAGVSoU+ffpg2LBhqF+/Ptq1a4fWrVtDEAS88847qFGjBnr06IHz58+Lr93QoUOhVCqRlpaGnJwcTJ06VXIMHzp0CADQtm1bjBo1Crt370bHjh2Rnp6OMWPGYOHChTA2NkZISIjkddIHsA8//BAAcOvWLTRu3Bjfffcdli5dijZt2uD06dNITEwU+2zo0KEA8ubLValSBYsXL4ajoyM+/vhjVKlSBRYWFmjUqBFSU1OxZ88e/PrrrwCk78NevXrByMgI3bp1M3gf6n3xxRfYvn073nnnHSxZsgSNGjUCAMyZM0csc+jQIcyYMQNKpRJz586Fl5eXuP7TTz/F0qVL4eHhgbVr18LMzAzh4eGYN28ePD09AUjfLxUqVICfnx+GDBkChUIhhpl33nkHNjY24iiWXtWqVREeHg4A+Oqrr8T3RNWqVbFz504sWLAAnTp1wsqVKzF16lQ4OjpKRmmys7Nx7Ngx8fRzfrRaLS5fvgwHBwdxWVxcHOzt7aFQKCRlHRwcDOafPevPP/+UbEtPEATk5OQgOTkZkZGRuH79Orp27VqofWZlZUEQBINRbv3jZ09pP+tF78vjx49Dp9MhODgYs2fPFt8n8fHxAKTfAR9++CGsra0ln7nZ2dm4dOkSgLzPTVtbWwwfPhzJycnIzc3FkydPkJWVhRMnTsDa2hqTJk0SP39mzZqF8PBwfPLJJ1i2bBmaN2+O8+fPS/rjf//7HwDA2toaCxYswOjRo3HkyBFcuHDBoO3z58+HmZkZZs+eDQ8PD/z000+oU6cO9u7dizFjxiAsLAy///47vv/+e8lr9Pfff6Nq1aqSbbVs2RJJSUlo0aKF5JisWLEi2rVrh82bN6Ny5cpi+blz50IQBNja2sLIyAiHDx/G+++/j4CAAJiYmMDZ2RmNGzfGwIED8dtvv6F///549OgRunTpAgBYt24dzp8/L2mLPtR/8sknWLVqFbZu3QqdToc7d+6gZs2aYtlz586hX79+sLS0lPRRUFCQ+Ed2bm4ugoKCcOTIEYwePRoLFiyAhYUF+vXrZzDgcuHCBaxbtw5ff/01pkyZglOnTmHixIkYPnw4WrZsiUWLFsHW1hbDhg3Ld4T0uQQqdSEhIUK7du2EJ0+eCIMHDxZatmwpxMfHi+t79uwpDBw4UHy8cOFCwc3NzWA7X331ldCsWTMhNTW1wH2p1WqhU6dOBvtv3bq1+HjBggVCkyZNhAcPHojLnjx5IrRs2VKYOXOmuMzPz0/w9fUVnjx5Iqmbk5OTkJub+3KNf4EDBw4IarVaSEhIEARBEH7//XdBrVYLYWFhknJr164VNBqNcOXKFXFZcnKy4ObmJkyfPr1Qddbv4+zZs5J9XLt2TdBoNMLRo0fFZevWrRNcXV0L7PPs7Gzh1KlTglqtFuLi4iT18PDwENLT08VlOTk5go+PjzBmzBjJNsaMGSOo1Wrh999/f35nPSUkJERQq9XCiRMnxGVarVZwd3cXZs+eLaSmpgpubm7CnDlzJM/buHGj4OrqKiQlJQmCIAjff/+94O7uLmi1WrHMiRMnBLVaLYSEhIjLWrduLajVamH9+vWS/TVo0EBo0aKFZB+bNm0S1Gq1cP78eaFt27ZC3759xddOfwwnJCQU+Nqp1Wph5cqVwr///is4OzsLarVaOHLkiFhu5MiRglqtFs6dOycu++STT4Svvvoq377S6XRCdna20KZNG0mf6Y+Dhg0bCrNnzxbLR0dHC2q1Wjh+/LiQnZ0ttG3bVpg2bZrg7u4u+Pn5Sd6Hc+fOFXr27Ck+V79NfRt++eUXQa1WC507dxZycnLEci1atBAaNmwoLvv0008Fd3d3SRsWL14sqNVqYfv27YJOpxP27Nlj8P5+9v2Snp4uODs7CytWrBDbunr1akGtVovHh5+fn7B9+3ZBrVYLDx8+FARBEBISEgS1Wi388ssvkr6bMmWK8Mknn+Tbr3rHjx8XXF1dhczMzALLTJgwQXBzcxNu374tLuvbt6/Qv39/g7JTpkwR2rRpU+C2du3aJajVauHAgQMG67Zs2SL2v5ubm3Dw4EHJ+pfdp4eHhzBp0iRJmfDwcEGtVgvLli3Lt15qtVro2LHjc9+XgiAIX375pdCiRQshNzdXyM3NFY+xp9+r+u+A8ePHC23btpUsV6vVwtixYwUnJydBEP7/tdu5c6fQokULsf0NGzaUfMY/fPhQcHJykhzrS5cuFTQajdC0aVPxWPTz8xMcHR2FrKwssdxvv/0mqNVqwdHRUbLPESNGSPbp5OQkNGrUSPx8EQRBmDFjhtC0aVNBEPI+t7y8vARHR0dhwYIFkmNQ37Zjx45J9vH0Z/XTywYMGCDWt6DP/CdPngj9+/cXOnbsKDg6OgoJCQnisX/u3DnB0dFR6Ny5s6BWq4Xhw4eL6+7fvy/06NFD6N69u/Ddd98Jzs7Owh9//CFu/8svvxRatmyZbx8dOnRIEARBOHjwoKBWq4WYmBixTFZWltCyZUth6NCh4rKePXsKbm5uYj/o+0ytVgsbN24Ul/33338FHvfPw5HAMuLx48cYNGgQ4uLisGHDBtStW7fQ2/j999/Rtm1bVKxY8bnlmjdvLnlcr1493LlzR3x8/PhxeHp6wtraGjk5OcjJyYFSqcS7776Lf/75R/Lcd999V/IXcb169ZCdnf1KpzBfxrMTrU+dOoV33nkH9erVE5dVqlQJzZs3x59//lksda5Tpw48PDywfft2cdmOHTsM+nznzp34+OOP4e7uDicnJ3z22WcAYHB6z9PTExYWFuLjO3fu4N69e3j//fcl5dq2bfvcehXEyspKnMejf9y8eXP8/fffOHPmDDIyMvDBBx+Ir3FOTg6aN2+Ox48f4/LlywCAv//+G56enrCyshK34+XlhUqVKuW7zx49eoj/ViqV0Ol0MDMzA5A3QvLll19i/vz5APL+mo6Pj8fVq1fF105/DNesWbPA1w6AOE2ifv36UCgUkmNa/5e8ftTw4sWLOH/+vDhaC+SNdk+dOhV+fn5wcnKCk5MTrl27BoVCIekzAHB2dsbff/8ttmH69OlQKBTo168fnJycEB8fj9u3b4t1ePp9ePTo0QJHwP755x/xgq/ly5dL5gx++umnyMnJwcGDB5Gbm4sLFy4gPT1d0gb9KN/MmTPh5OSEr776CkDeqMHhw4clUxD075cTJ06gQoUKyMzMLPD4KIyGDRviwoULmD59Ok6dOiU5tap35MgReHl5FTgna/v27diyZQvCwsIM5sYV1sWLFzFp0iR07NgRrVu3NljfqlUrbNu2DREREQgICMDIkSPznd7wIp999hl27NiB3bt3IyUlBUeOHMG6desAwGAU8VnPe19qtVocPXoUZmZm8PHxgaOjo3iMxcXFie9TQRAK3L5SqZR8f9SsWRMVKlTA3bt3sWLFCmzatAmVKlWCsbExgoODxZGps2fPIjs7Gx988AGAvO+BRYsWITAwEFqtVvz8un//PiwtLSUjxj4+PjAzMzOol7e3t7jPqVOnQqFQwMjISHLRRd26daHVapGeno579+7h4cOHGDJkiGRkDci7S4K1tbXBBRh2dnYGZ8wA6XeE/jP/zp07CAkJwbp165CdnQ0XFxccO3YMN2/ehFqtluyzRo0aaNCggaSNT7frjz/+wJ9//om1a9di3Lhx4rxSIO/7qFWrVgZ9pFKpxM+0U6dOoWLFimjRooVYxsTEBO+//77B516DBg0k0x70r+/T71f9sqe/y18GQ2AZkZSUhD/++APvvfce3nrrrSJt49GjRwZD6Pl59qonExMTZGVliY+Tk5Nx8OBB8ctR/9/PP/9scIA9uy19uHry5EmR2vCynh72B/JOJ1WpUiXfcikpKZJlr1LnLl264PDhw0hKSsLFixdx4cIFfPrpp+L6AwcOICQkBK6urpg/fz62bNmCxYsX57v9Z9tw//59AIZznPJr18vIb65U5cqVcf/+fSQnJwPIC2JPv8Zt2rQBAPEU+v379w3qWdC2nz2NqA8hjx8/RlpaGvr3749bt25h7NixMDY2Rvfu3dGgQQM8efJEbOPTx/DzXrvY2FhkZmbCyckJ5ubmklCvv4BIPw9q+/btqFmzpuSKwdDQUOzZswf9+/dHZGQktm3bhjp16uTbj3Z2drh//77YhuTkZFSuXBkbNmzAtm3bxDbo+0nfhtu3b+PixYsFXhkaGxsrXhBkY2MjWae/GGzr1q1ISkpCbm4ubG1tJW3QT+Fo0aKF2AZvb2/odDoMHToUXl5e4ql9fd0OHz6MFi1a4MGDBwUeH4XRsWNHjB07FseOHUOPHj3g5eWFqVOn4vHjx2KZo0ePFtgH0dHRCAsLw5AhQyQXcQB5r3VaWprBc1JSUiQXL+glJiYiKCgIrq6u4tzRZ9na2sLFxQW+vr6YNm0afH19JaciX3afgwYNwvvvv48xY8bAw8MDX331FYYPHw4g73h5nue9L3ft2oXs7GwYGxsjNDRUPMZq1KiBAwcOiO9T/R84+dW3QoUKSE9Pl9TXxMQET548QYMGDeDu7g5LS0v4+fnh5MmTOHDggNhGIO/z5vz58xg2bBjat28vzpHWz2fLzs6WXCmtZ25ubvAZYGVlJe6zc+fOUKvVSEtLE/eprxuQF0JPnjwJKysrcSqGXlZWFmJjY+Hn54cDBw4gKytLDK+NGzfOt5+fPpZVKhV0Oh2++OIL/Pnnn+JtepYsWQJfX19kZWXl+7o8vezpP4TXrFmDwMBA2Nra4p133sF3332Hixcviuu1Wm2+76WnP9MKKlOlSpUXfmfp++zpOhX1u5dXB5cRb731FoYOHYqvvvoKNjY2+OKLLwq9jUqVKhXLbU2sra3RokULyaX8emXlas9n/9q2trYW58w87eHDh/l+YRRVmzZt8O2332LXrl24efMmateuDQ8PD3H9vn374OjoKPkS0s+hedazbdB/eTx7Yc2DBw+KVNf8LtB5+PAh7OzsxD4JDw/Pd/RF/xexnZ1dviOk+W372QtK9B9Q5ubm+Ouvv3Dnzh0sX74cb7/9NnJycqBWqxETEwMTExNxH08fw8977QYMGIBz585h165dBY68nD9/Hnfv3sXu3bvRq1cvsdyTJ09w9OhRhIaGSi6qEgQh3xGWlJQU2NnZiW3o3r079uzZI/7ln5qaiurVqxu04ciRI6hTp06+c9P0bYiKisK///6LP//8E02aNBHXPXjwAAqFArGxseLomrOzs6QNx48fB5A3GqAfWdJfcfnbb78hJiZGPA4VCgUEQUBMTAy+/vpr3Lhxo8DjozCUSiX69OmDPn364O7du9i7dy/mzJkDGxsbfPnll7h69Spu3LiR72joX3/9hREjRuDjjz/O97PGwcEBsbGxEARB8hrHx8dDrVZLyiYlJWHAgAGoXLkywsPDDcJIQZycnCQXmbzsPitUqIA5c+Zg/PjxuH//PmrVqiXeqkQ/p7Mgz3tfbt26FQAwb948ySgUkBd2xo0bBwDYtWsXbty4AQcHBzx48MAgpMbFxRV43Onp53/qL4jSj+6fO3cOEydOhLu7O6ZOnSqOlOrXW1hYID093WCfaWlpLwzA5ubmUCgU4j6fFhwcDFtb23xvzaK/RU1QUBB27tyJ3377TbyY5On3zdOe/Vy4fv06Lly4IP5RvnHjRmg0Gjx+/BhGRkb5vi5JSUkwNjaMSRqNBlWqVIG5ubk4qjx79mysXLkSQN73UX7vpac/0woq8+DBg2L9znoRjgSWIR988AFmzJiBhQsXYs2aNQWWe3bkTs/Lywu//vprvn/JFkbz5s1x9epV1KtXDy4uLpL/NBrNK237dWnSpAkuXbokmTCekpKCEydOFPghURD9F0h+f1GZmpqiQ4cO2Lp1q3hRwtMfNo8fPzb4Atq9e/dL7bd69eqws7OT/JUMQLzgoLBSU1Ml9/dKTU3FiRMn0KhRI7i7u8Pc3Bx37twxeI1dXFzEkSlXV1ecPHlScmoxNjbW4Co3IO+q86f3p9PpoFQq8fjxY3FkyMTEBL/88guAvC+TxMREWFtbi6+d/hi+devWc187IyMjzJkzBzVq1MDjx4/zvVDA3Nwco0aNQkpKCjp27Cguz8rKgk6nk7xOaWlp4ujns/dEO3v2LBo1aiS2oUGDBmLf6i8mycnJwYkTJwD8//vw4MGDz70YwsjICFOnTgWQ9+X2dBv0f0xYW1vj66+/BgDJa6C/OOFpaWlpOHz4MIC8yfKBgYHiKWMg7/RzUlISfH194eLiUuDxkZ/nvSf0qlWrhv79+0Oj0YhtOXLkCBo2bIhq1apJyl65cgWDBg1Cs2bNMGXKlHy35+vri5SUFEkd4+PjceHCBcmtU9LT0xEUFITs7GysWLHihdNhnvbnn39Kbj31svvUs7W1hUajgYWFBTZs2ICmTZu+MHwV1O/169cX72bw9LF5+vRp3L59GyqVSnx/Vq5cGVlZWfDx8YFSqRSv9AXy/pg5duzYC28v8/DhQ2RnZ4t/8Lm4uIgXVdWoUQMLFy4U36+VK1cWTzfqR97072Mg7yK3nJycF96fLz09HYIgSE676sNctWrV4Onpme8fdUePHoW3tzfq168PFxcX7N27F0ePHgWAAqemPEt/7D7dt3fv3sWZM2dQoUIF/Pfff5Kr2vUj+S9So0YN9OnTB7/99hsuXLgAIO/76NChQ5IR0+PHj0Or1YqfaU2aNEFaWprkhuX6KSCF/c56FRwJLGM++ugjPHnyBGFhYahQoQK6detmUKZevXrIycnB2rVr4e7ujooVK8LBwQFDhw7F0aNH8dlnn+Hzzz+HnZ0drl69iszMTPHeTS+jb9++2L17N3r27InevXvjrbfeEm/2Wq1aNfTt27cYW1w8OnbsiDVr1mDQoEEYOXIkzMzMsHTpUhgbG0t+GeFl1K1bF0ZGRti+fTuMjY1hZGQkmXPSpUsXrF27FkZGRpJwAeQF6G+++QaLFy+Gu7s7oqOjDUJFQYyMjDBw4EB89913qFy5Mry9vXH8+PEiXfYP5H04jh8/HsOHD4eVlRUiIiIgCAL69OkDlUqF4cOH4/vvv8edO3fg4eEBIyMjJCQk4NChQ5Irwjdu3IigoCAEBQWJ90LL74NXqVQa7M/ExAR3797Fzz//DDMzM3zxxRe4ffs2nJ2dMX/+fFSrVg3Vq1dHZmYmBg0ahB49euDgwYMICAiAQqGAi4sL1q9fj8zMTIP9mZiYICAgAMuXL0ffvn2xYcMGyRe6h4cHDh8+DB8fH9SoUUNcbmVlBRcXF0RERMDW1hbGxsZYsWIFTExMkJ2dLbZBHwr1fQbkBde9e/eiTp06GDZsGJRKJaytrXH27FkxfAwdOhRHjhzBiRMn4OzsjNjYWPF96OrqKmmDs7MzWrdujcOHD6NLly4YO3YsYmNjcebMGSxZsgSnTp1CZGQkHB0dcebMGUyYMAEffPABLly4AFNTU2RlZeHcuXNQqVSYPn06srKykJOTg//973+4efOmJNQdOXIE7u7uqFSpEnx9feHk5IQxY8aIQfHzzz8vMEDZ2dlBpVJh7969qFmzJkxNTaHRaDB16lSoVCq4ublBpVLh9OnTuHjxIrp37y7u89lTwQ8fPsSAAQNgZmaGPn36SK6ErFixongq3N3dHT4+Phg3bhxCQkJgZmaGefPmQaPRiNMWAGDYsGG4ePEivvvuO9y6dUtyFbb+nnFHjx7Fzp070bJlS9SoUQMpKSnYs2cPjh07hrlz54rlX3af0dHRuHHjBurXr4+UlBTs3r0bJ0+exI8//ihp65UrVyQ3M05JSYGFhQVGjRqFr7/+WvK+rFy5MgRBgLm5OaZMmYKBAwfi7t27WLRokUGI1n8H/Prrr/Dz88P06dOhVCpx48YNPHnyBHZ2dpLvjszMTJw6dQpHjhxBhQoVkJqaiujoaGg0GnHupIWFBaysrJCcnIy6deti27ZtOHPmDH755RcEBQWJc1b1N9P+5ptvcPXqVSiVSqxfv148hf20/fv3w8zMDBUqVMDFixdx+fJlWFpaivt8/PgxVq9eDSBvZHzPnj3IzMzEX3/9JXkdjx49ii+//BJA3u1ZFixYACMjI5iZmUmOyef9AeDg4IDq1atjzpw54h9nY8aMQdWqVVGhQgUoFAoMHjxYPLMzevRoVKlS5YVzPAGgX79+WL9+PSIiIjBv3jwMHjwY3bp1w6BBg9CrVy88ePAAc+bMgaurK9577z0AeXMWXV1dMWbMGIwaNQpVqlTBDz/8gHv37mHhwoUv3GdxYQgsgzp37ownT55gypQp+U6m9vPzw2effYYVK1bg4cOHePfdd/HDDz+gbt262LRpE+bMmYMpU6YgNzcXdevWxcCBAwu1fxsbG2zevBnz58/H7Nmz8ejRI1SuXBmNGjUyuGihrKhYsSJ++OEHzJgxAxMnToROp0Pjxo2xfv16SQB4Gba2tggLC8PKlSuxa9cu5OTkSO43WL9+fdStWxe1a9c2+HDu1q0bbt68ifXr14s3nJ0zZ454scKL9OrVC1qtFhs3bsSPP/4ozrH6/PPPC9UGIO+Le/To0Zg1axZu3LiBd955B5GRkeL8u/79+6NatWpYvXq1+CFeu3ZttGzZUvxruWrVqoiIiMDUqVMxYsQI1K5dG2FhYfneL9HY2BhhYWGS/a1Zswb379/H4sWLkZOTI95bLisrC1OmTMHKlSthbGwsvnYLFy6ETqeDpaUlHj9+jPHjxz/3GNZ/EdSuXRt9+vQR7wMI5I1iHj58WDJnU2/OnDkICwtDaGgoKlWqhF69euHJkyf477//xDboJ8JPnDhR7LMFCxZg1qxZuH37NkxMTMTRwZo1a6JFixY4dOgQ6tati+DgYPEXJNatW/fcNsyZMwezZs3Cli1bMG7cONSrVw8LFy6Ev78/bGxsEBkZiYEDByIlJQXLly/Hrl270KhRI0yZMgVjx47F1q1bcfjwYfj7++PYsWO4ceMG+vfvDzs7OzRr1kwcJTp69Kh4s2OFQoElS5Zg0qRJ4oiOv78/Hjx4IM43e5pSqcT06dMxd+5c9O3bF1lZWTh06BDc3d2xZcsWbN26FZmZmahVqxbGjh2Lzp07IyUlBWfOnJH82hGQF4z0c4uf/YPSw8ND8vNr8+fPx/Tp0xEWFoacnBz4+PhgwoQJklN0+tPiz+4HgPi+rVWrFrKysjBnzhwkJyfDxsYGGo0GP/zwg2Q6x8vu09jYGNu2bcP169dhbGwMDw8PbN68WXJhGpA3Wqa/lQkAcaTJ3NwcK1askLwvv/vuO7i6umLYsGGYNWsWhgwZgrp164rvk6c9+x1QrVo1zJkzRxyhX716tWS+mJGREa5cuYJRo0ZBEARkZWWhQYMGWLt2rTjF58GDB+Jc4T179kjuaxkVFSXersvZ2RkrV65EaGioeDGMnZ0dwsPDDf44/Ouvv3D06FEIgoC3334bdnZ2cHBwkOxTfyw8/WtQXbt2Fbd15coV3L59W/xjIiAgADNnzoQgCPj222+xevVq8Zh8+v3/LFNTUyxatAjffPON2J89e/bEpUuXcO7cOaxatQqTJk0Sb+rdvXt3HDhw4KWmFlSqVAk9e/ZEREQEbty4AWdnZ6xatQpz587FsGHDYGFhAX9/f4SEhIhh2sjICCtWrMCsWbPw/fffIyMjA05OTli1ahWcnZ1fuM9iU6hriYlIuH79uqDRaIR9+/aVdlXoOebPny94eHhIbg1RUiZMmCAMHz78lbfzvDbob3eiv33S89y5c0dQq9XC5cuXX7lOL2vXrl1C8+bNBZ1OV2L7pDfP0qVLhU8//bTE95ucnCw0adJEWLRoUYnvuyQpBOE515oTkSg5ORnx8fFYvHgx4uPjsX///nwnDVPpiouLQ3x8PEJDQ/HZZ58hODi4tKtUaM+2oV+/fggPD0ezZs1gaWmJf/75B8uWLUOzZs0kN3onoqJZsWIFqlSpgrfffhv379/HqlWrEB8fj6ioqEKfTSpP+A1G9JKOHDmCcePGoU6dOvj+++8ZAMuoSZMm4a+//kKLFi3En1ssb55tg06nQ0JCAvbs2YPU1FTY2NigQ4cO4ik6Ino1SqUSS5cuxd27d2FkZIRGjRph7dq1b3QABACOBBIRERHJEG8RQ0RERCRDDIFEREREMsQQSERERCRDDIFEREREMsQQSET0inbs2AGNRiPeDPtl9erVS/wx++Li7+9v8MsNRET5YQgkIiIikiGGQCIiIiIZYggkIiIikiGGQCKiYnbw4EEMHDgQPj4+cHZ2RuvWrbF48WLk5ubmW/7cuXPo1q0bXF1d4e/vjx9//NGgTFZWFhYuXIj3338fzs7OeO+99zBr1ixkZWW97uYQ0RuKv3tFRFTMfvrpJ1hYWKBfv36wsLDA77//joULFyItLQ0hISGSsikpKRg4cCACAgLQrl07/PLLL5g8eTJMTEzQqVMnAIBOp8MXX3yBP//8E126dEG9evVw6dIlrF27FteuXePvBxNRkTAEEhEVszlz5qBChQri4+7duyMsLAw//vgjgoODYWpqKq67d+8eQkND0a9fPwBA165d0aVLF8ydOxcdOnSAiYkJdu/ejRMnTuCHH35A06ZNxee+8847mDRpEk6fPo3GjRuXXAOJ6I3A08FERMXs6QCYlpaGpKQkNG3aFJmZmYiLi5OUNTY2RteuXcXHpqam6Nq1Kx4+fIjz588DAPbt24d69erBwcEBSUlJ4n/NmjUDAJw8ebIEWkVEbxqOBBIRFbPLly9j/vz5+P3335GWliZZl5qaKnlctWpVWFhYSJbVrVsXAJCYmAg3Nzdcv34dV69ehZeXV777e/jwYfFVnohkgyGQiKgYabVa9OzZExUrVsTw4cNRu3ZtmJmZ4fz585g9ezZ0Ol2ht6nT6aBWqzF27Nh811evXv1Vq01EMsQQSERUjP73v//h0aNHCA8Px7vvvisuL+jXRO7du4eMjAzJaOC1a9cAAG+//TYAoHbt2rh48SK8vLygUCheX+WJSFY4J5CIqBgplXkfq4IgiMuysrKwcePGfMvn5ORg8+bNkrKbN2+Gra0tnJycAAABAQG4e/cutmzZYvD8x48fIyMjozibQEQywZFAIqJi5O7uDmtra4SGhqJXr15QKBT4+eefJaHwaVWrVkVERAQSExNRt25dREVF4d9//8W3334LExMTAECHDh3wyy+/YNKkSTh58iQaN26M3NxcxMXFYd++fVi5ciVcXFxKsplE9AZgCCQiKkY2NjZYtmwZZs6cifnz50OlUuGjjz6Cl5cXBgwYYFDe2toaM2bMwNSpU7FlyxZUqVIFYWFh6NKli1hGqVRi8eLFWLNmDX7++WccOHAA5ubmqFmzJnr16gV7e/uSbCIRvSEUQkF/nhIRERHRG4tzAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhkiCGQiIiISIYYAomIiIhk6P8ALamRxlRN/YYAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "sns.set_theme(style=\"darkgrid\")\n",
