ImagePreProcess.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "582fb36c-d314-4a2a-a6eb-0cc46a4d9160",
   "metadata": {},
   "source": [
    "### Remove Background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7de87ada-152d-454c-8041-7e4b0ed3cbe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ecoli Citrus\\2023-09-28_3_30_45_uv_Photo_Capture\n",
      "Processed - 2023-09-28_3_30_45_uv_Photo_Capture\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def process_leaf_image(image_path):\n",
    "    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))\n",
    "    morph_img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel, iterations=3)\n",
    "    _, thresh_img = cv2.threshold(morph_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
    "    contours, _ = cv2.findContours(thresh_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "    if contours:\n",
    "        largest_contour = max(contours, key=cv2.contourArea)\n",
    "        if largest_contour.shape[0] >= 5:\n",
    "            ellipse = cv2.fitEllipse(largest_contour)\n",
    "            mask = np.zeros_like(img)\n",
    "            cv2.ellipse(mask, ellipse, (255, 255, 255), -1)\n",
    "            dilated_mask = cv2.dilate(mask, kernel, iterations=3)\n",
    "            img_masked = cv2.bitwise_and(img, dilated_mask)\n",
    "            return img_masked\n",
    "    return img  # Return original if processing is not applicable\n",
    "\n",
    "def process_subfolders(base_folder):\n",
    "    # List all subdirectories in the base folder\n",
    "    subdirectories = [d for d in os.listdir(base_folder) if os.path.isdir(os.path.join(base_folder, d))]\n",
    "    for subdir in subdirectories:\n",
    "        if subdir == 'backup':\n",
    "            continue\n",
    "        subdir_path = os.path.join(base_folder, subdir)\n",
    "        # if \"2023-09-28_3_30_45_uv_Photo_Capture\" in subdir_path:\n",
    "        print(subdir_path)\n",
    "        # Process each file in the subdir \n",
    "        for file in os.listdir(subdir_path):\n",
    "            if file.lower().endswith(('.png', '.jpg', '.jpeg')):\n",
    "                image_path = os.path.join(subdir_path, file)\n",
    "                processed_image = process_leaf_image(image_path)\n",
    "                processed_image_path = os.path.join(subdir_path, f\"processed_{file}\")\n",
    "                cv2.imwrite(processed_image_path, processed_image)\n",
    "        print(f\"Processed - {subdir}\")\n",
    "\n",
    "# Define the base folder path\n",
    "base_folder = \"ecoli Citrus\"  # Update this path to your actual folder path\n",
    "\n",
    "# Process the images in subfolders\n",
    "process_subfolders(base_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a60b153-3364-463a-843a-9e144f3859ca",
   "metadata": {},
   "source": [
    "### Comparision Images "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c8d0a9e0-a37b-4072-b01f-0120229cdbf2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Comparison image created for 2023-09-28_3_19_42_uv_Photo_Capture\n",
      "Comparison image created for 2023-09-28_4_27_29_uv_Photo_Capture\n",
      "Comparison image created for 2023-09-28_4_30_13_uv_Photo_Capture\n",
      "Comparison image created for 2023-09-28_4_33_18_uv_Photo_Capture\n",
      "Comparison image created for 2023-09-28_4_37_08_uv_Photo_Capture\n",
      "Comparison image created for 2023-09-28_4_39_21_uv_Photo_Capture\n",
      "Comparison image created for 2023-09-28_4_41_43_uv_Photo_Capture\n",
      "Comparison image created for 2023-09-28_4_44_31_uv_Photo_Capture\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def resize_image_to_same_height(img, height):\n",
    "    \"\"\"Resize an image to the given height while maintaining aspect ratio.\"\"\"\n",
    "    ratio = height / img.shape[0]\n",
    "    width = int(img.shape[1] * ratio)\n",
    "    resized_img = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)\n",
    "    return resized_img\n",
    "\n",
    "def create_comparison_for_all_images(subdir_path, subdir_name):\n",
    "    original_images = []\n",
    "    processed_images = []\n",
    "\n",
    "    for file in os.listdir(subdir_path):\n",
    "        if file.lower().endswith(('.png', '.jpg', '.jpeg')):\n",
    "            if file.startswith('processed_'):\n",
    "                processed_path = os.path.join(subdir_path, file)\n",
    "                processed_img = cv2.imread(processed_path, cv2.IMREAD_GRAYSCALE)\n",
    "                processed_images.append(processed_img)\n",
    "            else:\n",
    "                original_path = os.path.join(subdir_path, file)\n",
    "                original_img = cv2.imread(original_path, cv2.IMREAD_GRAYSCALE)\n",
    "                original_images.append(original_img)\n",
    "\n",
    "    # Ensure all images are the same height for comparison\n",
    "    max_height = max(img.shape[0] for img in original_images + processed_images)\n",
    "    original_images = [resize_image_to_same_height(img, max_height) for img in original_images]\n",
    "    processed_images = [resize_image_to_same_height(img, max_height) for img in processed_images]\n",
    "\n",
    "    # Concatenate all original images horizontally and all processed images horizontally\n",
    "    concatenated_originals = np.hstack(original_images) if original_images else None\n",
    "    concatenated_processeds = np.hstack(processed_images) if processed_images else None\n",
    "\n",
    "    # Concatenate the two sets of images vertically\n",
    "    if concatenated_originals is not None and concatenated_processeds is not None:\n",
    "        comparison_img = np.vstack((concatenated_originals, concatenated_processeds))\n",
    "        comparison_path = os.path.join(subdir_path, f\"comparison_{subdir_name}.png\")\n",
    "        cv2.imwrite(comparison_path, comparison_img)\n",
    "        print(f\"Comparison image created for {subdir_name}\")\n",
    "\n",
    "def create_comparison_for_subfolders(base_folder):\n",
    "    subdirectories = [d for d in os.listdir(base_folder) if os.path.isdir(os.path.join(base_folder, d))]\n",
    "    for subdir in subdirectories:\n",
    "        if subdir == 'backup':\n",
    "            continue\n",
    "        subdir_path = os.path.join(base_folder, subdir)\n",
    "        create_comparison_for_all_images(subdir_path, subdir)\n",
    "\n",
    "# Define the base folder path\n",
    "base_folder = \"ecoli Citrus\"  # Update this path to your actual folder path\n",
    "\n",
    "# Create comparison images for subfolders\n",
    "create_comparison_for_subfolders(base_folder)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b576817-980b-4c90-a6c3-45e582730d32",
   "metadata": {},
   "source": [
    "### PyWavelets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83adcfdc-6e9e-4fc5-939c-d9b64e8b8060",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pywt\n",
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def enhance_image_with_wavelets(image_path):\n",
    "    # Load the image\n",
    "    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "    \n",
    "    # Perform a 2-level wavelet decomposition\n",
    "    coeffs = pywt.wavedec2(img, 'db1', level=2)\n",
    "    cA2, (cH2, cV2, cD2), (cH1, cV1, cD1) = coeffs\n",
    "    \n",
    "    # Enhance the detail coefficients (this is a simplistic approach; adjustments may be needed)\n",
    "    cH2 *= 1.5\n",
    "    cV2 *= 1.5\n",
    "    cD2 *= 1.5\n",
    "    \n",
    "    # Reconstruct the image from the modified coefficients\n",
    "    coeffs = cA2, (cH2, cV2, cD2), (cH1, cV1, cD1)\n",
    "    enhanced_img = pywt.waverec2(coeffs, 'db1')\n",
    "    \n",
    "    # Ensure the enhanced image has the same scale as the original\n",
    "    enhanced_img = np.clip(enhanced_img, 0, 255).astype(np.uint8)\n",
    "    \n",
    "    return enhanced_img\n",
    "\n",
    "# Example usage\n",
