Spineweb_regiongrow.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\manjw\\.conda\\envs\\dataEngineering\\Lib\\site-packages\\torchaudio\\backend\\utils.py:74: UserWarning: No audio backend is available.\n",
      "  warnings.warn(\"No audio backend is available.\")\n",
      "c:\\Users\\manjw\\.conda\\envs\\dataEngineering\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "C:\\Users\\manjw\\AppData\\Local\\Temp\\ipykernel_19872\\3418379195.py:16: DeprecationWarning: the imp module is deprecated in favour of importlib and slated for removal in Python 3.12; see the module's documentation for alternative uses\n",
      "  import imp\n"
     ]
    }
   ],
   "source": [
    "import nibabel as nib\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.transforms import ToTensor\n",
    "import torchmetrics.functional as evafunc\n",
    "import torchvision.transforms as transforms\n",
    "import numpy as np\n",
    "import torchvision.models as models\n",
    "from segmentation_models_pytorch.losses import DiceLoss, FocalLoss\n",
    "import segmentation_models_pytorch as smp\n",
    "import os\n",
    "import imp\n",
    "import CTimageLIB\n",
    "imp.reload(CTimageLIB)\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自定义数据集类\n",
    "class SpineWeb15(Dataset):\n",
    "    def __init__(self, data_path, transform=None):\n",
    "        self.data_path = data_path\n",
    "        self.transform = transform\n",
    "        \n",
    "    def __len__(self):\n",
    "        file_list = os.listdir(self.data_path)  # 列出文件夹中的所有文件和文件夹\n",
    "        file_count = len(file_list)  # 获取文件数量\n",
    "        return file_count\n",
    "    \n",
    "    def __getitem__(self, index):\n",
    "        metadata = np.load(self.data_path+str(index)+\".npy\")\n",
    "        image = metadata[0]\n",
    "        label = metadata[1]\n",
    "\n",
    "        image_data = torch.from_numpy(image).float()\n",
    "        label_data = torch.from_numpy(label).float()\n",
    "        \n",
    "        if self.transform:\n",
    "            image_data = self.transform(image_data)\n",
    "            label_data = self.transform(label_data)\n",
    "        return image_data, label_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_pixel_accuracy(prediction, target):\n",
    "    # 将预测结果和目标标签进行逐像素比较，并计算正确预测像素的比例\n",
    "    correct_pixels = (prediction == target).sum()\n",
    "    total_pixels = target.size\n",
    "    return correct_pixels, total_pixels\n",
    "\n",
    "def calculate_iou(pred, target):\n",
    "    intersection = np.logical_and(pred, target)\n",
    "    union = np.logical_or(pred, target)\n",
    "    iou_score = np.sum(intersection) / np.sum(union)\n",
    "    return iou_score\n",
    "\n",
    "def calculate_dice(pred, target):\n",