        "sns.countplot(data =df, x='type')\n",
        "plt.title('Image type count')\n",
        "df['type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "CJ4GYBz7Q0-K",
        "outputId": "17286cd2-3b4f-45e6-fa19-78bc058172ad"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "jpg     4246\n",
              "jpeg    1607\n",
              "png        5\n",
              "JPG        1\n",
              "out        1\n",
              "Name: type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7,5))\n",
        "sns.set_theme(style=\"darkgrid\")\n",
        "sns.countplot(data =df, x='label',hue ='type', palette=\"CMRmap\")\n",
        "plt.title('Image type count by Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "uWCwMhRUQ2qR",
        "outputId": "2b24de43-5d9a-4f8a-bf7c-e5c598bc70d6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Extracting height and width of images for analysis\n",
        "# height, width = [], []\n",
        "# for image in tqdm(df['path'].values):\n",
        "#   img = cv2.imread(image)\n",
        "#   height.append(img.shape[0])\n",
        "#   width.append(img.shape[1])"
      ],
      "metadata": {
        "id": "1oTs6DKIQ5Ht"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Height analysis\n",
        "# plt.figure(figsize=(7,5))\n",
        "# plt.title('PDF of Image height')\n",
        "# sns.set_theme(style=\"darkgrid\")\n",
        "# sns.distplot(height, hist= True)\n",
        "\n",
        "# for i in range(10,101,10):\n",
        "#   print(\"{}% images has height {}\".format(i,np.percentile(height,i)))"
      ],
      "metadata": {
        "id": "1RfawpXlRFKR"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Width analysis\n",
        "# plt.figure(figsize=(7,5))\n",
        "# plt.title('PDF of Image width')\n",
        "# sns.set_theme(style=\"darkgrid\")\n",
        "# sns.distplot(width)\n",
        "\n",
        "# for i in range(10,101,10):\n",
        "#   print(\"{}% images has width {}\".format(i,np.percentile(width,i)))"
      ],
      "metadata": {
        "id": "qFcIFpyCRHt7"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # visualizing 4 random images from all the classes\n",
        "# fig, ax = plt.subplots(6, 4)\n",
        "# fig.set_size_inches(20,20)\n",
        "# fig.tight_layout()\n",
        "# for i in range(len(ax)):\n",
        "#   row = ax[i]\n",
        "#   label = df['label'].unique()[i]\n",
        "#   temp_df = df[df['label'] == label]\n",
        "#   for col in row:\n",
        "#     num = np.random.randint(0,len(temp_df.values))\n",
        "#     Image = cv2.imread(temp_df.values[num][0])\n",
        "#     col.imshow(Image)\n",
        "#     col.axis('off')\n",
        "#     col.set_title(label)"
      ],
      "metadata": {
        "id": "X6lOYWumRKMD"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "msk = np.random.rand(len(df)) < 0.8\n",
        "X_train = df[msk]\n",
        "X_test = df[~msk]"
      ],
      "metadata": {
        "id": "h7OOzORe-sh7"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Image Augmentation\n",
        "datagen_train = ImageDataGenerator(\n",
        "        rescale=1./255,\n",
        "        rotation_range = 40,\n",
        "        shear_range = 0.2,\n",
        "        zoom_range = 0.2,\n",
        "        horizontal_flip = True,\n",
        "        brightness_range = (0.5, 1.5),\n",
        "        )\n",
        "\n",
        "datagen_test = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "\n",
        "train_generator=datagen_train.flow_from_dataframe(\n",
        "    X_train, x_col='path',\n",
        "    y_col='label',\n",
        "    class_mode=\"categorical\",\n",
        "    target_size=(224,224),\n",
        "    batch_size=32)\n",
        "\n",
        "test_generator=datagen_test.flow_from_dataframe(\n",
        "    X_test, x_col='path',\n",
        "    y_col='label',\n",
        "    class_mode=\"categorical\",\n",
        "    target_size=(224,224),\n",
        "    batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJ0VcqeFRrcf",
        "outputId": "e3318693-7057-47eb-c062-9f34cb2fe52d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4711 validated image filenames belonging to 6 classes.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/preprocessing/image.py:1137: UserWarning: Found 1 invalid image filename(s) in x_col=\"path\". These filename(s) will be ignored.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1148 validated image filenames belonging to 6 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Baseline Model\n",
        "# model = Sequential()\n",
        "# model.add(Conv2D(64, (3, 3), padding='same',\n",
        "#                  input_shape=(224,224,3)))\n",
        "# model.add(Activation('relu'))\n",
        "# model.add(Conv2D(32, (3, 3)))\n",
        "# model.add(Activation('relu'))\n",
        "# model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "# model.add(Dropout(0.25))\n",
        "# model.add(Conv2D(32, (3, 3), padding='same'))\n",
        "# model.add(Activation('relu'))\n",
        "# model.add(Conv2D(16, (3, 3)))\n",
        "# model.add(Activation('relu'))\n",
        "# model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "# model.add(Dropout(0.25))\n",
        "# model.add(Flatten())\n",
        "# model.add(Dense(32))\n",
        "# model.add(Activation('relu'))\n",
        "# model.add(Dropout(0.5))\n",
        "# model.add(Dense(6, activation='softmax'))\n",
        "# model.compile(optimizer= 'adam' ,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
        "# model.summary()"
      ],
      "metadata": {
        "id": "L8KdB_qoRu60"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