    "image_path = 'ecoli Citrus/2023-09-28_4_30_13_uv_Photo_Capture/processed_2023-09-28_4_30_13_uv_Photo_Capture_1.png'  # Update with the actual path\n",
    "enhanced_img = enhance_image_with_wavelets(image_path)\n",
    "\n",
    "# Display the original and enhanced images side by side\n",
    "original_img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
    "ax[0].imshow(original_img, cmap='gray')\n",
    "ax[0].set_title('Original Processed Image')\n",
    "ax[1].imshow(enhanced_img, cmap='gray')\n",
    "ax[1].set_title('Enhanced Image')\n",
    "for a in ax:\n",
    "    a.axis('off')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "610675c8-9137-4684-a102-d584d615d910",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c54e59dc-157d-47fe-910e-574c881292f4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "4d259969-997b-47f0-814a-01b99bdd2dd4",
   "metadata": {},
   "source": [
    "### Clear Comparision and Processed images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4dc658bb-f6d9-47ba-b30e-17d342154caa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed ecoli Citrus\\2023-09-28_3_19_42_uv_Photo_Capture\\comparison_2023-09-28_3_19_42_uv_Photo_Capture.png\n",
      "Removed ecoli Citrus\\2023-09-28_3_19_42_uv_Photo_Capture\\processed_2023-09-28_3_19_42_uv_Photo_Capture_1.png\n",
      "Removed ecoli Citrus\\2023-09-28_3_19_42_uv_Photo_Capture\\processed_2023-09-28_3_19_42_uv_Photo_Capture_2.png\n",
      "Removed ecoli Citrus\\2023-09-28_3_19_42_uv_Photo_Capture\\processed_2023-09-28_3_19_42_uv_Photo_Capture_3.png\n",
      "Removed ecoli Citrus\\2023-09-28_3_19_42_uv_Photo_Capture\\processed_2023-09-28_3_19_42_uv_Photo_Capture_4.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_27_29_uv_Photo_Capture\\comparison_2023-09-28_4_27_29_uv_Photo_Capture.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_27_29_uv_Photo_Capture\\processed_2023-09-28_4_27_29_uv_Photo_Capture_1.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_27_29_uv_Photo_Capture\\processed_2023-09-28_4_27_29_uv_Photo_Capture_2.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_27_29_uv_Photo_Capture\\processed_2023-09-28_4_27_29_uv_Photo_Capture_3.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_27_29_uv_Photo_Capture\\processed_2023-09-28_4_27_29_uv_Photo_Capture_4.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_30_13_uv_Photo_Capture\\comparison_2023-09-28_4_30_13_uv_Photo_Capture.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_30_13_uv_Photo_Capture\\processed_2023-09-28_4_30_13_uv_Photo_Capture_1.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_30_13_uv_Photo_Capture\\processed_2023-09-28_4_30_13_uv_Photo_Capture_2.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_30_13_uv_Photo_Capture\\processed_2023-09-28_4_30_13_uv_Photo_Capture_3.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_30_13_uv_Photo_Capture\\processed_2023-09-28_4_30_13_uv_Photo_Capture_4.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_33_18_uv_Photo_Capture\\comparison_2023-09-28_4_33_18_uv_Photo_Capture.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_33_18_uv_Photo_Capture\\processed_2023-09-28_4_33_18_uv_Photo_Capture_1.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_33_18_uv_Photo_Capture\\processed_2023-09-28_4_33_18_uv_Photo_Capture_2.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_33_18_uv_Photo_Capture\\processed_2023-09-28_4_33_18_uv_Photo_Capture_3.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_33_18_uv_Photo_Capture\\processed_2023-09-28_4_33_18_uv_Photo_Capture_4.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_37_08_uv_Photo_Capture\\comparison_2023-09-28_4_37_08_uv_Photo_Capture.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_37_08_uv_Photo_Capture\\processed_2023-09-28_4_37_08_uv_Photo_Capture_1.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_37_08_uv_Photo_Capture\\processed_2023-09-28_4_37_08_uv_Photo_Capture_2.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_37_08_uv_Photo_Capture\\processed_2023-09-28_4_37_08_uv_Photo_Capture_3.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_39_21_uv_Photo_Capture\\comparison_2023-09-28_4_39_21_uv_Photo_Capture.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_39_21_uv_Photo_Capture\\processed_2023-09-28_4_39_21_uv_Photo_Capture_1.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_39_21_uv_Photo_Capture\\processed_2023-09-28_4_39_21_uv_Photo_Capture_2.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_39_21_uv_Photo_Capture\\processed_2023-09-28_4_39_21_uv_Photo_Capture_3.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_39_21_uv_Photo_Capture\\processed_2023-09-28_4_39_21_uv_Photo_Capture_4.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_41_43_uv_Photo_Capture\\comparison_2023-09-28_4_41_43_uv_Photo_Capture.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_41_43_uv_Photo_Capture\\processed_2023-09-28_4_41_43_uv_Photo_Capture_1.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_41_43_uv_Photo_Capture\\processed_2023-09-28_4_41_43_uv_Photo_Capture_2.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_41_43_uv_Photo_Capture\\processed_2023-09-28_4_41_43_uv_Photo_Capture_3.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_41_43_uv_Photo_Capture\\processed_2023-09-28_4_41_43_uv_Photo_Capture_4.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\comparison_2023-09-28_4_44_31_uv_Photo_Capture.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\processed_2023-09-28_4_44_31_uv_Photo_Capture_1.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\processed_2023-09-28_4_44_31_uv_Photo_Capture_2.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\processed_2023-09-28_4_44_31_uv_Photo_Capture_3.png\n",
      "Removed ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\processed_2023-09-28_4_44_31_uv_Photo_Capture_4.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def clear_processed_and_comparison_images(base_folder):\n",
    "    for subdir, _, files in os.walk(base_folder):\n",
    "        for file in files:\n",
    "            if file.startswith(\"processed_\") or file.startswith(\"comparison_\"):\n",
    "                # Construct the full path to the file\n",
    "                file_path = os.path.join(subdir, file)\n",
    "                # Remove the file\n",
    "                os.remove(file_path)\n",
    "                print(f\"Removed {file_path}\")\n",
    "\n",
    "# Define the base folder path\n",
    "base_folder = \"ecoli Citrus\"  # Replace with your actual base folder path\n",
    "\n",
    "# Run the function to clear the images\n",
    "clear_processed_and_comparison_images(base_folder)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36745b40-1ac8-407f-9e96-42a2eeca4bf7",
   "metadata": {},
   "source": [