        "# STEP_SIZE_TEST=test_generator.n//test_generator.batch_size\n",
        "\n",
        "# import datetime\n",
        "# %load_ext tensorboard\n",
        "# log_dir = os.path.join(\"logs\",'fits', datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "# tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir,histogram_freq=1,write_graph=True)\n",
        "\n",
        "# callbacks = [\n",
        "#     tf.keras.callbacks.ModelCheckpoint('/content/drive/MyDrive/Self Case study/Real estate image/Saved models/baseline_weights.h5', save_weights_only=True, save_best_only=True, \\\n",
        "#                                        mode='max', monitor='val_accuracy'),\n",
        "#             tf.keras.callbacks.ReduceLROnPlateau(), tensorboard_callback]\n",
        "\n",
        "# model.fit(train_generator,\n",
        "#           steps_per_epoch=STEP_SIZE_TRAIN,\n",
        "#           validation_data=test_generator,\n",
        "#           validation_steps=STEP_SIZE_TEST,\n",
        "#           callbacks = callbacks,\n",
        "#           epochs = 20)"
      ],
      "metadata": {
        "id": "aLTeYS3GWu2L"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # resume traning from previous last epoch\n",
        "# model.fit(train_generator,\n",
        "#           steps_per_epoch=STEP_SIZE_TRAIN,\n",
        "#           validation_data=test_generator,\n",
        "#           validation_steps=STEP_SIZE_TEST,\n",
        "#           callbacks = callbacks,\n",
        "#           epochs = 20)"
      ],
      "metadata": {
        "id": "NTYYdRUxW0KF"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# STEP_SIZE_TEST=test_generator.n//test_generator.batch_size\n",
        "# baseline_model.evaluate_generator(generator=test_generator)"
      ],
      "metadata": {
        "id": "qu2zfMTJW3gk"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# n_batches = len(test_generator)\n",
        "# y_true = np.concatenate([np.argmax(test_generator[i][1], axis=1) for i in range(n_batches)])\n",
        "# predict  = np.argmax(baseline_model.predict_generator(test_generator, steps=n_batches), axis=1)\n",
        "\n",
        "# vocab_dict = (train_generator.class_indices)\n",
        "# plot_confusion_matrix(y_true, predict)"
      ],
      "metadata": {
        "id": "gydmOlwmW4y5"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "2Fd9kQQVJGRW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm -rf ./logs/\n",
        "\n",
        "# resnet_layer = tf.keras.applications.ResNet50V2(\n",
        "#     include_top=False,\n",
        "#     weights=\"imagenet\",\n",
        "#     input_tensor=None,\n",
        "#     input_shape=(224,224,3),\n",
        "#     pooling=None,\n",
        "#     classes=6,\n",
        "#     classifier_activation=\"softmax\",\n",
        "# )\n",
        "\n",
        "# faltten_layer = Flatten()(resnet_layer.output)\n",
        "# fc1 = Dense(256,activation='relu')(faltten_layer)\n",
        "\n",
        "# output_layer = Dense(6,activation='softmax')(fc1)\n",
        "\n",
        "# for layer in resnet_layer.layers:\n",
        "#   layer.trainable = False\n",
        "\n",
        "# resnet_1 = Model(resnet_layer.input,output_layer)\n",
        "# resnet_1.compile(optimizer= \"Adam\",loss='CategoricalCrossentropy', metrics=['accuracy'])\n",
        "# resnet_1.summary()"
      ],
      "metadata": {
        "id": "vpoRgWVmXHWZ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
        "# STEP_SIZE_TEST=test_generator.n//test_generator.batch_size\n",
        "\n",
        "# import datetime\n",
        "# %load_ext tensorboard\n",
        "\n",
        "# log_dir = os.path.join(\"logs\",'fits', datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "# tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph=True)\n",
        "# callbacks = [tf.keras.callbacks.ModelCheckpoint(saved_model_fp, save_weights_only=True, save_best_only=True, \\\n",
        "#                                        mode='max', monitor='val_accuracy'),\n",
        "#             tf.keras.callbacks.ReduceLROnPlateau(), tensorboard_callback]\n",
        "\n",
        "# resnet_1.fit_generator(generator=train_generator,\n",
        "#                     steps_per_epoch=STEP_SIZE_TRAIN,\n",
        "#                     validation_data=test_generator,\n",
        "#                     validation_steps=STEP_SIZE_TEST,\n",
        "#                     callbacks=callbacks,\n",
        "#                     epochs=20)"
      ],
      "metadata": {
        "id": "vY6IdN6zXVwE"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# os.chdir(\"/content/drive/MyDrive/Colab Notebooks/HackMIT/REI-Dataset/backyard/\")\n",
        "# !ls"
      ],
      "metadata": {
        "id": "xAy0sM2ZHfzo"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # serialize model to JSON\n",
        "# model_json = resnet_1.to_json()\n",
        "# with open(saved_model_fp, \"w\") as json_file:\n",
        "#     json_file.write(model_json)"
      ],
      "metadata": {
        "id": "xixKHFqUXXxb"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# with open(saved_model_fp, \"r\") as json_file:\n",
        "#     resnet_1 = json_file.read()\n",
        "# resnet_1 = tf.keras.models.model_from_json(resnet_1)\n",
        "# resnet_1.load_weights(saved_model_fp)\n",
        "# resnet_1.compile(optimizer= 'adam' ,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "3iOcEdv4XbwJ"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# STEP_SIZE_TEST=test_generator.n//test_generator.batch_size\n",
        "# resnet_1.evaluate_generator(generator=test_generator)"
      ],
      "metadata": {