    "### Rotation Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9d68e2ac-1d62-418b-a60b-7955ef61d7af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nTqxw2Q4dOoiIVPodV2XzWrZsWaNxoW6gbi+elJQUKSoqktzcXOPHOHz4cEB9N2rUqMLvjBP5qcazs7Pl0KFDFS5DjduJWg6tYcOGiYjIP//5T/+0Ro0aVbivFpEa7a+pZZShls1MmTJF0tLSpEWLFnLw4EE5ePCg/0OeI0eOVForZVJSUoKaz+rasmVLUC2ffYO3c3Xo0EH27t1b7odjZXbt2iXh4eHSpEmTGo2ttllxBvZsZX+83a9fP1mwYIFMmzbNf/e9vXv3Bi2/Z88eqV+/vv/ToooujX3llVeksLBQMjMzpVmzZv7pmzZtClq2qpfXnj2u/v37B8zbu3dvSO8aWJ7U1FQR+enSi7NvFvH999/Lt99+W+ObND322GMBn2ZVtoPt06ePxMfHy8qVK2X69Onlblyef/55EREZMWJEjcaFuoe6rV379+8Xr9cr0dHRxo/RsGFD2bBhQ8C0srNF5UlPT5eVK1fK888/X+4NLk6fPi3r1q2Tbt26cdBrMWq55goLC6W0tDTgUsrU1FTZtGmTnD59OuBGTtu3b/fPN0UtozzUctUdOnRIvvnmG2nRokXQvMGDB0tcXFzQjaTOtX///qCzwtXVtWvXoFqu7M8I0tPTZcuWLbJ69WoZOXJk0PyDBw/KBx98IEOGDLHuGz+sOAN7rmuvvVZ69uwp8+fPl4KCAmnUqJGkpqbK8uXLA95An3/+ubz11lsyaNAg/7Sywjv3jVbWUKmqf1p2drYsXbo0aP1RUVHnfaOK/HT33+TkZHnqqacCPv14/fXX5csvv5QbbrihKk83SFVv+92xY0dp166dLFmyJOA23osWLRKHwyE33nij0frLdO/eXQYMGOD/KTvLWp7IyEiZMmWK7N27t9y7zL322muybNkySU9Pl86dO9doXKibqNuaffVGec69LFBEZOfOnZKZmSnXX3+90aVOZbxeb0B9DxgwIOBOlecaPny4dOzYUR566KGguyCXlpbKXXfdJSdPnqz0LpOwA7VctVo+depUucs888wz/vGVufHGG8Xn8wXcJbWwsFCWLl0qvXr1kpSUFKOxilDLqBi1XLVaXrJkiaxduzbg5+677xYRkUcffVReeOEF/7Ll7ZfXr18v//M//yO/+MUvjMZZJiEhIaiWz/6b4HP97ne/k4YNG8rkyZOD/v63oKBAbrvtNnE4HDJlypQajetisO4MbJnJkyfLiBEjZNmyZTJu3DjJyMiQ//qv/5KrrrpK7rjjDsnPz5e//vWvEhcXJ7NmzfLnunfvLiIi06dPl5tvvlnCwsIkPT1drr/+egkPD5f09HT53e9+J2fOnJGnn35akpOTgy7r6d69uyxatEjmzJkjrVu3luTk5KBPhEREwsLC5OGHH5bbbrtN+vbtK7fccov88MMP8sQTT0jz5s39l1NU17333ivLly+XAwcOnPcPzzMyMmTw4MFy/fXXy8033yyff/65LFiwQO68805p37690fpNTZkyRT799FN5+OGHZevWrTJ8+HCJiIiQzZs3y9///nfp2LGjLFu2LCDzzTffyIoVK0RE/DvSOXPmiMhPn8j993//d60+B9QMdVu1un3//ffl/fffF5Gfdoa5ubn+9/0111wj11xzjYiI/PrXv5aIiAjp3bu3JCcny+7du2XJkiUSGRkpDz30kNE4TYWFhclLL70k/fv3l6uvvlpuu+02ufLKK+XUqVOycuVK+eSTT+TPf/6z//LJMq+88ors3LlTRH76jr1du3b5n+vgwYOlS5cutfo8UDXU8vlr+d1335UJEybIjTfeKJdffrkUFRXJBx98IC+//LJceeWVAWdEevXqJSNGjJB7771XfvzxR2ndurUsX75cDh48KM8++6zROE1Ry/9ZqOXz1/L1118fNK2s8e7bt2/Ah1G9e/eWK664Qq688kqJi4uTTz75RJ577jlJSUmRP//5z0bjNJWQkCBr1qyRQYMGSbdu3eTOO++UDh06yNGjR2XZsmWyf/9+WbBgQdD3Pa9YsUK++eYb/98vv//++/5a/u///u+6cSXaxbsB8vlVdItqVVWfz6etWrXSVq1aaUlJiaqqbty4Ufv06aMREREaGxur6enpunv37qDs/fffr02aNFGn0xlw++zMzEzt0qWLer1ebd68uT788MP63HPPBd1i++jRo3rDDTdoTEyMioj/FuDn3va7zKpVq/SKK65Qj8ejiYmJ+pvf/Ea//fbbgGVGjx6tUVFRQWOdOXNm0FfHVPcW/mvXrtXU1FT1eDzatGlTnTFjhhYVFVUpG2qlpaW6bNky7dOnj//1ExEdMGCAFhYWBi1f9pqW91PRV4vg4qJua163Zfnyfs6+nf0TTzyhPXv21MTERHW73dqoUSMdOXKkfvXVV+ddx4Vy7Ngx/dOf/qStW7fW8PBw/7ifffbZcpcve13K+1m6dGntDh4BqOWa1fLXX3+to0aN0pYtW2pERIR6vV7t2LGjzpw5U8+cORO0fH5+vk6aNEkbNmyoHo9He/TooW+88Ual67iQqOVLB7UcmuPps1X0mk6fPt3/VVRhYWHarFkzveuuu/To0aPVXkeoHDx4UMeOHavNmjVTt9vtr8uNGzeWu3zfvn0rrOVzfycXi0P1rHP8QC0rLi6W9PR0efvtt+WVV16p8eUVAOqWzz77TNLS0iQlJUU2b95c7btPAqgbqGXg0vD222/LoEGD5Oqrr5bXX3+9wjuS12VW/g0sLh1llyqlpqbKiBEj5JNPPrnYQwIQQp07d5Z169bJV199JUOHDg346i0A9qCWgUvDddddJ8uXL5dNmzbJbbfdJjaey+QMLAAAAADACpyBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVnCH4kEcDkcoHgaoU/4T729GLeNSRC0Dl4b/xFoWoZ5xaapJPXMGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBffFHgAAAFXhdPKZK3ApoJYB1ARbEAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFdwXewAAgP8s4eHhRrnY2NgQjwS4tERGRhrl8vPzjXJer9coFxERYZQD/pOY7itNlZSUGOXc7tpvJzkDCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwgvtiDwCoqgYNGhjlXC5XiEcCXFrCw8NrNef1eo1yYWFhRjnANlFRUUa5yMhIo1xERIRRznT/qqpGOSAUHA6HUc70fWtaz6b7SqfT7Pyk6fMzXV9NcAYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAF98UeAOzVtGlTo1xpaalRzuVyGeXCw8ONcsDF4nA4jHJJSUlGOdMacTrNPgM1rWWfz2eUAy6WsLAwo1xEREStrs+0lt1us8NI0xxwNtP3UVxcnFHOdB9U28ehHo/HKBcdHW2UKywsNMrVBGdgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWcF/sAeDia9iwoVGutLTUKOdwOIxyUVFRRrnWrVsb5YCaioyMNMp5PB6jXFhYmFEuOjraKBcfH2+UM9125OTkGOWAMk6n2ef2MTExRjnTbUB4eLhRznT/avq6mI7T9LgDOJvpPs/lchnlTN/vtZ1r0aKFUS45Odkol5eXZ5SrCc7AAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACs4L7YA0DoJCcnX+whVImqGuXi4uJCPBKgahwOh1EuMjLSKOfxeIxy4eHhRrnExESjXP369Y1yPp/PKBcWFmaUA8pER0cb5WJiYoxyprVcr149o5xpbWVnZ9fq+ky3VcDZantf6XSanfczXV9sbKxRznT7YXqcHRUVZZSrCc7AAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACs4L7YA0Cw+vXrG+XCwsKMcj6fzyhXUlJilDOVm5trlIuPjw/tQPAfJyIiwihnWpOqapQz5XA4jHKm2w7T18V0nLj0mNak1+s1yrndZodLzZo1M8rFxcUZ5YqKioxyLpfLKJeVlWWUO3bsmFEOlybT93t0dLRRrra3A6bHyzk5ObWaM92nO521fz6UM7AAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAK7os9gEtZvXr1jHLx8fFGueTkZKPc999/b5TLzc01yrndZm+7nJwco9zhw4eNcrj0OJ1mn9lFR0cb5VS1VnN5eXlGuVOnThnlHA6HUc70+WVnZxvlcOmJjY01ypnuf1wul1GuuLjYKFdSUmKU83g8RjnT1yUsLMwoV1BQYJRD3WZalzExMUa5iIgIo1yzZs2Mcqb7rtOnTxvlTPfN+/btM8qZ/h4uBs7AAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACs4L7YA7BBQkKCUa60tNQoV79+faNcdHR0reZyc3ONcm632dvO9PU0HScuPVFRUUY50/es02n2GWFxcbFRzuFwGOVOnz5tlCssLDTKmb4upjnUXWFhYUY50/eCqhrl8vPzjXKmteVyuYxyJSUlRrmTJ08a5UxRyzib6fshKSnJKGd6LGDKdN9sut0x3c7l5eUZ5S4GtiAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACu4L/YAbOBwOIxyYWFhRjmPx2OUKy0tNcoVFxcb5Xw+n1EuLy/PKOd0mn3eYppD3RUeHm6Ui46ONsolJycb5Uxr8uTJk0Y501o2VVBQYJQz3aZSy5cel8tllDOtLdP3Xm3Xcm5urlHO9PVU1VrNmb6eqB0RERFGuZiYmBCPpHKmx5P5+flGOa/Xa5QrLCw0ypkeZ5vuK2u7H6gJjgYAAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFZwX+wB1KakpCSjnMvlqtVcdna2US4qKsoop6pGObfb7O3jcDiMco0bNzbKFRcXG+Vw4Zm+F8LCwoxypu9Z09oy3Qbk5eXVaq6kpMQo5/F4jHKmvz+fz2eUQ91l+l5wOs0+fzfd5pi+90xry5Tp/tw0Z/p7oJbrNtPfj+n7yLROTp8+bZQrLS01ypkeQ5jum03HWVhYaJQz/T3U9nZOhDOwAAAAAABL0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAruC/2AGqTw+Ewynm9XqNccXGxUe6HH34wypWWltZqzuVyGeViY2ONcomJiUY51F1Op9lnaB6PxyhXUlJilHO7zTaVpjVimjN9PU1zDRo0MMqFhYUZ5QoLC41yqLtM96/16tUL8Ugqd/r0aaOc6Xu2qKjIKGday9HR0UY5VTXKmW6LUTtM90Gmx5Omx+e5ublGOdP6Mn1dTOuktl9Pn89Xq+urCc7AAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACs4L7YAzARFxdnlHM6zfp1t9vsZQoPDzfKRUREGOV8Pp9RLisrq1bX53A4jHKmvz9VNcrhwrPld2NaIx6PxyhXUlJilHO5XEY5r9drlDPdxkVHRxvlTMeJCy8sLKxWc6bvBdPjB9P9XX5+vlHOVHx8vFEuMTHRKGf6+8vLyzPKoXaY7ptre59uuj7Tfazp8WtxcbFRznQ7Z9pHmB5D1PZ2ToQzsAAAAAAAS9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK7gv9gBMqKpRzuk069cdDodRLiEhwSgXHR1tlCsuLjbKlZSUGOVOnjxplMvJyTHKFRYWGuWSk5ONcrjwTN/rHo/HKOdyuYxypu/10tJSo5zpNseU6ThNazImJsYoFxERYZTDhWe6f83PzzfKmb5nTY8foqKijHKmz6+goMAoZyoyMtIoZ7qtMt0Wo3aYHheavh9MjwV8Pp9RLi8vzyhnWpem7/d69eoZ5UxfT1NFRUW1uj4RzsACAAAAACxBAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKzgUFW92IMAAAAAAOB8OAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAhtC7774rDodD3n333Ys9FGOlpaVSUlJysYcB1JpLoW6ro7Cw8GIPAbggqGXg0kAt43zqdAO7bNkycTgc/h+32y1NmjSRMWPGyHfffWf0mFu2bJFZs2bJqVOnjMe1cOFCWbZsmXG+tmVmZkq3bt3E6/VKs2bNZObMmeU2qY888ojExsZKbGysTJ069YKMRVVlxYoVcs0110h8fLxERkZK586dZc6cOZKXlxewbGlpqSxbtkwGDx4sKSkpEhUVJZ06dZI5c+ZIQUHBBRkfao66rblVq1bJyJEj5fLLLxeHwyHXXnttucuV7eTL+9m2bVvAsidOnJCBAwdKZGSktGjR4oIdGJw4cUImT54sbdu2Fa/XK4mJiTJw4EB57bXXgpY9fPiw3HfffdKzZ09JSEiQ+vXry7XXXisbN268IGND9VDLNXfPPfdIt27dJDExUSIjI6V9+/Yya9YsOXPmTNCyhYWFMnXqVGncuLFERERIr169ZMOGDUHLUcuoLmo5tPbt2yder1ccDofs2LEjYN65r/XZP0ePHg1Y9sCBA9KrVy/xer3SpUsX2bVr1wUZ76FDh2TcuHHSvHlz8Xg8kpycLL/61a9ky5YtQcvu2bNHpkyZIqmpqRITEyONGjWSG264Ieh5XnRahy1dulRFRGfPnq0rVqzQp59+Wu+44w51uVzaqlUrzc/Pr/ZjZmRkqIjogQMHjMfVsWNH7du3b9B0n8+n+fn56vP5jB871NavX68Oh0P79eunS5Ys0bvvvludTqeOGzcuYLnNmzdrfHy8Ll68WFesWKEpKSm6atWqkI6lpKREb7rpJhURTUtL03nz5unixYt15MiR6nQ6tXPnzvrDDz/4l8/JyVER0Z/97Gc6Z84cXbJkid52223qdDr12muv1dLS0pCOD6FB3dZc3759NTo6Wvv166cJCQnljltVddOmTSoiOmHCBF2xYkXAz7FjxwKWvfPOO/Waa67RtWvX6vTp0zUpKUnPnDkT0nHv2bNHmzRpouHh4fq73/1On376ac3IyNDU1FQVEZ06dWrA8n/96181IiJCb7nlFl2wYIHOnz9fu3XrpiKizz33XEjHhuqjlmuuT58+OmHCBH3yySd1yZIletddd6nH49E+ffoEjfPmm29Wt9utkyZN0sWLF+tVV12lbrdbP/jgg4DlqGVUF7UcWunp6RoVFaUioh9//HHAvHNf67N/zn2dBwwYoMOGDdN169bp2LFjtX379iEf6+bNmzU2NlZjY2P1j3/8oz7zzDM6Z84cbd26tTocDl24cGHA8n/60580Pj5e77jjDl28eLE+8sgj2qpVK3W5XLphw4aQj8+UFQ3suW+OqVOnqogYNVgXsuDqog4dOmjXrl21uLjYP2369OnqcDj0yy+/9E/LyMjQP/zhD/7/z58/X3//+9+HdCwPPvigiohOmjQpaF5mZqY6nU4dNGiQf1phYaF++OGHQcved999KiJ1qpDwf6jbmjt06JB/x13ZuMsa2NWrV5/3MTt27Kiffvqp//+pqam6Y8eOkIxXVbWoqEg7deqkkZGRum3btoB5JSUl+utf/1pFRF988UX/9M8//zyo0S4oKNB27dpp06ZNQzY2mKGWL4xHH31URUS3bt3qn7Z9+3YVEc3IyPBPy8/P11atWulVV10VkKeWUV3Ucui88cYbGh4erjNmzKi0gT13enmioqL01KlT/v8nJCTo8ePHQzbWrKwsbdiwoTZo0EC//vrrgHl5eXmalpamLpcrYFu0Y8cOzcnJCVj2+PHjmpSUpH369AnZ2GqqTl9CXJG0tDQR+ekU/tneeecdSUtLk6ioKImPj5chQ4bIl19+6Z8/a9YsmTx5soiItGjRwn9K/+DBgyIisnTpUunfv78kJyeLx+ORDh06yKJFiwLW0bx5c/niiy/kvffe8+fLLu+r6Jr91atXS/fu3SUiIkLq168vI0eODLpkY8yYMRIdHS3fffedDB06VKKjoyUpKUkmTZokPp8vYNkjR47Inj17pLi4uNLXaffu3bJ7924ZO3asuN1u//Tx48eLqsqaNWv801q2bCmZmZmybds22blzp7zwwgty+eWXV/r41ZGfny8ZGRnSpk0bmTt3btD89PR0GT16tKxfv14++ugjEREJDw+X3r17By37q1/9SkQk4HeLuo+6rVrdioikpKSI01m9zXNOTk6lf7/esmVLefLJJ2Xfvn2yevVq+frrr+Wyyy6r1joq89JLL8nnn38u06ZNk169egXMc7lcsnjxYomPj5eZM2f6p3fs2FHq168fsKzH45FBgwbJt99+Kzk5OSEbH0KHWq56LZenefPmIiIBl16uWbNGXC6XjB071j/N6/XKHXfcIVu3bpXDhw/7p1PLCBVquXq1XFxcLBMnTpSJEydKq1atzrt8Tk5O0DrP1rJlS8nIyJADBw7IwoULxel0SmJiYpXGUhWLFy+Wo0ePSkZGRtB4IyIiZPny5SIiMnv2bP/07t27S3R0dMCy9erVk7S0tLp13H2xO+jKVPQpxoIFC1REdNGiRf5pGzZsULfbrW3atNFHHnlE77vvPq1fv74mJCT4Px3auXOn3nLLLSoiOm/ePP8p/bJLb3r06KFjxozRefPm6V//+le9/vrrVUR0wYIF/vWsXbtWmzZtqu3atfPn33rrLVX9v7MhmzZtCnoOPXr00Hnz5um0adM0IiJCmzdvridPnvQvN3r0aPV6vdqxY0e9/fbbddGiRTp8+HAVkaDT+6NHj67Sp15///vfVUR0+/btQfOaNm2qw4YN8//f5/Pp0KFDVURURLRfv35Gl5RU5K233lIR0VmzZlW4TNnrN2PGjCo91sqVK0M2PoQOdVuzuj1XVc7ARkdHq4ioy+XSa6+9ttxPfj/77DNt3Lixioi63e6A1ycUbr31VhURPXjwYIXLlL0G534SXN5jRUZGaklJSUjHiOqhlkNTy8XFxXrs2DH97rvv9M0339R27dppTEyMnjhxwr/MgAEDyr18cOPGjSoimpmZ6Z9GLaO6qOXQ1PIjjzyiycnJmp2dXeFrWja9bL8cHh6u6enp+r//+79Bj/fOO+9oXFyciohGRkbqyy+/XKVxVFXv3r3V6/VqQUFBhcv07dtXw8LCznvM37t3b23Tpk1Ix1cTVjSwGzdu1GPHjunhw4d1zZo1mpSUpB6PRw8fPuxfNjU1VZOTkwN2CDt37lSn06mjRo3yT6vskoe8vLygaQMHDtSWLVsGTKvogPLcgisqKtLk5GTt1KlTwBvj1VdfVRHRv/zlL/5pZUU0e/bsgMe84oortHv37gHTqlpwZc/10KFDQfN69OihP/vZz4Km7969W3fu3Bnyvy+dP3++ioiuXbu2wmWysrJURAIa6/IMGDBAY2NjAzZYqDuo25rV7bkqa2A//PBDHT58uD777LO6bt06nTt3rtarV0+9Xq9+8sknQcvn5ubq9u3b9ciRI9UaQ1WkpqZqXFxcpcs8/vjjQQfj5/rqq6/U6/Xqf//3f4d4hKguajk0tbx161b/h8Miom3btg04MC97Tv379w/KfvHFFyoi+tRTTwVMp5ZRHdRyzWv5yJEjGhMTo4sXL1bVij8UWLVqlY4ZM0aXL1+ua9eu1RkzZmhkZKTWr1+/3OPxU6dO6bZt2wJe71CJj4/Xrl27VrrMhAkTVER0165dFS7z/vvvq8Ph0P/v//v/QjxCc1ZcQjxgwABJSkqSlJQUufHGGyUqKkoyMzOladOmIvLTJQCffvqpjBkzJuDUe5cuXeTnP/+5rF+/vkrriYiI8P87Oztbjh8/Ln379pX9+/dLdnZ2tce9Y8cO+fHHH2X8+PHi9Xr902+44QZp165duXfyGzduXMD/09LSZP/+/QHTli1bJqrqvwypIvn5+SLy02U85/J6vf75Z2vfvr106dJFHA5HpY9dXWWXD8XExFS4TNm8yi41evDBB2Xjxo3y0EMPSXx8fEjHiNCibs3qtjp69+4ta9askdtvv10GDx4s06ZNk23btonD4ZB77703aPnIyEjp2bOnNGzYMGRjKJOTk1NpfYucv8bz8vJkxIgREhERIQ899FDIxwgz1HLNarlDhw6yYcMG+de//iVTpkyRqKiooLsQ5+fnV7ivLpt/NmoZJqhl81qeOnWqtGzZUu68885Kl7vppptk6dKlMmrUKBk6dKjcf//98uabb8qJEyfkgQceCFo+Li5OevXqFdJLh8uEopZ//PFHufXWW6VFixYyZcqUkI/RlPv8i1x8f/vb36RNmzaSnZ0tzz33nLz//vsBG/pvvvlGRETatm0blG3fvr28+eabkpubK1FRUZWu58MPP5SZM2fK1q1bg77SJTs7W+Li4qo17srG1a5dO9m8eXPANK/XK0lJSQHTEhIS5OTJk9Vab5myDUh53y9VUFAQsIExkZWVJUVFRQHrq+g1qkpzWjYvOTm53PmrVq2SGTNmyB133CF33XWX6bBRS6hbs7qtqdatW8uQIUPk5ZdfFp/PJy6Xy+hxfD6fHDt2LGBaYmKihIeHl7t8TEyMHD9+vNLHrKzGfT6f3HzzzbJ79255/fXXpXHjxkbjRuhRyzWr5djYWBkwYICIiAwZMkRWrlwpQ4YMkU8++US6du0qIj/tPyvaV5fNN0Utowy1bFbL27ZtkxUrVsjbb79d7ftTiIhcffXV0qtXrxp/rVRRUZFkZWUFTEtKSqpwPx8TE3Pevz+vrJZzc3Pll7/8peTk5MjmzZuD/jb2YrLiDGzPnj1lwIABMnz4cMnMzJROnTrJrbfeWu73qJnat2+fXHfddXL8+HF5/PHH5bXXXpMNGzbIPffcIyI/fSfphWZ6oFmRRo0aichPn6id68iRIzXeqQwbNkwaNWrk/5k4cWKFy3bo0EFEpNLvuCqb17Jly6B5GzZskFGjRskNN9wgTz31VI3GjdpB3V48KSkpUlRUJLm5ucaPcfjw4YD6btSoUbnfGVemQ4cOkp2dLYcOHapwmcpq/Le//a28+uqrsmzZMunfv7/xuBF61HJoDRs2TERE/vnPf/qnNWrUqMJ9tYjUaH9NLaMMtWxmypQpkpaWJi1atJCDBw/KwYMH/R/yHDlypNJaKZOSkhLUfFbXli1bgmr57Bu8natDhw6yd+/ecj8cK7Nr1y4JDw+XJk2aBEwvKiqSYcOGya5du2TdunXSqVOnGo091Kw4A3s2l8slc+fOlX79+smCBQtk2rRp/rvv7d27N2j5PXv2SP369f2fFlV0aewrr7wihYWFkpmZKc2aNfNP37RpU9CyVb289uxxnbsR37t3b0jvGlie1NRUEfnp0ouePXv6p3///ffy7bffBtzt0MRjjz0W8GlWZTvYPn36SHx8vKxcuVKmT59e7sbl+eefFxGRESNGBEzfvn27/OpXv5Irr7xSXnzxxYA7KsMO1G3t2r9/v3i93hp9WtqwYUPZsGFDwLSys0XlSU9Pl5UrV8rzzz8vM2bMCJp/+vRpWbdunXTr1i3ooHfy5MmydOlSmT9/vtxyyy3GY8aFRy3XXGFhoZSWlgZcSpmamiqbNm2S06dPS2xsrH/69u3b/fNNUcsoD7VcdYcOHZJvvvlGWrRoETRv8ODBEhcXF3BX8fLs378/6KxwdXXt2jWoliv7M4L09HTZsmWLrF69WkaOHBk0/+DBg/LBBx/IkCFDAq7yKC0tlVGjRsnbb78tL774ovTt27dG474gLu6f4Fausu9S6tmzpzZo0MD/x9ypqanaoEGDgBv7fPbZZ0F/dL5o0SIVEf33v/8d8HhPPvlk0F33Tp06pY0aNQr6A+9evXqV+0fRFf3ReZcuXQLuALZ+/fpy/+g8Kioq6DFnzpyp5/6avv/+e/3yyy+1qKgoaPlztWvXTrt27RpwB8AZM2aow+HQ3bt3nzcfSmXfA3vuF6Cr/vSH+E6nU9PT0wOm7969W+vVq6cdO3bUrKys2hoqaoC6rXndnq2ymzj9+OOPQdM+/fRTDQsL08GDB1drPTVVVFSkHTt21KioqKDfvc/n89/Z9KWXXgqY98gjj6iI6J///OfaHC6qgFquWS2fPHmy3GXKvgf22Wef9U/btm2byjnfA1tQUKCtW7fWXr16VbqeUKOWLz3Ucs1q+c0339S1a9cG/Nx9990qIvroo4/qq6++6l+2vP3ya6+9piKiEyZMqHQ9oVb2PbANGzbUffv2BczLz8/Xa6+9Vt1ud9D3PY8fP15FxH/DqrrI2gZ29erVAbf+Lrvtd7t27TQjI0Nnz56tSUlJmpCQoPv37/fnPvroIxURHTRokD7//PP6j3/8Q8+cOaN79uzR8PBw7dy5sy5YsEAfeughbdWqlXbt2jWo4MaPH68Oh0Pvv/9+/cc//qFvv/22qlZ+2+9evXrp/Pnz9d5779XIyMhyb/td1YKrzl3TXnnlFXU4HNq/f39dsmSJTpgwQZ1Op/72t789bzbUSkpK9KabblIR0WuuuUafeOIJXbJkiY4aNUqdTqd27tw54C5sp0+f1pSUFHU6nfrQQw/5b7Ne9rNly5Zafw44P+q25nX73nvv6f3336/333+/Jicna/Pmzf3/f++99/zL9evXTwcNGqRz5szRJUuW6B/+8AeNjIzUuLi4Wv+ASlV1z5492rhxY/V4PDpu3Dh95pln9NFHH9Vu3bqVe2D78ssvq4jo5ZdfHlTfK1as0KNHj9b6c8D/oZZrVstr167VlJQUveeee3ThwoU6f/58HT58uDocDr3yyiu1sLAwYPkRI0ao2+3WyZMn6+LFi7V3797qdrsDar62UMuXFmq55vvlqr6mrVu31hEjRujDDz+sTz31lI4dO1bdbrempKRclDrYvHmzxsbGalxcnP7pT3/SZ599Vh944AG9/PLL1eVyBXyFkqrqvHnzVET0qquuKreWy74q6WKztoH1+XzaqlUrbdWqlf/s4saNG7VPnz4aERGhsbGxmp6eXu5B3P33369NmjRRp9MZ8MbNzMzULl26qNfr1ebNm+vDDz+szz33XNCb++jRo3rDDTdoTEyMioj/7Eh5Baf60y21r7jiCvV4PJqYmKi/+c1v9Ntvvw1Y5kIW3Nq1azU1NVU9Ho82bdpUZ8yYUe2zQKFSWlqqy5Yt0z59+vhfPxHRAQMGBO3MDxw44J9f3s/o0aMvynNA5ajbmtdtWb68n5kzZ/qXe+KJJ7Rnz56amJiobrdbGzVqpCNHjtSvvvrqvOu4UI4dO6Z/+tOftHXr1hoeHu4f99lnm8pU9jzL+52gdlHLNavlr7/+WkeNGqUtW7bUiIgI/3dTzpw5s9yDwPz8fJ00aZI2bNhQPR6P9ujRQ994441K13EhUcuXDmq59hrY6dOn+7+KKiwsTJs1a6Z33XXXRf0Q5+DBgzp27Fht1qyZut1uf11u3LgxaNmy16SiH5PX6kJwqKoKcJEUFxdLenq6vP322/LKK6/IL37xi4s9JAAh9Nlnn0laWpqkpKTI5s2bq333SQB1A7UMXBrefvttGTRokFx99dXy+uuvV3hH8rrMirsQ49IVFhYmL730kqSmpsqIESPkk08+udhDAhBCnTt3lnXr1slXX30lQ4cODfjqLQD2oJaBS8N1110ny5cvl02bNsltt90mNp7L5AwsAAAAAMAKnIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFjBXdUFHQ7HhRwHYA3bb9xNLQM/oZaBS4PttSxCPQNlqlLPnIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWcF/sAQCoXS6Xyyjn8/lCPJLKORwOo5yqhngkAAAAqCs4AwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsIL7Yg+gIomJiUY5l8tllDt27JhRDrBNTEyMUa64uNgo53abbWZU1SiXn59vlDN9fsDFEhsba5QrLS01ypnWSGFhoVEO+E8SHh5ulPP5fEY5p9PsHJZpznSfbvr8TPqBoqIio3Wh9nEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBXdVF2zQoIHRClwul1HOVHFxsVEuKSnJKOfz+YxyJrKysmptXbh0eb1eo1xERIRRrqSkxCjncDiMch6PxyiXn59vlMvLy6t2prS01GhdwNkiIyONcqb7rdpen+n+vDbXV1RUZLQu4FxxcXG1uj6n0+wclum+WVXrfC4nJ8doXQUFBUY5mOMMLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACu6qLhgbG2u0gtLSUqNcUVGRUS4sLMwop6pGuYKCglpbX1JSktG6jh07ZpTDpSkiIsIo53Safd5lmjPddhQXFxvlTMcZHh5e7UxeXp7Rukyfm8/nM8qhbmvYsKFRzu2u8q4/QFZWllEuPz/fKGc6TofDYZQzUVJSYpQzrWXTnOk2B7XH4/HU6vpMj3tN982m+9jaHGdcXJzRuky3OabbRnAGFgAAAABgCRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBXdVF+zcubPRCg4fPmyUO3HihFEuLCzMKOdwOIxyUVFRRrnc3NxqZ/Lz843WlZSUZJQ7duyYUQ51m9td5bIPYPpeT0lJMcrVq1fPKGe6zTHNnT59utoZ099BcXGxUa6goMAoZ7rNQe246qqrjHKFhYVGue+//94od/ToUaNcTk6OUa6oqMgoV1JSUu2MaS27XC6jXHh4uFHO5LmJmL+WqL4WLVoY5UzrxOfzGeVU1Shnuh8yfQ+a5EpLS43WZXp8VNuv5aWEM7AAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACu4q7pgSUmJ0QqSk5ONcidPnjTKlZaWGuXatGljlLvsssuMcseOHat25uOPPzZaV15enlGufv36Rrnjx48b5VA7VNUoFxYWZpSLj483ykVFRRnlmjVrZpQrLi42yplsc3Jzc43W5XSafeYYHh5ulDP9HZw4ccIoZ/re/E9lul82reWkpCSjnOk+yOv1GuUKCwuNcmfOnKm1dZlub0zFxMQY5UxrGdVnesxl+l6KiIgwyvl8PqOc6fbddH956tSpamdMt1WmHA5Hra7vUsIZWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFdxVXdDj8Rit4IcffjDK+Xw+o1xERIRRrl69ekY5h8NhlEtKSqp2pnnz5kbr+uqrr4xyxcXFRrnk5GSj3I8//miUQ/WoqlEuPz/fKJeVlWWUc7urvHkKibCwMKNcYmJira3rzJkzRrmSkhKjXG1u30REsrOzjXKFhYVGOdsdOnTIKBcbG2uUKyoqMsqZbnNMjztMc5GRkdXOmL5njx8/bpQz5XSana/wer1GuYKCAqPcfzLT7Vht709M9wumx4Ym+1gRkfj4+GpnvvnmG6N15ebmGuVMexbT90ppaalRri7iDCwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALCCu6oL7t6922gFPp/PKBcZGVmr6zt06JBRrnnz5ka58PDwame8Xq/RuuLj441yWVlZRjlVNco1atTIKHfkyBGj3H8q0/dRaWmpUe7o0aNGudzcXKOcSW2JiBQVFRnlIiIiqp3xeDxG63K7q7zJDnD69GmjnOlrYiosLMwoV1hYGOKR2OHHH380yp08edIoZ/r+KykpMcoVFBQY5UxqUkSkfv361c6Y1rLD4TDKHT9+3Chnuv02fX6mv7v/ZKbHXKavdX5+vlEuNjbWKGf6HjQ9ZjHJRUVFGa3LdB9k+po4nWbnH03XVxdxBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAV3VRcsKioyW4G7yqsICdP1nTp1yij3zTffGOWioqKqnTEdY2xsrFGuuLjYKJeTk2OU8/l8RrlGjRoZ5Y4cOWKUs52q1ur6CgsLjXKm7weXy2WUq00ej8col5CQYJQzfS1Nf3dOp9lno16v1yh35swZo5ztTLfRBQUFRrnS0lKjXElJiVEuLCzMKBcfH2+UMzl+MN3/mG6Hs7OzjXKmvzvTYz9U38mTJ41ypttph8NhlDPd7pjmTI998/Pzq50xHaNp72G6b2ZfyRlYAAAAAIAlaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAV3FVdMD8/32gFTqdZjxwWFmaU83q9RrmioiKj3OnTp41yBQUF1c64XC6jdUVFRRnlHA6HUa6wsNAoV1xcbJTz+XxGueTkZKOc7XJzc41ypu8/U6WlpbW6PtNtjkl9xcfHG63LdPtWUlJilMvOzjbKmdayqdp+b9YVeXl5RjnT/Z3p/tz092P6PjJdn8lxjun+x/R3YLqdMl1fZGSkUc70mPE/WW3vm93uKrcAAUyOX0VETp48aZQzPRY1OYYwrWdTpvUcERFhlDPdZ9T28VhVcAYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFd1UXdLlcRisoLCw0yhUXFxvl8vPzjXJOp1kvn5uba5TzeDzVzjgcDqN1FRQUGOXi4+ONcjExMUa5EydOGOVMXxefz