        "id": "g2NMns9BXddG"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# n_batches = len(test_generator)\n",
        "# y_true = np.concatenate([np.argmax(test_generator[i][1], axis=1) for i in range(n_batches)])\n",
        "# predict  = np.argmax(resnet_1.predict_generator(test_generator, steps=n_batches), axis=1)\n",
        "\n",
        "# vocab_dict = (train_generator.class_indices)\n",
        "# plot_confusion_matrix(y_true, predict)"
      ],
      "metadata": {
        "id": "-A5tRheoXfn3"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "E9sWXXt0XiwF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm -rf ./logs/\n",
        "\n",
        "# resnet_layer = resnet_layer = tf.keras.applications.ResNet50V2(\n",
        "#     include_top=False,\n",
        "#     weights=\"imagenet\",\n",
        "#     input_tensor=None,\n",
        "#     input_shape=(224,224,3),\n",
        "#     pooling=None,\n",
        "#     classes=6,\n",
        "#     classifier_activation=\"softmax\",\n",
        "# )\n",
        "\n",
        "# conv_layer = tf.keras.layers.Conv2D(64,kernel_size=(7,7),activation='relu')(resnet_layer.output)\n",
        "# conv_layer2 = tf.keras.layers.Conv2D(32,kernel_size=(1,1),activation='relu')(conv_layer)\n",
        "\n",
        "# faltten_layer = Flatten()(conv_layer2)\n",
        "# fc1 = Dense(128,activation='relu')(faltten_layer)\n",
        "# fc2 = Dense(64,activation='relu')(fc1)\n",
        "\n",
        "# output_layer = Dense(6,activation='softmax')(fc2)\n",
        "\n",
        "# for layer in resnet_layer.layers[:143]:\n",
        "#   layer.trainable = False\n",
        "\n",
        "# resnet_2 = Model(resnet_layer.input,output_layer)\n",
        "# resnet_2.compile(optimizer= \"Adam\",loss='CategoricalCrossentropy', metrics=['accuracy'])\n",
        "# resnet_2.summary()"
      ],
      "metadata": {
        "id": "Ltj_UuiIXj5j"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = tf.keras.applications.ResNet50V2(weights='imagenet',\n",
        "    # base_model = tf.keras.applications.EfficientNetB2(weights='imagenet',\n",
        "                                include_top=False,\n",
        "                                # classes=6,\n",
        "                                # classifier_activation=\"softmax\",\n",
        "                                input_shape=(224,224,3))\n",
        "# Freeze the base model\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "x = base_model.output\n",
        "\n",
        "# Add new layers to the end of the base model\n",
        "x = GlobalAveragePooling2D()(x)\n",
        "x = Flatten()(x)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "\n",
        "predictions = Dense(6, activation=\"softmax\")(x)\n",
        "\n",
        "resnet_2 = Model(inputs=base_model.inputs, outputs=predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmNgirJvhUuJ",
        "outputId": "f6ede0f6-946f-474b-ee91-8eb1da68f9fc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94668760/94668760 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "METRICS = ['accuracy',\n",
        "           Precision(name='precision'),\n",
        "           Recall(name='recall')]\n",
        "\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "resnet_2.compile(loss='categorical_crossentropy',\n",
        "              optimizer=Adam(),\n",
        "              metrics=METRICS)"
      ],
      "metadata": {
        "id": "kXMkKupvh7FN"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
        "STEP_SIZE_TEST=test_generator.n//test_generator.batch_size\n",
        "\n",
        "import datetime\n",
        "%load_ext tensorboard\n",
        "\n",
        "logs = r'/content/drive/MyDrive/Colab Notebooks/HackMIT/logs'\n",
        "log_dir = os.path.join(logs,'fit', datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1, write_graph=True)\n",
        "callbacks = [tf.keras.callbacks.ModelCheckpoint(saved_model_fp, save_weights_only=True, save_best_only=True, \\\n",
        "                                       mode='max', monitor='val_accuracy'),\n",
        "            tf.keras.callbacks.ReduceLROnPlateau(), tensorboard_callback]\n",
        "\n",
        "# resnet_2.fit_generator(generator=train_generator,\n",
        "#                     steps_per_epoch=STEP_SIZE_TRAIN,\n",
        "#                     validation_data=test_generator,\n",
        "#                     validation_steps=STEP_SIZE_TEST,\n",
        "#                     callbacks=callbacks,\n",
        "#                     epochs=20)\n",
        "\n",
        "history = resnet_2.fit(train_generator,\n",
        "                    steps_per_epoch=STEP_SIZE_TRAIN,\n",
        "                    validation_data=test_generator,\n",
        "                    validation_steps=STEP_SIZE_TEST,\n",
        "                    callbacks=callbacks,\n",
        "                    epochs=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3UZ4EBIXqVU",
        "outputId": "f221cf3b-ccf2-4d13-8b54-062d5c827c57"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "147/147 [==============================] - 1654s 11s/step - loss: 0.4943 - accuracy: 0.8171 - precision: 0.8413 - recall: 0.7944 - val_loss: 0.3239 - val_accuracy: 0.8786 - val_precision: 0.8809 - val_recall: 0.8714 - lr: 0.0010\n",
            "Epoch 2/20\n",
            "147/147 [==============================] - 96s 654ms/step - loss: 0.3083 - accuracy: 0.8807 - precision: 0.8906 - recall: 0.8733 - val_loss: 0.2807 - val_accuracy: 0.9018 - val_precision: 0.9055 - val_recall: 0.8982 - lr: 0.0010\n",
            "Epoch 3/20\n",
            "147/147 [==============================] - 96s 653ms/step - loss: 0.2749 - accuracy: 0.8968 - precision: 0.9049 - recall: 0.8904 - val_loss: 0.2374 - val_accuracy: 0.9170 - val_precision: 0.9209 - val_recall: 0.9143 - lr: 0.0010\n",