2eUs51pTZq+zqa1ZZpzu6u8WQtg+n6IiIiodqa0tNRoXSbbDRGR2NhYo1xkZKRR7vTp00Y5VTXKmb5XbFfb+ztTpscPYWFhRrns7GyjnNfrNcqZsKVGTPcXqD7T361pznRfabpfMH0vmW4/wsPDq52Ji4szWpfpNsd0Gw7OwAIAAAAALEEDCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArOCu6oL169c3WsHRo0eNcqWlpUY5h8NhlHM6zXp501xRUVG1M4mJiUbratKkiVHO5XIZ5bKzs41yqmqUM+V2V/ntf0nxer1GueLi4hCPpHKmtWz6ezXNmWyrTOpfROTUqVNGuby8PKOc6fbN9Hdn+rrU9nuzrjD9/ZgKCwszysXGxhrlTJ9fQUGBUc7n81U7YzpG0/es6bGR6fYtIiLCKJebm2uUq+3jgEuBx+MxyiUnJxvlTI8Nz5w5Y5Qz3Z80bty42hnT959pPZtsc0TM95Wm24+6iDOwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAruKu6YHR0tNEKvF6vUa6oqMgoV1paapRzOs16+fDwcKOcqlY7U79+faN1FRcXG+USEhKMcvHx8Ua5I0eOGOVM3ysmv4NLgenvJycnxygXERFhlIuJiTHKmSooKDDK5efnVzvj8/mM1mW6nTpz5oxRzrS2TIWFhdXq+mxnun/1eDxGuSZNmhjlTJWUlBjlTLftbneVD4n8TLenWVlZRrnjx48b5UxfE9Pjh//U/WtNmNZzUlKSUc50O+BwOIxypsfLeXl5RjmT/ZfJNkDE/Hg5NzfXKGf6O7iUcAYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFd1UXLCwsNFqBy+UyyjVu3Ngol5CQYJTLzc01yuXn59daLi8vz2hdXq/XKHfq1CmjXHFxsVFOVY1yDoejVtdnO9OajIiIMMrVq1fPKOfxeIxyps/vxIkTRrnafB+dOXPGKGe6/a7tGsnOzq7V9dkuKirKKOd2V3nXH8D0/WCaczrNPmMvLS01ysXFxVU7Exsba7SuoqIio1xOTo5RzufzGeVM9+eovvDwcKOc6e/WtC5N37umx2olJSVGuSNHjlQ7U79+faN1ZWVlGeVMX0vT94rp8ZHpe+xC4gwsAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKDlXViz0IAAAAAADOhzOwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINbAi9++674nA45N13373YQzFWWloqJSUlF3sYQK25FOq2OgoLCy/2EIALgloGLg3UMs6nTjewy5YtE4fD4f9xu93SpEkTGTNmjHz33XdGj7llyxaZNWuWnDp1ynhcCxculGXLlhnna1tmZqZ069ZNvF6vNGvWTGbOnFluk/rII49IbGysxMbGytSpUy/IWFRVVqxYIddcc43Ex8dLZGSkdO7cWebMmSN5eXlByz/99NPSt29fadCggXg8HmnRooXcdtttcvDgwQsyPtQcdVtzq1atkpEjR8rll18uDodDrr322nKXK9vJl/ezbdu2gGVPnDghAwcOlMjISGnRosUFOzA4ceKETJ48Wdq2bSter1cSExNl4MCB8tprrwUtm5+fL3fccYd06tRJ4uLiJDo6Wrp27SpPPPGEFBcXX5Dxoeqo5Zq75557pFu3bpKYmCiRkZHSvn17mTVrlpw5cyZo2cLCQpk6dao0btxYIiIipFevXrJhw4ag5ahlVBe1HFr79u0Tr9crDodDduzYETDv3Nf67J+jR48GLHvgwAHp1auXeL1e6dKli+zateuCjPfQoUMybtw4ad68uXg8HklOTpZf/epXsmXLlqBlv//+exk5cqS0bdtWYmJiJD4+Xnr27CnLly8XVb0g4zPhvtgDqIrZs2dLixYtpKCgQLZt2ybLli2TzZs3y+effy5er7daj7Vlyxa57777ZMyYMRIfH280noULF0r9+vVlzJgxAdOvueYayc/Pl/DwcKPHvRBef/11GTp0qFx77bXy17/+VT777DOZM2eO/Pjjj7Jo0SL/ch9++KHMnTtXHn/8cYmMjJQ///nP0r17d7nppptCNhafzye33nqrvPjii5KWliazZs2SyMhI+eCDD2TmzJny4osvysaNGyU5Odmf+fe//y0tWrSQwYMHS0JCghw4cECefvppefXVV2Xnzp3SuHHjkI0PoUXdmlu0aJH8z//8j/To0UNOnDhx3uUnTJggPXr0CJjWunXrgP9PmzZNCgoK5KWXXpIdO3bITTfdJAcOHJCoqKiQjXvv3r1y3XXXybFjx+S2226TK6+8Uk6dOiUvvPCC/PKXv5SpU6fKQw895F8+Pz9fvvjiCxk0aJA0b95cnE6nbNmyRe655x7Zvn27rFy5MmRjgzlq2dzHH38saWlpctttt4nX65V///vf8tBDD8nGjRvl/fffF6fz/84jjBkzRtasWSN/+MMf5PLLL5dly5bJoEGDZNOmTXL11Vf7l6OWYYpaDo177rlH3G53pWdOy17rs537Oo0dO1aaNm0q06dPl9dee01uvvlm2b17d0jH+uGHH8qgQYNEROTOO++UDh06yNGjR2XZsmVy9dVXy9/+9je56667/MsfP35cvv32W7nxxhulWbNmUlxcLBs2bJAxY8bI3r175cEHHwzp+IxpHbZ06VIVEf34448Dpk+dOlVFRFetWlXtx8zIyFAR0QMHDhiPq2PHjtq3b1/jfG3q0KGDdu3aVYuLi/3Tpk+frg6HQ7/88kv/tIyMDP3DH/7g///8+fP197//fUjH8uCDD6qI6KRJk4LmZWZmqtPp1EGDBp33cXbs2KEionPnzg3p+BAa1G3NHTp0SH0+n6pWPu5NmzapiOjq1avP+5gdO3bUTz/91P//1NRU3bFjR0jGq6paVFSknTp10sjISN22bVvAvJKSEv31r3+tIqIvvvjieR/r97//vYqIHjlyJGTjQ/VRyxfGo48+qiKiW7du9U/bvn27iohmZGT4p+Xn52urVq30qquuCshTy6guajl03njjDQ0PD9cZM2aU+5pW9FqXJyoqSk+dOuX/f0JCgh4/fjxkY83KytKGDRtqgwYN9Ouvvw6Yl5eXp2lpaepyuQK2RRX55S9/qVFRUVpSUhKy8dVEnb6EuCJpaWki8tMp/LO98847kpaWJlFRURIfHy9DhgyRL7/80j9/1qxZMnnyZBERadGihf+UftnlqEuXLpX+/ftLcnKyeDwe6dChQ8BZShGR5s2byxdffCHvvfeeP192eV9F1+yvXr1aunfvLhEREVK/fn0ZOXJk0CUbY8aMkejoaPnuu+9k6NChEh0dLUlJSTJp0iTx+XwByx45ckT27Nlz3stydu/eLbt375axY8eK2/1/J9vHjx8vqipr1qzxT2vZsqVkZmbKtm3bZOfOnfLCCy/I5ZdfXunjV0d+fr5kZGRImzZtZO7cuUHz09PTZfTo0bJ+/Xr56KOPKn2s5s2bi4jU6LIV1D7qtmp1KyKSkpIScGamKnJycir9+/WWLVvKk08+Kfv27ZPVq1fL119/LZdddlm11lGZl156ST7//HOZNm2a9OrVK2Cey+WSxYsXS3x8vMycOfO8j0WN123UctVruTzlvb/XrFkjLpdLxo4d65/m9XrljjvukK1bt8rhw4f906llhAq1XL1aLi4ulokTJ8rEiROlVatW510+JycnaJ1na9mypWRkZMiBAwdk4cKF4nQ6JTExsUpjqYrFixfL0aNHJSMjI2i8ERERsnz5chH56Wzx+TRv3lzy8vKkqKgoZOOrkYvdQVemok8xFixYoCKiixYt8k/bsGGDut1ubdOmjT7yyCN63333af369TUhIcH/6dDOnTv1lltuURHRefPm6YoVK3TFihV65swZVVXt0aOHjhkzRufNm6d//etf9frrr1cR0QULFvjXs3btWm3atKm2a9fOn3/rrbdU9f/OhmzatCnoOfTo0UPnzZun06ZN04iICG3evLmePHnSv9zo0aPV6/Vqx44d9fbbb9dFixbp8OHDVUR04cKFAc9/9OjRVfrU6+9//7uKiG7fvj1oXtOmTXXYsGH+//t8Ph06dKiKiIqI9uvXT/Pz8yt9/Op46623VER01qxZFS5T9vrNmDEjaN7x48f1hx9+0I8//ljT09NVRPyvO+oW6rZmdXuuqpyBjY6OVhFRl8ul1157bbmf/H722WfauHFjFRF1u90Br08o3HrrrSoievDgwQqXKXsNzv0kuLCwUI8dO6aHDh3Sl19+WRs2bKiXXXZZwJUjqH3Ucmhqubi4WI8dO6bfffedvvnmm9quXTuNiYnREydO+JcZMGCAtm/fPii7ceNGFRHNzMz0T6OWUV3Ucmhq+ZFHHtHk5GTNzs6u8DUtm162Xw4PD9f09HT93//936DHe+eddzQuLk5FRCMjI/Xll1+u0jiqqnfv3ur1erWgoKDCZfr27athYWFBx/x5eXl67NgxPXDggC5btkyjoqK0d+/eIR1fTVjRwG7cuFGPHTumhw8f1jVr1mhSUpJ6PB49fPiwf9nU1FRNTk4O2CHs3LlTnU6njho1yj+tskse8vLygqYNHDhQW7ZsGTCtogPKcwuuqKhIk5OTtVOnTgFvjFdffVVFRP/yl7/4p5UV0ezZswMe84orrtDu3bsHTKtqwZU910OHDgXN69Gjh/7sZz8Lmr57927duXOnlpaWVvrY1TV//nwVEV27dm2Fy2RlZamIBDTWZTwej7+5rlevnj755JMhHR9Ch7qtWd2eq7IG9sMPP9Thw4frs88+q+vWrdO5c+dqvXr11Ov16ieffBK0fG5urm7fvv2CXM6XmpqqcXFxlS7z+OOPBx2Mq6r+4x//8Ne3iOiVV16pu3btCvkYUT3UcmhqeevWrQHv77Zt2wYcmJc9p/79+wdlv/jiCxURfeqppwKmU8uoDmq55rV85MgRjYmJ0cWLF6tqxR8KrFq1SseMGaPLly/XtWvX6owZMzQyMlLr169f7vH4qVOndNu2bQGvd6jEx8dr165dK11mwoQJKiJBdTp37tyAWr7uuuvKHf/FYsUlxAMGDJCkpCRJSUmRG2+8UaKioiQzM1OaNm0qIj9dAvDpp5/KmDFjAk69d+nSRX7+85/L+vXrq7SeiIgI/7+zs7Pl+PHj0rdvX9m/f79kZ2dXe9w7duyQH3/8UcaPHx/wx/E33HCDtGvXrtw7+Y0bNy7g/2lpabJ///6AacuWLRNV9V+aU5H8/HwREfF4PEHzvF6vf/7Z2rdvL126dBGHw1HpY1dXTk6OiIjExMRUuEzZvLJlz/b666/L+vXr5bHHHpNmzZpJbm5uSMeH0KNuzeq2Onr37i1r1qyR22+/XQYPHizTpk2Tbdu2icPhkHvvvTdo+cjISOnZs6c0bNgwZGMok5OTU2l9i1Rc4/369ZMNGzbI6tWrZdy4cRIWFkaN1yHUcs1quUOHDrJhwwb517/+JVOmTJGoqKiguxDn5+dXuK8um382ahkmqGXzWp46daq0bNlS7rzzzkqXu+mmm2Tp0qUyatQoGTp0qNx///3y5ptvyokTJ+SBBx4IWj4uLk569eoV0kuHy9Sklm+55RbZsGGDrFy5Um699VYRCd4OXUxW3IX4b3/7m7Rp00ays7Plueeek/fffz9gQ//NN9+IiEjbtm2Dsu3bt5c333xTcnNzz3uHvg8//FBmzpwpW7duDfpKl+zsbImLi6vWuCsbV7t27WTz5s0B07xeryQlJQVMS0hIkJMnT1ZrvWXKNiDl3SWtoKAgYANjIisrK+Ba+IiIiApfo8qa0zJl886+C3GZfv36iYjIf/3Xf8mQIUOkU6dOEh0dLb///e+Nx48Li7o1q9uaat26tQwZMkRefvll8fl84nK5jB7H5/PJsWPHAqYlJiZWeFfImJgYOX78eKWPWVGNN2jQQBo0aCAiIjfeeKM8+OCD8vOf/1y++uqrC3KAjuqhlmtWy7GxsTJgwAARERkyZIisXLlShgwZIp988ol07dpVRH7af1a0ry6bb4paRhlq2ayWt23bJitWrJC333672venEBG5+uqrpVevXrJx40aj9ZcpKiqSrKysgGlJSUkV7udjYmIqPe4WqbiWL7vsMv/f1t9yyy0yduxYGTBggOzdu7fG/UMoWHEGtmfPnjJgwAAZPny4ZGZmSqdOneTWW28t93vUTO3bt0+uu+46OX78uDz++OPy2muvyYYNG+See+4REZHS0tKQrasipgeaFWnUqJGI/PSJ2rmOHDlS46+gGTZsmDRq1Mj/M3HixAqX7dChg4hIpd9xVTavZcuWla63VatWcsUVV8gLL7xgMGrUFur24klJSZGioqIanfk4fPhwQH03atSo3O+MK9OhQwfJzs6WQ4cOVbhMVWv8xhtvlDNnzsi6devMBo+QopZDa9iwYSIi8s9//tM/rVGjRhXuq0WkRvtrahllqGUzU6ZMkbS0NGnRooUcPHhQDh486P+Q58iRI5XWSpmUlJSg5rO6tmzZElTLZ9/g7VwdOnSQvXv3Vvp1P7t27ZLw8HBp0qRJpeu+8cYb5fDhw/L+++8bjz+UrDgDezaXyyVz586Vfv36yYIFC2TatGn+Twj27t0btPyePXukfv36/k+LKro09pVXXpHCwkLJzMyUZs2a+adv2rQpaNmqXl579rj69+8fMG/v3r0hvWtgeVJTU0Xkp0svevbs6Z/+/fffy7fffhtwt0MTjz32WMCnWZXtYPv06SPx8fGycuVKmT59erkbl+eff15EREaMGHHedefn51dakKhbqNvatX//fvF6vRIdHW38GA0bNpQNGzYETCs7W1Se9PR0WblypTz//PMyY8aMoPmnT5+WdevWSbdu3c570Ft2mZLJpWa4sKjlmissLJTS0tKA93dqaqps2rRJTp8+LbGxsf7p27dv9883RS2jPNRy1R06dEi++eaboO91FREZPHiwxMXFnfdO2/v37w86K1xdXbt2Darlyq5sSE9Ply1btsjq1atl5MiRQfMPHjwoH3zwgQwZMuS8Z1XrWi1bcQb2XNdee6307NlT5s+fLwUFBdKoUSNJTU2V5cuXB7yBPv/8c3nrrbf8X+ArIv7CO/eNVtZQqap/WnZ2tixdujRo/VFRUVW6JfyVV14pycnJ8tRTTwU0W6+//rp8+eWXcsMNN1Tl6Qap6m2/O3bsKO3atZMlS5YE3MZ70aJF4nA45MYbbzRaf5nu3bvLgAED/D9lZ1nLExkZKVOmTJG9e/fK9OnTg+a/9tprsmzZMklPT5fOnTuLiEhJSUm5l3t89NFH8tlnn8mVV15Zo/GjdlG3NfvqjfKce1mgiMjOnTslMzNTrr/+eqNLncp4vd6A+h4wYIAkJCRUuPzw4cOlY8eO8tBDD8mOHTsC5pWWlspdd90lJ0+eDKj/48ePB/zuyjzzzDMiItR4HUUtV62WT506Ve4y5b2/b7zxRvH5fLJkyRL/tMLCQlm6dKn06tVLUlJSjMYqQi2jYtRy1Wp5yZIlsnbt2oCfu+++W0REHn300YArAsvbL69fv17+53/+R37xi18YjbNMQkJCUC2f/TfB5/rd734nDRs2lMmTJwf9/W9BQYHcdttt4nA4ZMqUKZWOX0Tk2WefFYfDId26davRcwiZi3X3qKqo7MuAV69eHXDr77Lbfrdr104zMjJ09uzZmpSUpAkJCbp//35/7qOPPlIR0UGDBunzzz+v//jHP/TMmTO6Z88eDQ8P186dO+uCBQv0oYce0latWmnXrl2D7lA2fvx4dTgcev/99+s//vEPffvtt1W18tt+9+rVS+fPn6/33nuvRkZGlnvb76ioqKDnOXPmTD3311Sdu6a98sor6nA4tH///rpkyRKdMGGCOp1O/e1vf3vebKiVlJToTTfdpCKi11xzjT7xxBO6ZMkSHTVqlDqdTu3cuXPAXdhOnjypUVFRevvtt+tjjz2mTz31lP6///f/NDIyUhMTE8u9JTkuPuq25nX73nvv6f3336/333+/Jicna/Pmzf3/f++99/zL9evXTwcNGqRz5szRJUuW6B/+8AeNjIzUuLg43b1793nXE2p79uzRxo0bq8fj0XHjxukzzzyjjz76qHbr1k1FRP/85z8HLD9v3jxt27atTp06VRcvXqyPPvqo/vznP1cR0fT09FofPwJRyzWr5bVr12pKSorec889unDhQp0/f74OHz5cHQ6HXnnllVpYWBiw/IgRI9TtduvkyZN18eLF2rt3b3W73QE1X1uo5UsLtVzz/XJVX9PWrVvriBEj9OGHH9annnpKx44dq263W1NSUvTo0aPVXk9Nbd68WWNjYzUuLk7/9Kc/6bPPPqsPPPCAXn755epyuQK+QklVdeLEiXrllVfqjBkzdMmSJfrQQw9pjx49VET07rvvrvXxV8TaBtbn82mrVq20VatWWlJSoqo/fV9anz59NCIiQmNjYzU9Pb3cg7j7779fmzRpok6nM+CNm5mZqV26dFGv16vNmzfXhx9+WJ977rmgN/fRo0f1hhtu0JiYGBUR/y3Ayys41Z9uqX3FFVeox+PRxMRE/c1vfqPffvttwDIXsuDWrl2rqamp6vF4tGnTpjpjxgwtKiqqUjbUSktLddmyZdqnTx//6yciOmDAgKCdeWFhoU6cOFG7dOmisbGxGhYWppdddpnecccdRhsb1A7qtuZ1W5Yv72fmzJn+5Z544gnt2bOnJiYmqtvt1kaNGunIkSP1q6++Ou86LpRjx47pn/70J23durWGh4f7x/3ss88GLfvxxx/riBEjtFmzZurxeDQqKkq7deumjz/+ON8bWQdQyzWr5a+//lpHjRqlLVu21IiICP93U86cOdP/fZlny8/P10mTJmnDhg3V4/Fojx499I033qh0HRcStXzpoJZrr4GdPn26/6uowsLCtFmzZnrXXXddlOa1zMGDB3Xs2LHarFkzdbvd/lreuHFj0LJvvfWW/vKXv9TGjRtrWFiYxsTEaJ8+fXTp0qUh/4rNmnColnPNB1BLiouLJT09Xd5++2155ZVXanx5BYC65bPPPpO0tDRJSUmRzZs3V/vukwDqBmoZuDS8/fbbMmjQILn66qvl9ddfr/CO5HWZlX8Di0tHWFiYvPTSS5KamiojRoyQTz755GIPCUAIde7cWdatWydfffWVDB06NOCrtwDYg1oGLg3XXXedLF++XDZt2iS33XZbuX+/XtdxBhYAAAAAYAXOwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArOAOxYM4HI5QPEyVmX73WERERK2uLz4+3igXFRVllHO7zX6dJ06cMModOXLEKOdyuYxypqKjo41yu3fvDvFI6r7armXTmkxISDDKeb1eo5xpbZk+v9LSUqNcTk6OUa6goMAoZ/rdccnJyUY509/D1q1bjXI2s6WWIyMjjXKm++V69eoZ5Xw+n1Hu6NGjtbo+09+D6euSmJholDP11ltv1er66orarmePx2OUa9CggVGuSZMmRjlTx48fN8qdOnXKKGd63Fu/fn2jXMOGDY1yTqfZeU3T9+cbb7xhlBPhDCwAAAAAwBI0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAruiz0AE6pqlCsuLjbKlZSUGOWysrKMcg6HwyjncrmMcvn5+UY5p7N2P/8wXZ/p7x1VZ/q78Xg8IR5J5UxrJDY21igXFxdnlPP5fEY509+D6TbOdFv8448/GuXCw8ONcrjwioqKjHLR0dFGucLCQqOc6XvPtEZM37Om+62CggKj3JEjR4xyZ86cMcqVlpYa5VC3me67TEVGRhrlTPfpptsB0+Ps3Nxco5zpdi4hIcEoZ7odqAnOwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArOC+2AMwoapGudLSUqNcfn6+Uc50nDk5OUY5j8djlHM4HEY50+dnynScPp8vxCPBuUxrq6SkpFbXZ/qeNc2ZPj+Xy2WUCw8Pr9VccXGxUc4UtVx3eb1eo5zpe90053SafW5fr149o5zpfrmgoMAod+bMGaNcUVGRUS4vL88oZ/r7Q+0w3ceavh+OHTtmlEtKSjLKmR5PmtaJKdP+w3T7cfr0aaOc6fulJjgDCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwgvtiD8BETk6OUS4uLs4oV1RUZJRzuVxGOVPFxcVGOdPXJTY21ihXUFBglCssLDTKlZaWGuVw4fl8PqNcSUmJUc60ls+cOWOUM63JsLAwo1x+fr5RzrRGTHNut9muR1WNcrjw8vLyjHKRkZFGOdP9q9frNcpFR0fX6vqioqKMcg6HwyiXlZVllDNFLddttf37MT2uN90Hme6bTevZdJ9neoxkeixgemzldNb++VDOwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArEADCwAAAACwAg0sAAAAAMAKNLAAAAAAACvQwAIAAAAArOC+2AOoTdnZ2Ua5evXqGeVKS0uNcqaSk5ONcmFhYUY5j8djlHM6zT43yc/PN8qh7iopKTHKFRcXG+UKCgqMcqpqlMvJyanV9ZnWstfrNcrFxMQY5Ux/f3l5eUY5XHim7z3T/aTptsO0tnw+n1GutsdpmnO5XEY5U7W9PlRPbe+bTfdBRUVFRrnw8HCjXHx8vFEuKirKKGf6epoee5j2SbXd74hwBhYAAAAAYAkaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAX3xR6ADU6cOGGUS0xMNMp5PB6jXFhYmFFOVY1y+fn5RjmHw1GrOdPnhwuvpKTEKOfz+YxyRUVFRjlTpu9ZUy6Xyyjn9XqNchEREUa54uJio1xt//5Qdaa/G9Oc2212+GKay87ONso5nWbnCUy3jYWFhUa58PBwo5zpNqC2t42oHabHW6Y50/etac70+Nx0u2O6Ty8oKDDK1fY4a4IzsAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK7gv9gAuZVlZWUa5Ro0aGeVycnKMcomJiUa50tJSo9ypU6eMcsXFxUY5j8djlMOFp6pGuTNnzhjlvF6vUc5UWFiYFetzuVxGuaKiIqOc6e/d6eQz10tNYWGhUc7tNjt8Md1vmeZM91um63M4HEY5021jeHi4UQ6XptzcXKNcbe+7THP5+flGuZKSEqOcKdN+wHTfXNvHViKcgQUAAAAAWIIGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWMF9sQeAYEeOHDHKJSUlGeXy8/ONcj6fzyjncDiMcqZKSkpqdX2ouwoKCoxypu+h8PBwo1xkZKRRrri42ChnWssej8coV1hYaJQzfX6ou4qKioxyYWFhRjlVNcrl5uYa5ZzO2j1PYLp/Na1lW44DUDtMf6+m+1jTfUltv/9Mtzu1va90u83awtrezolwBhYAAAAAYAkaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFiBBhYAAAAAYAUaWAAAAACAFWhgAQAAAABWoIEFAAAAAFjBfbEHgNA5duyYUa5evXpGOZfLZZRTVaOc2232ds3PzzfKAWVKSkqMcj6fzyhXVFRklDOtLdPnZ1qTBQUFRjnT54dLj+l7qLi42CgXHh5ulAsLCzPKOZ1m5xdM98t5eXlGuYiICKOc6euCus10G236/jNlOk7T7Yfp+91031zbz8/0WKcmOAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALCC+2IPABffiRMnanV9sbGxRjlVNco5HA6jHFBTpu/ZkpISo5xpLefm5hrlIiIijHJut9mux+fzGeVw6TF9L5jmioqKjHIul8soZ7rfCg8Pr9X15eXlGeVMt424NJWWlhrlTN9/pkz3zcXFxUY5p9PsPGNtH/eePHmyVtcnwhlYAAAAAIAlaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVaGABAAAAAFaggQUAAAAAWIEGFgAAAABgBRpYAAAAAIAVHKqqNX4QhyMUYwHqlBCUhnWoZVSHy+Uyyvl8PqNcWFiYUa6oqMgoZzNqGdVh+n4x3QaYru8/sZZFqOdQczrNzt+53e4Qj+TCMH2/1HYuLy/PKCfCGVgAAAAAgCVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBVoYAEAAAAAVqCBBQAAAABYgQYWAAAAAGAFGlgAAAAAgBUcqqo1fhCHIxRjAeqUEJSGdahlXIqoZeDS8J9YyyLUMy5NNalnzsACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACjSwAAAAAAAr0MACAAAAAKxAAwsAAAAAsAINLAAAAADACg5V1Ys9CAAAAAAAzoczsAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDSwAAAAAwAo0sAAAAAAAK9DAAgAAAACsQAMLAAAAALACDWwIvfvuu+JwOOTdd9+92EMxVlpaKiUlJRd7GECtuRTqtjoKCwsv9hCAC4JaBi4N1DLOp043sMuWLROHw+H/cbvd0qRJExkzZox89913Ro+5ZcsWmTVrlpw6dcp4XAsXLpRly5YZ52tbZmamdOvWTbxerzRr1kxmzpxZbpP6yCOPSGxsrMTGxsrUqVMvyFhUVVasWCHXXHONxMfHS2RkpHTu3FnmzJkjeXl5lWaLi4ulQ4cO4nA45NFHH70g40PNUbc1t2rVKhk5cqRcfvnl4nA45Nprry13ubKdfHk/27ZtC1j2xIkTMnDgQImMjJQWLVpcsAODEydOyOTJk6Vt27bi9XolMTFRBg4cKK+99tp5s5s3b/aP//jx4xdkfKg6arnm7rnnHunWrZskJiZKZGSktG/fXmbNmiVnzpwJWrawsFCmTp0qjRs3loiICOnVq5ds2LAhaDlqGdVFLYfWvn37xOv1isPhkB07dgTMO/e1Pvvn6NGjAcseOHBAevXqJV6vV7p06SK7du26IOM9dOiQjBs3Tpo3by4ej0eSk5PlV7/6lWzZsuW82RdeeEEcDodER0dfkLEZ0zps6dKlKiI6e/ZsXbFihT799NN6xx13qMvl0latWml+fn61HzMjI0NFRA8cOGA8ro4dO2rfvn2Dpvt8Ps3Pz1efz2f82KG2fv16dTgc2q9fP12yZInefffd6nQ6ddy4cQHLbd68WePj43Xx4sW6YsUKTUlJ0VWrVoV0LCUlJXrTTTepiGhaWprOmzdPFy9erCNHjlSn06mdO3fWH374ocL8Y489plFRUSoimpGREdKxIXSo25rr27evRkdHa79+/TQhIaHccauqbtq0SUVEJ0yYoCtWrAj4OXbsWMCyd955p15zzTW6du1anT59uiYlJemZM2dCOu49e/ZokyZNNDw8XH/3u9/p008/rRkZGZqamqoiolOnTq0w6/P5NDU11V/j544ftY9arrk+ffrohAkT9Mknn9QlS5boXXfdpR6PR/v06RM0zptvvlndbrdOmjRJFy9erFdddZW63W794IMPApajllFd1HJopaen+9/fH3/8ccC8c1/rs3/OfZ0HDBigw4YN03Xr1unYsWO1ffv2IR/r5s2bNTY2VmNjY/WPf/yjPvPMMzpnzhxt3bq1OhwOXbhwYYXZnJwcbdy4sUZFRWlUVFTIx1YTVjSw5745pk6dqiJi1GBdyIKrizp06KBdu3bV4uJi/7Tp06erw+HQL7/80j8tIyND//CHP/j/P3/+fP39738f0rE8+OCDKiI6adKkoHmZmZnqdDp10KBB5WZ/+OEHjYuL09mzZ9PA1nHUbc0dOnTIv+OubNxlDezq1avP+5gdO3bUTz/91P//1NRU3bFjR0jGq6paVFSknTp10sjISN22bVvAvJKSEv31r3+tIqIvvvhiuflFixZpvXr1dOLEiRz01hHU8oXx6KOPqojo1q1b/dO2b98etG/Lz8/XVq1a6VVXXRWQp5ZRXdRy6LzxxhsaHh6uM2bMqLSBPXd6eaKiovTUqVP+/yckJOjx48dDNtasrCxt2LChNmjQQL/++uuAeXl5eZqWlqYulytgW3S2qVOnatu2bfU3v/lNnWtg6/QlxBVJS0sTkZ9O4Z/tnXfekbS0NImKipL4+HgZMmSIfPnll/75s2bNksmTJ4uISIsWLfyn9A8ePCgiIkuXLpX+/ftLcnKyeDwe6dChgyxatChgHc2bN5cvvvhC3nvvPX++7PK+iq7ZX716tXTv3l0iIiKkfv36MnLkyKBLNsaMGSPR0dHy3XffydChQyU6OlqSkpJk0qRJ4vP5ApY9cuSI7NmzR4qLiyt9nXbv3i27d++WsWPHitvt9k8fP368qKqsWbPGP61ly5aSmZkp27Ztk507d8oLL7wgl19+eaWPXx35+fmSkZEhbdq0kblz5wbNT09Pl9GjR8v69evlo48+Cpo/bdo0adu2rYwcOTJkY0Ltom6rVrciIikpKeJ0Vm/znJOTU+nfr7ds2VKefPJJ2bdvn6xevVq+/vprueyyy6q1jsq89NJL8vnnn8u0adOkV69eAfNcLpcsXrxY4uPjZebMmUHZrKwsmTFjhsyePVvi4+NDNiZcGNRy1Wu5PM2bNxcRCbj0cs2aNeJyuWTs2LH+aV6vV+644w7ZunWrHD582D+dWkaoUMvVq+Xi4mKZOHGiTJw4UVq1anXe5XNycoLWebaWLVtKRkaGHDhwQBYuXChOp1MSExOrNJaqWLx4sRw9elQyMjKCxhsRESHLly8XEZHZs2cHZb/66iuZN2+ePP744wE9RJ1xsTvoylT0KcaCBQtURHTRokX+aRs2bFC3261t2rTRRx55RO+77z6tX7++JiQk+D8d2rlzp95yyy0qIjpv3jz/Kf2yS2969OihY8aM0Xnz5ulf//pXvf7661VEdMGCBf71rF27Vps2bart2rXz59966y1V/b+zIZs2bQp6Dj169NB58+bptGnTNCIiQps3b64nT570Lzd69Gj1er3asWNHvf3223XRokU6fPhwFZGg0/ujR4+u0qdef//731VEdPv27UHzmjZtqsOGDfP/3+fz6dChQ1VEVES0X79+RpeUVOStt95SEdFZs2ZVuEzZ6zdjxoyA6du3b1en06lbtmzRAwcOcAa2jqNua1a356rKGdjo6GgVEXW5XHrttdeW+8nvZ599po0bN1YRUbfbHfD6hMKtt96qIqIHDx6scJmy1+DcT4LHjx+vHTt21JKSEp05cyZnbeoIajk0tVxcXKzHjh3T7777Tt98801t166dxsTE6IkTJ/zLDBgwoNzLBzdu3KgiopmZmf5p1DKqi1oOTS0/8sgjmpycrNnZ2RW+pmXTy/bL4eHhmp6erv/7v/8b9HjvvPOOxsXFqYhoZGSkvvzyy1UaR1X17t1bvV6vFhQUVLhM3759NSwsLOiYf9CgQTpw4EBV/el1qmtnYK1oYDdu3KjHjh3Tw4cP65o1azQpKUk9Ho8ePnzYv2xqaqomJycH7BB27typTqdTR40a5Z9W2SUPeXl5QdMGDhyoLVu2DJhW0QHluQVXVFSkycnJ2qlTp4A3xquvvqoion/5y1/808qKaPbs2QGPecUVV2j37t0DplW14Mqe66FDh4Lm9ejRQ3/2s58FTd+9e7fu3LlTS0tLK33s6po/f76KiK5du