            "Epoch 4/20\n",
            "147/147 [==============================] - 100s 677ms/step - loss: 0.2461 - accuracy: 0.9060 - precision: 0.9136 - recall: 0.8991 - val_loss: 0.2439 - val_accuracy: 0.9134 - val_precision: 0.9155 - val_recall: 0.9089 - lr: 0.0010\n",
            "Epoch 5/20\n",
            "147/147 [==============================] - 94s 637ms/step - loss: 0.2326 - accuracy: 0.9115 - precision: 0.9197 - recall: 0.9060 - val_loss: 0.3494 - val_accuracy: 0.8813 - val_precision: 0.8896 - val_recall: 0.8777 - lr: 0.0010\n",
            "Epoch 6/20\n",
            "147/147 [==============================] - 93s 634ms/step - loss: 0.2262 - accuracy: 0.9132 - precision: 0.9203 - recall: 0.9079 - val_loss: 0.2581 - val_accuracy: 0.9062 - val_precision: 0.9058 - val_recall: 0.9018 - lr: 0.0010\n",
            "Epoch 7/20\n",
            "147/147 [==============================] - 93s 630ms/step - loss: 0.2058 - accuracy: 0.9254 - precision: 0.9278 - recall: 0.9194 - val_loss: 0.2444 - val_accuracy: 0.9170 - val_precision: 0.9184 - val_recall: 0.9143 - lr: 0.0010\n",
            "Epoch 8/20\n",
            "147/147 [==============================] - 95s 647ms/step - loss: 0.1855 - accuracy: 0.9342 - precision: 0.9399 - recall: 0.9286 - val_loss: 0.2311 - val_accuracy: 0.9205 - val_precision: 0.9220 - val_recall: 0.9187 - lr: 0.0010\n",
            "Epoch 9/20\n",
            "147/147 [==============================] - 95s 644ms/step - loss: 0.1862 - accuracy: 0.9284 - precision: 0.9330 - recall: 0.9250 - val_loss: 0.2665 - val_accuracy: 0.9098 - val_precision: 0.9152 - val_recall: 0.9062 - lr: 0.0010\n",
            "Epoch 10/20\n",
            "147/147 [==============================] - 93s 631ms/step - loss: 0.1676 - accuracy: 0.9397 - precision: 0.9436 - recall: 0.9361 - val_loss: 0.2355 - val_accuracy: 0.9223 - val_precision: 0.9263 - val_recall: 0.9205 - lr: 0.0010\n",
            "Epoch 11/20\n",
            "147/147 [==============================] - 92s 627ms/step - loss: 0.1633 - accuracy: 0.9384 - precision: 0.9419 - recall: 0.9348 - val_loss: 0.2915 - val_accuracy: 0.9045 - val_precision: 0.9056 - val_recall: 0.8991 - lr: 0.0010\n",
            "Epoch 12/20\n",
            "147/147 [==============================] - 95s 644ms/step - loss: 0.1551 - accuracy: 0.9446 - precision: 0.9473 - recall: 0.9408 - val_loss: 0.2555 - val_accuracy: 0.9179 - val_precision: 0.9194 - val_recall: 0.9161 - lr: 0.0010\n",
            "Epoch 13/20\n",
            "147/147 [==============================] - 94s 641ms/step - loss: 0.1391 - accuracy: 0.9472 - precision: 0.9517 - recall: 0.9429 - val_loss: 0.2529 - val_accuracy: 0.9179 - val_precision: 0.9211 - val_recall: 0.9170 - lr: 0.0010\n",
            "Epoch 14/20\n",
            "147/147 [==============================] - 95s 643ms/step - loss: 0.1612 - accuracy: 0.9357 - precision: 0.9401 - recall: 0.9329 - val_loss: 0.2370 - val_accuracy: 0.9250 - val_precision: 0.9274 - val_recall: 0.9241 - lr: 0.0010\n",
            "Epoch 15/20\n",
            "147/147 [==============================] - 94s 638ms/step - loss: 0.1327 - accuracy: 0.9521 - precision: 0.9543 - recall: 0.9472 - val_loss: 0.2386 - val_accuracy: 0.9223 - val_precision: 0.9239 - val_recall: 0.9214 - lr: 0.0010\n",
            "Epoch 16/20\n",
            "147/147 [==============================] - 97s 663ms/step - loss: 0.1374 - accuracy: 0.9474 - precision: 0.9503 - recall: 0.9442 - val_loss: 0.2365 - val_accuracy: 0.9250 - val_precision: 0.9265 - val_recall: 0.9232 - lr: 0.0010\n",
            "Epoch 17/20\n",
            "147/147 [==============================] - 94s 639ms/step - loss: 0.1275 - accuracy: 0.9521 - precision: 0.9541 - recall: 0.9498 - val_loss: 0.2286 - val_accuracy: 0.9250 - val_precision: 0.9266 - val_recall: 0.9241 - lr: 0.0010\n",
            "Epoch 18/20\n",
            "147/147 [==============================] - 94s 639ms/step - loss: 0.1360 - accuracy: 0.9502 - precision: 0.9535 - recall: 0.9472 - val_loss: 0.2681 - val_accuracy: 0.9179 - val_precision: 0.9193 - val_recall: 0.9152 - lr: 0.0010\n",
            "Epoch 19/20\n",
            "147/147 [==============================] - 93s 632ms/step - loss: 0.1522 - accuracy: 0.9440 - precision: 0.9479 - recall: 0.9414 - val_loss: 0.2428 - val_accuracy: 0.9161 - val_precision: 0.9200 - val_recall: 0.9143 - lr: 0.0010\n",
            "Epoch 20/20\n",
            "147/147 [==============================] - 95s 646ms/step - loss: 0.1317 - accuracy: 0.9528 - precision: 0.9547 - recall: 0.9496 - val_loss: 0.2242 - val_accuracy: 0.9268 - val_precision: 0.9275 - val_recall: 0.9259 - lr: 0.0010\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_filepath = r'/content/drive/MyDrive/Colab Notebooks/HackMIT/saved_models/resnet50v2_20-EPOCHS_v2'\n",
        "resnet_2.save(model_filepath)"
      ],
      "metadata": {
        "id": "H1jX6A0vnurw"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "json_model_filepath = r'/content/drive/MyDrive/Colab Notebooks/HackMIT/saved_models/resnet50v2_20-EPOCHS_v2.json'\n",
        "# resnet_2.save_model(model_filepath)\n",
        "\n",
        "model_json = resnet_2.to_json()\n",
        "with open(json_model_filepath, \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "final_model_fp = r'/content/drive/MyDrive/Colab Notebooks/HackMIT/saved_models/resnet50v2_20-EPOCHS_v2.h5'\n",
        "resnet_2.save_weights(final_model_fp)\n",
        "print(\"Saved model to disk\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72higuUJn356",
        "outputId": "504c61fb-aabd-4ae0-dab0-0e0e30a6376e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved model to disk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(history):\n",
        "    fig, axs = plt.subplots(nrows=1, ncols=4, figsize=(30, 4), constrained_layout=True)\n",
        "\n",
        "    # I hand calculated F1-score since tfa's caused errors\n",
        "    f1_scores = []\n",
        "    # calculate f1-score from precision and recall\n",
        "    for i in range(len(history.history['val_recall'])):\n",
        "        if history.history['val_precision'][i] != 0 and history.history['val_recall'][i] != 0:\n",
        "            f1_scores.append((2 * history.history['val_precision'][i] * history.history['val_recall'][i]) / (history.history['val_precision'][i] + history.history['val_recall'][i]))\n",
        "\n",
        "    for i, metric in enumerate(['accuracy', 'loss', 'precision', 'recall']):\n",
        "        axs[i].plot(history.history[metric])\n",
        "        axs[i].plot(history.history['val_' + metric])\n",
        "        axs[i].set_title('Model {}'.format(metric.capitalize()))\n",
        "        axs[i].set_xlabel('Epochs')\n",
        "        axs[i].set_ylabel(metric.capitalize())\n",
        "        axs[i].legend(['Training', 'Validation'])\n",
        "\n",
        "        if metric is not 'loss':\n",
        "            non_zeros = [num for num in history.history['val_' + metric] if num != 0]\n",
        "            avg = \"{:.4f}\".format(np.mean(non_zeros))\n",
        "            std = \"{:.4f}\".format(np.std(non_zeros))\n",
        "            se = \"{:.4f}\".format(float(std) / (test_generator.n) ** 0.5)\n",
        "            print(metric + ': {} ± {}'.format(avg, se))\n",
        "\n",
        "    # print f1-score\n",
        "    f1_score = \"{:.4f}\".format(f1_scores[-1])\n",
        "    avg = \"{:.4f}\".format(np.mean(f1_scores))\n",
        "    std = \"{:.4f}\".format(np.std(f1_scores))\n",
        "    se = \"{:.4f}\".format(float(std) / (test_generator.n) ** 0.5)\n",
        "    print('final f1-score: ' + f1_score)\n",
        "    print('f1-score' + ': {} ± {}'.format(avg, se))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKSQRdyrA-V6",
        "outputId": "5be40c17-ce92-4d4e-fc96-5fbf076e4ccc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<>:19: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<>:19: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "<ipython-input-19-5f2b4a3418a4>:19: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n",
            "  if metric is not 'loss':\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "N_k5q4-cAVzf",
        "outputId": "8e5573b9-3b59-4aad-eee5-7727f48c4adc"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy: 0.9133 ± 0.0004\n",
            "precision: 0.9159 ± 0.0004\n",
            "recall: 0.9106 ± 0.0004\n",
            "final f1-score: 0.9267\n",
            "f1-score: 0.9132 ± 0.0004\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x400 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # serialize model to JSON\n",
        "# model_json = resnet_2.to_json()\n",
        "# with open(saved_model_fp, \"w\") as json_file:\n",
        "#     json_file.write(model_json)\n",
        "# with open(saved_model_fp, \"r\") as json_file:\n",
        "#     resnet_2 = json_file.read()\n",
        "# resnet_2 = tf.keras.models.model_from_json(resnet_2)\n",
        "# resnet_2.load_weights(saved_model_fp)\n",
        "# resnet_2.compile(optimizer= 'adam' ,loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
        "# STEP_SIZE_TEST=test_generator.n//test_generator.batch_size\n",
        "# resnet_2.evaluate_generator(generator=test_generator)\n",
        "\n",
        "\n",
        "# serialize model to JSON\n",
        "# model_json = resnet_2.to_json()\n",
        "\n",
        "\n",
        "# with open(\"model.json\", \"w\") as json_file:\n",
        "#     json_file.write(model_json)\n",
        "# # serialize weights to HDF5\n",
        "# model.save_weights(\"model.h5\")\n",
        "# print(\"Saved model to disk\")"
      ],
      "metadata": {
        "id": "vTwT4Z9PXskP"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report\n",
        "from sklearn.metrics import roc_curve, roc_auc_score, precision_recall_curve, average_precision_score, PrecisionRecallDisplay"
      ],
      "metadata": {
        "id": "Baj1OrLNEXyX"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(data_generator, predictions):\n",
        "    cm = confusion_matrix(y_true=data_generator.labels, y_pred=predictions)\n",
        "    cm_display = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['backyard', 'bathroom', 'bedroom', 'frontyard', 'kitchen', 'living_room'])\n",
        "\n",
        "    # cm_display.plot(include_values=True,\n",
        "    #                 cmap='Blues',\n",
        "    #                 xticks_rotation=45,\n",
        "    #                 values_format='.5g')\n",
        "\n",
        "    cm_display.plot()\n",
        "\n",
        "    plt.title('Confusion Matrix', fontdict = {'family': 'DejaVu Sans',\n",
        "                                              'color' : 'black',\n",
        "                                              'weight': 'heavy',\n",
        "                                              'size'  :  12})\n",
        "    # plt.tick_params(axis=u'both', which=u'both',length=0)\n",
        "    # plt.grid(b=None)\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "loLVxPhIZfTl"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_precision_recall(data_generator, predictions, ax):\n",
        "    precision, recall, thresholds = precision_recall_curve(y_true=data_generator.labels, probas_pred=predictions)\n",
        "    average_precision = average_precision_score(y_true=data_generator.labels, y_score=predictions)\n",