7bCZbKyslREAhrr0tJS7dmzp95yyy2qqjSwFqBua1a356qsgf3www91+PDh+uyzz+q6det07ty5Wq9ePfV6vfrJJ58ELZ+bm6vbt2/XI0eOVGsMVZGamqpxcXGVLvP4448HHYzv3LlTXS6Xvvnmm6qqHPTWIdRyaGp569at/g+HRUTbtm0bcGBe9pz69+8flP3iiy9URPSpp54KmE4tozqo5ZrX8pEjRzQmJkYXL16sqhV/KLBq1SodM2aMLl++XNeuXaszZszQyMhIrV+/frnH46dOndJt27YFvN6hEh8fr127dq10mQkTJqiI6K5du/zTXn31VXW73frFF1+oat1sYK24hHjAgAGSlJQkKSkpcuONN0pUVJRkZmZK06ZNReSnSwA+/fRTGTNmTMCp9y5dusjPf/5zWb9+fZXWExER4f93dna2HD9+XPr27Sv79++X7Ozsao97x44d8uOPP8r48ePF6/X6p99www3Srl27cu/kN27cuID/p6Wlyf79+wOmLVu2TFTVfxlSRfLz80VExOPxBM3zer3++Wdr3769dOnSRRwOR6WPXV05OTkiIhITE1PhMmXzypYV+em5fvbZZ/Lwww+HdDy48Khbs7qtjt69e8uaNWvk9ttvl8GDB8u0adNk27Zt4nA45N577w1aPjIyUnr27CkNGzYM2RjK5OTkVFrfIuXX+IQJE+S//uu/5Prrrw/5mBAa1HLNarlDhw6yYcMG+de//iVTpkyRqKiooLsQ5+fnV7ivLpt/NmoZJqhl81qeOnWqtGzZUu68885Kl7vppptk6dKlMmrUKBk6dKjcf//98uabb8qJEyfkgQceCFo+Li5OevXqFdJLh8uY1HJRUZHcc889Mm7cOOnQoUPIxxQqdfCi5mB/+9vfpE2bNpKdnS3PPfecvP/++wEb+m+++UZERNq2bRuUbd++vbz55puSm5srUVFRla7nww8/lJkzZ8rWrVuDvtIlOztb4uLiqjXuysbVrl072bx5c8A0r9crSUlJAdMSEhLk5MmT1VpvmbINSHnfL1VQUBCwgTGRlZUlRUVFAeur6DUqb2d3rrJ5ycnJIiJy+vRpuffee2Xy5MmSkpJSo7Gi9lG3ZnVbU61bt5YhQ4bIyy+/LD6fT1wul9Hj+Hw+OXbsWMC0xMRECQ8PL3f5mJiY835dxrk1vmrVKtmyZYt8/vnnRmNE7aCWa1bLsbGxMmDAABERGTJkiKxcuVKGDBkin3zyiXTt2lVEftp/VrSvLptvilpGGWrZrJa3bdsmK1askLfffrva96cQEbn66qulV69esnHjRqP1lykqKpKsrKyAaUlJSRXu52NiYio97hYJruV58+bJ8ePH5b777qvRWC80K87A9uzZUwYMGCDDhw+XzMxM6dSpk9x6663lfo+aqX379sl1110nx48fl8cff1xee+012bBhg9xzzz0iIlJaWhqydVXE9ECzIo0aNRKRnz5RO9eRI0ekcePGNXr8YcOGSaNGjfw/EydOrHDZsk9xKvuOq7J5LVu2FBGRRx99VIqKiuTXv/61HDx4UA4ePCjffvutiIicPHlSDh48GNBAo26hbi+elJQUKSoqktzcXOPHOHz4cEB9N2rUqNLvjOvQoYNkZ2fLoUOHKlzm3BqfPHmyjBgxQsLDw/01XnZjm8OHD8v3339vPH6EDrUcWsOGDRMRkX/+85/+aY0aNapwXy0iNdpfU8soQy2bmTJliqSlpUmLFi387++yD3mOHDlSaa2USUlJCWo+q2vLli1BtXz2Dd7O1aFDB9m7d2+5H46V2bVrl4SHh0uTJk0kOztb5syZI7/97W/l9OnT/ud65swZUVU5ePCg/PjjjzV6DqFixRnYs7lcLpk7d67069dPFixYINOmTfPffW/v3r1By+/Zs0fq16/v/7SooktjX3nlFSksLJTMzExp1qyZf/qmTZuClq3q5bVnj6t///4B8/bu3RvSuwaWJzU1VUR+uvSiZ8+e/unff/+9fPvttwF3OzTx2GOPBXyaVdkOtk+fPhIfHy8rV66U6dOnl7txef7550VE5P9v725j6yzLOIDfpz193VvWhDHQEeYwwSxjcyIkokZ0MWakYgLTDxoSPohiotEgJAoJiVMCaOJIjEID8pI4PhBTgShGXQiRmGh8wWgUhUyYJM5tWbu+nvb0nNsPyxpKK5xe3Urv8fsl+8D2/LnPOX2u5zn/Pu1z9uzZk1I6+cHLQ0NDaevWrfO2veOOO9Idd9yR/vSnP80+T1Yuc7u8Dh48mLq7u5f0weMbN25Mv/zlL+f83amrRQvp7+9P+/fvT4888ki67bbb5v37yMhIevzxx9POnTtn3/T++9//Tvv370/79++ft/3OnTvT9u3b03PPPRd+Dpx+ZnnppqamUrPZnPOjlDt27EhPP/10GhkZSWvXrp39+9/+9rez/x5lllmIWW7doUOH0ssvv5w2b948798+/vGPp3Xr1s25q/hCDh48OO+q8GJt37593iy/3q8R9Pf3p9/85jfpscceW/BTPF566aX061//Ol199dWpp6cn/fe//01jY2Pp7rvvTnffffe87Tdv3pyuvvrq9JOf/GRJz+N0KOIK7Gt96EMfSpdddlnat29fqtVq6bzzzks7duxIDz/88Jwd6K9//Wv6xS9+kXbv3j37d6cG77U72qlClXOe/bsTJ06kBx98cN76q1atesMdNaWULr300rRhw4Z07733zvnux1NPPZX+/ve/p6uuuqqVpztPq7f93rp1a7r44ovTwMDAnNt4/+AHP0iVSiVde+21ofVPec973pN27do1++f1fla+t7c33XLLLekf//hHuvXWW+f9+09/+tP00EMPpf7+/rRt27aU0snfpxkcHJzz57777kspnbxN+uDg4IIHE1Ymc7u0j95YyGt/LDCllP785z+nJ554In30ox8N/ajTKd3d3XPme9euXWn9+vX/d/trrrkmbd26Nd15553p97///Zx/azab6cYbb0xDQ0Nz5v+18z04OJg+9alPpZROfkPru9/9bvjxc+aY5dZmeXh4eMFt7r///tnHd8q1116bGo1GGhgYmP27qamp9OCDD6bLL798Sb9GY5b5f8xya7M8MDAwb//+4he/mFI6+dOCP/rRj2a3Xei8/LOf/Sz94Q9/SB/72MdCj/OU9evXz5vlV/9O8Gt97nOfSxs3bkw333zzvN//rdVq6frrr0+VSiXdcsstKaWTP0a80CxfeeWVqbu7Ow0ODi54f403xZt2+6gWvN6HAT/22GNzbv196rbfF198cf72t7+dv/GNb+Rzzjknr1+/Ph88eHA297vf/S6nlPLu3bvzI488kh999NE8NjaWn3/++dzZ2Zm3bduWv/e97+U777wzb9myJW/fvn3eHcq+8IUv5Eqlkvfu3ZsfffTRfODAgZzz69/2+/LLL8/79u3LX/va13Jvb++Ct/1e6A5fp+7i92qLuWvak08+mSuVSv7whz+cBwYG8pe+9KXc1taWP/vZz75h9nSbmZnJn/zkJ3NKKX/wgx/M99xzTx4YGMjXXXddbmtry9u2bXvDu7C5C/HKZ26XPrfPPPNM3rt3b967d2/esGFDvvDCC2f/+5lnnpnd7sorr8y7d+/O3/zmN/PAwED+8pe/nHt7e/O6devy3/72tzdc53R7/vnn8/nnn5+7urry5z//+Xz//ffn73znO3nnzp05pZS//vWvv+H/w51LVw6zvLRZHhwczJs2bcpf+cpX8ve///28b9++fM011+RKpZIvvfTSPDU1NWf7PXv25Gq1mm+++eZ833335fe97325Wq3OmfnlYpbPLmZ56eflVl/Tiy66KO/Zsyffdddd+d5778033HBDrlaredOmTfnw4cOLXmepnn322bx27dq8bt26fNNNN+UHHnggf+tb38rvfOc7c3t7+5yPUPp/VuJdiIstsI1GI2/ZsiVv2bIlz8zM5JxPfl7aFVdckXt6evLatWtzf3//gm/i9u7dm9/2trfltra2OTvuE088kS+55JLc3d2dL7zwwnzXXXflH/7wh/N27sOHD+errroqr1mzJqeUZm8BvtDA5Xzyltrvfve7c1dXV+7r68uf/vSn8yuvvDJnmzM5cIODg3nHjh25q6srv/3tb8+33XZbnp6ebil7ujWbzfzQQw/lK664Yvb1SynlXbt2zTuZL0SBXfnM7dLn9lR+oT+333777Hb33HNPvuyyy3JfX1+uVqv5vPPOy5/5zGfyCy+88IZrnClHjx7NN910U77oootyZ2fn7ON+4IEHWsp707tymOWlzfKLL76Yr7vuuvyOd7wj9/T0zH425e233z77eZmvNjk5mb/61a/mjRs35q6urvze9743//znP3/dNc4ks3z2MMvLV2BvvfXW2Y+i6ujoyBdccEG+8cYb35TyespLL72Ub7jhhnzBBRfkarU6O8u/+tWvWsqvxAJbyflV1/hhmdXr9dTf358OHDiQnnzyySX/eAWwsvzlL39JH/jAB9KmTZvSs88+u+i7TwIrg1mGs8OBAwfS7t270/vf//701FNP/d87kq9kRf4OLGePjo6O9OMf/zjt2LEj7dmzJ/3xj398sx8ScBpt27YtPf744+mFF15In/jEJ9w5HAplluHs8JGPfCQ9/PDD6emnn07XX399KvFapiuwAAAAFMEVWAAAAIqgwAIAAFAEBRYAAIAiKLAAAAAUodrqhu9617tCC0Rvzbxx48ZQrtlshnLd3d2h3LFjx0K50dHRRWfa29tDa/X29oZyk5OTy5obGxsL5er1eih39OjRUK70+55t3bo1lKtWWz5czHHOOeeEco1GI5Rra4t9X+7EiROh3OrVqxediX78xNDQUCj3n//8J5SL3mU0OpPDw8OhXPSYU/osb9u2LZSLzkhfX18oFxU9n4+MjIRyXV1di85s2LAhtFb0+Pavf/0rlJuYmAjljhw5EspFZzKq9FlOKaVLLrlkWddbs2ZNKNfR0RHKRfeJ6PvsiOh+VKvVQrnoMS76OEt539vKeq7AAgAAUAQFFgAAgCIosAAAABRBgQUAAKAICiwAAABFUGABAAAoggILAABAERRYAAAAiqDAAgAAUAQFFgAAgCIosAAAABRBgQUAAKAICiwAAABFqLa64czMTGiBrq6uUK7RaIRy9Xo9lGs2m6Hc1NRUKLdq1apFZyqVSmitkZGRUG56ejqUi+4r0a8BixOdkej+Nzk5GcpFjx3R/TZ6zGlvb190plpt+dD7puaix7darbasubeq6L7e1hb73vXw8HAo19vbG8pFjx3Rc15HR8eiM9HXcnx8PJSLvibR83J0PRYvOs8551Au+l4gej4p4fi+3O97o++roq9ldF9ZiVyBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKUG11w2azGVqgVquFclNTU6Fcb29vKDc0NBTKNRqNUG7z5s2LzvT09ITW+uc//xnKjY2NhXJtbbHvi+ScQ7mjR4+Gcm9V0X02OpOjo6Oh3MTERCgXPeZEj3EdHR2LzkSPU1HRr3l0JsfHx5d1vbeq6Nd1ZmZmWddb7v0hmoscOw4dOhRaK/paRk1PTy/reixevV5f1vUqlUooFz1XVqstV445+vr6QrnIe9jo+4eo6GsZfX90NnEFFgAAgCIosAAAABRBgQUAAKAICiwAAABFUGABAAAoggILAABAERRYAAAAiqDAAgAAUAQFFgAAgCIosAAAABRBgQUAAKAICiwAAABFUGABAAAoQvVML9BsNkO5w4cPh3KVSiWUazQaodzatWtDuXq9vuhMznnZ1lqK6GvJ8oh+faKzNT09HcpFjx3R5xfNHT9+fNGZ6GtSq9VCuehzi6633Mect6rlPta2t7eHch0dHaFcb29vKBc9dkRmObqvR8/n0a+BmVz5ovvtcuvs7Azlzj333FBuzZo1oVxkVoaGhkJrRecr+r4qevw4m7gCCwAAQBEUWAAAAIqgwAIAAFAEBRYAAIAiKLAAAAAUQYEFAACgCAosAAAARVBgAQAAKIICCwAAQBEUWAAAAIqgwAIAAFAEBRYAAIAiKLAAAAAUodrqhl1dXaEF2tvbQ7np6elQbmZmJpRrNBqh3MTERCjXbDaXba3JyclQLvq1Gx0dDeWOHTsWyrE4kX0vpZQ6OjpCudWrV4dy3d3doVx0ToaGhkK5yHyNj4+H1qpUKqFcW1vse5XRWc45h3IsTvR1jh4DVq1aFcqde+65oVz02HH8+PFQLnIOin4Nornh4eFQrlarhXKsfMt9HIiqVluuHHNEz+lTU1OLzkTf90Y7RHS95X6cK5ErsAAAABRBgQUAAKAICiwAAABFUGABAAAoggILAABAERRYAAAAiqDAAgAAUAQFFgAAgCIosAAAABRBgQUAAKAICiwAAABFUGABAAAoggILAABAEaqtbnj++eeHFlizZk0oV6lUQrmXX345lDt27FgoNz4+Hso999xzi850dnaG1oq+lvV6PZSLvpasbOvWrQvl+vr6Qrm2ttj316ampkK56P4ezUW0t7eHcmNjY6Fc9LVkeUTPCbVaLZRrNpuhXFR0v43OZM45lIuInpeX+2vA8lm1alUoF90nou9fo7kjR46EcqtXrw7lRkdHF52JnvOix+KRkZFQbnp6OpQ7m7gCCwAAQBEUWAAAAIqgwAIAAFAEBRYAAIAiKLAAAAAUQYEFAACgCAosAAAARVBgAQAAKIICCwAAQBEUWAAAAIqgwAIAAFAEBRYAAIAiKLAAAAAUodrqho1GI7TA6OhoKFettvzQ5jhx4kQoV6vVQrmccygXEf0adHZ2hnKvvPJKKMfZaXJyMpSbmJgI5ZrNZigXfZzR+YqIPrfo8W16ejqUY2VbtWpVKBc930VneXh4OJSLnrui683MzCw6E53l9vb2UC7yGCnD6tWrQ7l6vR7KRd+/Rs9Dx48fX9b1IsertrbYdb2xsbFQLnpMxRVYAAAACqHAAgAAUAQFFgAAgCIosAAAABRBgQUAAKAICiwAAABFUGABAAAoggILAABAERRYAAAAiqDAAgAAUAQFFgAAgCIosAAAABRBgQUAAKAI1VY3rNVqoQVyzqHc2NhYKDc5ORnKRR9ns9kM5arVll/6WW1tse83HDp0KJTj7NRoNEK56ExG9vWU4sec8fHxUG5qaiqUi8xlvV4PrTU9PR3KcXaKznKlUgnlovvtkSNHQrnoeXlmZiaUi8xydK0TJ06EctH3HKx80fmKam9vX9Zc9HgVPTdHjh+jo6OhtSYmJkI54lyBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARqq1u+OKLL8YWqLa8xBxtbbFuPTk5Gcq1t7eHch0dHaHc4cOHQzlYqpxzKDc6OhrKjYyMhHLRmYweO6LHqpmZmUVnJiYmQmvBq42NjYVy9Xr9ND+S1zc1NRXKRY9VjUYjlIvMZfS5wWs1m81QrqurK5QbGhoK5SLnvJTicxk1Pj6+6Ey0Q7D8XIEFAACgCAosAAAARVBgAQAAKIICCwAAQBEUWAAAAIqgwAIAAFAEBRYAAIAiKLAAAAAUQYEFAACgCAosAAAARVBgAQAAKIICCwAAQBEUWAAAAIpQPdML5JyXNVetxp5SpVIJ5Q4fPhzKwZtlZmYmlIvOZLPZDOWmp6dDuejzW+71YKkmJiZCuehMRnNR0ec3OTl5mh8JnHkjIyOhXKPRCOWmpqZCuXq9HsrVarVQLnqOjT4/yuAKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQhErOObe0YaVyph8LFKHFkVmxenp6QrmZmZllzcGZVvosr169OpSbnJwM5To6OkK5er0eyjWbzVCOt57SZzml+Lk5+v48+ppF57nRaIRyvPW0sm+6AgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFKGSc84tbVipnOnHAkVocWRWLLMMJ5llODuUPsspmWc4pZV5dgUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKUMk55zf7QQAAAMAbcQUWAACAIiiwAAAAFEGBBQAAoAgKLAAAAEVQYAEAACiCAgsAAEARFFgAAACKoMACAABQBAUWAACAIvwPmTurXctSPfQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "\n",