        "    pr_display = PrecisionRecallDisplay(precision=precision, recall=recall, average_precision=average_precision, estimator_name=None)\n",
        "\n",
        "    pr_display.plot(ax=ax, name='Proposed Model')\n",
        "\n",
        "    ax.set_title('Precision Recall Curve', fontdict = {'family': 'DejaVu Sans',\n",
        "                                                       'color' : 'black',\n",
        "                                                       'weight': 'heavy',\n",
        "                                                       'size'  :  12})"
      ],
      "metadata": {
        "id": "t4XqhojsECWg"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_roc_curve(data_generator, predictions, ax):\n",
        "    ns_predictions = [0 for i in range(data_generator.n)]\n",
        "\n",
        "    ns_auc = roc_auc_score(y_true=data_generator.labels, y_score=ns_predictions)\n",
        "    auc = roc_auc_score(y_true=data_generator.labels, y_score=predictions)\n",
        "\n",
        "    ns_fpr, ns_tpr, ns_thresholds = roc_curve(y_true=testing_generator.labels, y_score=ns_predictions)\n",
        "    fpr, tpr, thresholds = roc_curve(y_true=testing_generator.labels, y_score=predictions)\n",
        "\n",
        "    ax.plot(fpr, tpr, marker='.', label='Proposed Model (AUC = %0.3f)' % auc)\n",
        "    ax.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill (AUC = %0.3f)' % ns_auc)\n",
        "\n",
        "    ax.set_title('ROC Curve', fontdict = {'family': 'DejaVu Sans',\n",
        "                                         'color' : 'black',\n",
        "                                         'weight': 'heavy',\n",
        "                                         'size'  :  12})\n",
        "    ax.set_xlabel('False Positive Rate')\n",
        "    ax.set_ylabel('True Positive Rate')\n",
        "    ax.legend()"
      ],
      "metadata": {
        "id": "3ZTyElgZEGpG"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n_batches = len(test_generator)\n",
        "y_true = np.concatenate([np.argmax(test_generator[i][1], axis=1) for i in range(n_batches)])\n",
        "# predict  = np.argmax(resnet_2.predict_generator(test_generator, steps=n_batches), axis=1)\n",
        "\n",
        "predict = resnet_2.predict(test_generator, verbose=1)\n",
        "# rounded_predictions = np.around(predict.copy()).astype(int)\n",
        "rounded_predictions = np.argmax(predict.copy(), axis=1)\n",
        "\n",
        "vocab_dict = (train_generator.class_indices)\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
        "plot_roc_curve(test_generator, predict, ax1)\n",
        "plot_precision_recall(test_generator, predictions, ax2)\n",
        "\n",
        "# cm = sklearn.metrics.confusion_matrix(y_true=y_true, y_pred=predict)\n",
        "# cm_display = sklearn.metrics.ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['backyard', 'bathroom', 'bedroom', 'frontyard', 'kitchen', 'living_room'])\n",
        "\n",
        "plot_confusion_matrix(test_generator, rounded_predictions)\n",
        "\n",
        "# cm_display.plot()\n",
        "                    # include_values=True,\n",
        "                    # cmap='Blues',\n",
        "                    # xticks_rotation=45,\n",
        "                    # values_format='.5g')\n",
        "\n",
        "# plt.title('Confusion Matrix', fontdict = {'family': 'DejaVu Sans',\n",
        "#                                           'color' : 'black',\n",
        "#                                           'weight': 'heavy',\n",
        "#                                           'size'  :  12})\n",
        "# plt.tick_params(axis=u'both', which=u'both',length=0)\n",
        "# plt.grid(b=None)\n",
        "\n",
        "# plt.show()"
      ],
      "metadata": {
        "id": "1_DrAmdaXvhg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install scikit-learn==scikit-learn 0.23.2\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "\n",
        "n_batches = len(test_generator)\n",
        "y_true = np.concatenate([np.argmax(test_generator[i][1], axis=1) for i in range(n_batches)])\n",
        "predict  = np.argmax(resnet_2.predict_generator(test_generator, steps=n_batches), axis=1)\n",
        "\n",
        "vocab_dict = (train_generator.class_indices)\n",
        "plot_confusion_matrix(y_true, predict)"
      ],
      "metadata": {
        "id": "mtfDAPrKbFab"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sklearn.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "a08nmBLtcTmC",
        "outputId": "aa6644de-42b1-4d0c-af8e-f0f630c07add"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.2.2'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = load_model(model_filepath)"
      ],
      "metadata": {
        "id": "CiO0QKWFmFH5"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_fp = r'/content/drive/MyDrive/Colab Notebooks/HackMIT/REI-Dataset/bedroom/bedroom (1009).jpg'\n",
        "resized_img = np.asarray(resize(cv2.imread(test_fp), (224, 224))).astype(np.uint8)\n",
        "np.seterr(divide='ignore', invalid='ignore')\n",
        "resized_img = resized_img / 255\n",
        "\n",
        "testing_img = resized_img.reshape(1, 224, 224, 3)\n",
        "prediction = model.predict(testing_img)\n",
        "print(prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWU8PWSde3NN",
        "outputId": "80bbcf41-240a-4a98-ce8c-85f04e59bcf1"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 29ms/step\n",
            "[[5.3836058e-05 2.2085711e-04 9.2606759e-01 1.5787580e-05 4.7533318e-05\n",
            "  7.3594473e-02]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "a8bnkOKcX0dz"
      }
    }
  ]
}