    "# Load the image\n",
    "# img_path = 'C:\\\\Users\\\\vsneh\\\\Downloads\\\\test.png'\n",
    "img_path = 'C:\\\\Users\\\\vsneh\\\\Udemy-notebooks\\\\OPS Ecoli\\\\ecoli Citrus\\\\2023-09-28_3_19_42_uv_Photo_Capture\\\\processed_2023-09-28_3_19_42_uv_Photo_Capture_1.png'\n",
    "img = cv2.imread(img_path)\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB for visualization in Matplotlib\n",
    "\n",
    "# Function to rotate image\n",
    "def rotate_image(image, angle):\n",
    "    image_center = tuple(np.array(image.shape[1::-1]) / 2)\n",
    "    rot_mat = cv2.getRotationMatrix2D(image_center, angle, 1.0)\n",
    "    result = cv2.warpAffine(image, rot_mat, image.shape[1::-1], flags=cv2.INTER_LINEAR)\n",
    "    return result\n",
    "\n",
    "# Function to extract quadrants from the image\n",
    "def extract_quadrants(image):\n",
    "    height, width = image.shape[:2]\n",
    "    center = (width // 2, height // 2)\n",
    "\n",
    "    quadrants = []\n",
    "    quadrants.append(image[:center[1], :center[0]])  # Top-Left\n",
    "    quadrants.append(image[:center[1], center[0]:])  # Top-Right\n",
    "    quadrants.append(image[center[1]:, :center[0]])  # Bottom-Left\n",
    "    quadrants.append(image[center[1]:, center[0]:])  # Bottom-Right\n",
    "    return quadrants\n",
    "\n",
    "# Prepare to plot\n",
    "fig, axs = plt.subplots(4, 4, figsize=(10, 10))\n",
    "\n",
    "# Process and display the quadrants for each rotation increment\n",
    "for i in range(4):  # For 0, 10, 20, 30 degrees\n",
    "    angle = i * 15\n",
    "    rotated_img = rotate_image(img, angle)\n",
    "    quadrants = extract_quadrants(rotated_img)\n",
    "    \n",
    "    for j, quadrant in enumerate(quadrants):\n",
    "        axs[j, i].imshow(quadrant)\n",
    "        axs[j, i].set_title(f\"Rotation: {angle}° - Q{j+1}\")\n",
    "        axs[j, i].axis('off')\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "927ed7ce-69d7-4839-8f77-27d728fdb74f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Process and Save Rotated Images\n",
    "def process_and_save_images(base_dir):\n",
    "    for class_name in os.listdir(base_dir):\n",
    "        class_dir = os.path.join(base_dir, class_name)\n",
    "        if not (\"Capture\" in class_name and os.path.isdir(os.path.join(base_dir, class_name))):\n",
    "            continue\n",
    "\n",
    "        rotated_dir = os.path.join(class_dir, \"rotated\")\n",
    "        os.makedirs(rotated_dir, exist_ok=True)\n",
    "        \n",
    "        for image_name in os.listdir(class_dir):\n",
    "            # Check for the 'processed' prefix and image file extension\n",
    "            if image_name.startswith(\"processed\") and image_name.lower().endswith(('.png', '.jpg', '.jpeg')):\n",
    "                img_path = os.path.join(class_dir, image_name)\n",
    "                img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
    "                quadrants = extract_quadrants(img)\n",
    "                \n",
    "                \n",
    "                for i, quadrant in enumerate(quadrants):\n",
    "                    for angle in [0, 10, 20, 30]:\n",
    "                        rotated_quadrant = rotate_image(quadrant, angle)\n",
    "                        # Name the file according to the rotation and quadrant\n",
    "                        new_image_name = f\"rotated/rotated_{angle}deg_Q{i+1}_{image_name.replace('processed_', '').replace('.png', '')}.png\"\n",
    "                        cv2.imwrite(os.path.join(rotated_dir, new_image_name), rotated_quadrant)\n",
    "                print(image_name)\n",
    "\n",
    "# Set the base directory\n",
    "base_dir = \"ecoli Citrus\"\n",
    "# Run the function\n",
    "process_and_save_images(base_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7edb28ef-24b9-4d0b-9b8e-b64940202474",
   "metadata": {},
   "source": [
    "### Rough - (Renaming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5eb09332-e43c-4765-829d-b1ef186c047a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_20deg_Q4_2023-09-28_4_44_31_uv_Photo_Capture_4.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q1_2023-09-28_4_44_31_uv_Photo_Capture_1.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q1_2023-09-28_4_44_31_uv_Photo_Capture_2.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q1_2023-09-28_4_44_31_uv_Photo_Capture_3.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q1_2023-09-28_4_44_31_uv_Photo_Capture_4.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q2_2023-09-28_4_44_31_uv_Photo_Capture_1.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q2_2023-09-28_4_44_31_uv_Photo_Capture_2.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q2_2023-09-28_4_44_31_uv_Photo_Capture_3.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q2_2023-09-28_4_44_31_uv_Photo_Capture_4.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q3_2023-09-28_4_44_31_uv_Photo_Capture_1.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q3_2023-09-28_4_44_31_uv_Photo_Capture_2.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q3_2023-09-28_4_44_31_uv_Photo_Capture_3.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q3_2023-09-28_4_44_31_uv_Photo_Capture_4.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q4_2023-09-28_4_44_31_uv_Photo_Capture_1.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q4_2023-09-28_4_44_31_uv_Photo_Capture_2.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q4_2023-09-28_4_44_31_uv_Photo_Capture_3.png\n",
      "ecoli Citrus\\2023-09-28_4_44_31_uv_Photo_Capture\\rotated\\rotated_30deg_Q4_2023-09-28_4_44_31_uv_Photo_Capture_4.png\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# Define the base directory where the class directories are located\n",
    "base_dir = \"ecoli Citrus\"\n",
    "\n",
    "# Function to rename files in a directory\n",
    "def rename_files(directory):\n",
    "    for filename in os.listdir(directory):\n",
    "        # Check for the 'Â°' character in filenames\n",
    "        if \"Â°\" in filename:\n",
    "            # Replace 'Â°' with 'deg' in the filename\n",
    "            new_filename = filename.replace(\"Â°\", \"deg\")\n",
    "            new_filename = new_filename[:-4]\n",
    "            # Define the complete filepath\n",
    "            old_file_path = os.path.join(directory, filename)\n",
    "            new_file_path = os.path.join(directory, new_filename)\n",
    "            print(new_file_path)\n",
    "            # Rename the file\n",
    "            os.rename(old_file_path, new_file_path)\n",
    "\n",
    "# Iterate over each class directory to access the 'rotated' subdirectory\n",
    "for class_name in os.listdir(base_dir):\n",
    "    # Skip if it's not a directory or doesn't contain 'Capture'\n",
    "    class_dir = os.path.join(base_dir, class_name)\n",
    "    if \"Capture\" in class_name and os.path.isdir(class_dir):\n",
    "        rotated_dir = os.path.join(class_dir, \"rotated\")\n",
    "        # Check if the 'rotated' directory exists\n",
    "        if os.path.isdir(rotated_dir):\n",
    "            # Rename the files in the 'rotated' directory\n",
    "            rename_files(rotated_dir)\n",
    "            \n",
    "# # Output to check if the renaming was successful\n",
    "# # List some of the files to confirm changes\n",
    "# for root, dirs, files in os.walk(base_dir):\n",
    "#     for file in files:\n",
    "#         if \"deg\" in file:\n",
    "#             print(os.path.join(root, file))\n",
    "#             break  # Just print one file for checking\n",
    "\n"
   ]
  }
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