    "    intersection = np.logical_and(pred, target)\n",
    "    dice_score = 2 * np.sum(intersection) / (np.sum(pred) + np.sum(target))\n",
    "    return dice_score\n",
    "\n",
    "def visualize_results(image,label,output):\n",
    "    fig, axes = plt.subplots(3, 1)\n",
    "    axes[0].imshow(image, cmap='gray')\n",
    "    axes[0].set_title('image')\n",
    "    axes[1].imshow(label, cmap='gray')\n",
    "    axes[1].set_title('label')\n",
    "    axes[2].imshow(output, cmap='gray')\n",
    "    axes[2].set_title('output')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the dataset: 754\n",
      "----------------  Loading the dataset and evaluate  ----------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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X3traKt1ooKORTdBKm3jGNg+E0nG4SqAHG490AKQk0Kph6hcnlBZ0LB4d57ahdsAi0D4oMjU1Vaa2qA9kQ5HxTjEqIYTMaWqvncfHuJfbFSQ1segmm81m+QHaiUXJUwBxH6zW4KYPt6GobTy1CMQfUcn3JWnEpScfChONRmOS5vGOIYSQEog/XCI0lyKUbQDa0zfktFB/abwVSWS6R2azWQZN+egK7vR0N1eY1F4hEYRuFk0AaG1tlWKce03AmdWgVnJo40LctddKP2qvjaRT0JS8Mm06hvodCARiUjncxqK8JyWTKXAZDAZjPFBKNPt8vpihNPTS8WHZRCyK/dlsNmlC8FAIXTcfa98VJDWxgNjhyPQQKZUDnDagtQFPrUqMRzRqy0FvLj0krZeoPSYfkWkwGBAMBmX+jatYTqp4faTr4G356FEucflSLXykRCAQgN/vl/txp4D6FAgEOgwAPJO67gxJTywAMRMQyGgFYtUI0FGtAaelDJdC2ngXBw9KAqelGM9DckOdHjRt50FJTkQaWkz95H0gYnK1RMfm6Sg6F0/98IQyjQkjaF8im80mzQhySPi1dscrTGpi0c23WCwyzUIqgh5KOByWwVIteBxKm8Dlv9O+NFCOzsnJoE3xxLO1KJDLpQ1w2vPkY7b4S8DHq3eWD+RBXN5n7nmSjUQ2X7wZPMDpfCL1gSSl1qs9E5KaWABkLpDA30qSWD6fL+ZB8gentWfomASugvhQXZ4WIWlBx+AqkksZfh4uBbR12eNJC5oAS4TgIyPIK6Z9hTg9KYPbmnQek8kEv98vA6sU+KV7QlKetvN70eXn0uWWfRRELB4cJc8GaJc6jY2NMWPEaTt/wNxT1KoIiljTw6EHxO0aHtqgt51vt1qtSE9Pl0N4tKM0eYZAa8DzuJXFYoHD4UBKSoocGctDC9qHzyUqeaF8tAcN3yYS0jXxWKD25enSc+lyyz4IblfQjSXviT+kxsZGtLa2SgmjDZZycLuG/idSca+OJ4spx0fxHuC0FCEVZzabkZaWhpycHOnaE3gejpORByy1KSCr1QqHwwGn0xljz/GEOBE4XgCYhxLonlHsj9rz0EN3pBWQ5MQCTqsNbh9wgzYajaK5uRk+n6/TtEQ8dcj/5w9Xm3rhnhxt5xMTSJ2Ew2E0NzfDaDRKF59fAzfouY1E0OYauUTW2oX0Emhzipx4JP3sdrsMUZD9yIlN5OYStStIemKR7UE3kqcqAMg30ufzxTwcHsGm//k22k6gh0H/c9UXzxWPRtsnc7S2tiIQCKClpQVerxdtbW1ITU2Vdhodj6eMtKMeeFyJ22Z8fzKuiTg8aa29Jp4i4i8k/U7hG22u86KJY/F0Dr3xNIECiHXT6+vrY4xhngfTSgI6FofWLuLqSZtiIXuF1BB5X0S03NxcOBwOuV1r7/E4kjYoq1Xh3MinsEI86UttaV+SjtTebrfHzLDmhOusNMGZkNQSi6saUgd8IgE9tHA4jKamJrS1tUl7hxvG/KHFG7VAUW0eSqC/8W449YGrXS6RUlJSkJaWJn/j59SeP170n4cZ4gVlaZQq7U/k5yYChS4oYEs5RHJMKExB5+ADCLuCHifWE/9/mVn+GTFihPw9EAjggQceQGZmJlwuF2bPnt2hnFF3QCNH6UFTDIt7eSSxWltbY+wkoOPwl3gqkaSV1nvj+9N27jQAp4cCk/dKQdy0tDTpYfJ0iVYF8uPFSz9x24zUFmUftMSjY/B4GIUt+BxGTmJtqKSr0EUVXnLJJfj8889Pn4S5+T/5yU/w5z//Ge+//z48Hg8efPBB/PCHP8TGjRu7fR6SJFyFcFuBo6WlBc3Nzejfv3+MFIhnEGvB83HxJApXqfR2c6mVkZGBzMxM6cW1tLQgEAggJSUF9fX1MVKQjsGj3wSeVObql0tDsjEDgYA0zvmLQBVoeAaBBv/Ri8ljd9rQR1ehC7HMZnPcAmpNTU343e9+hxUrVmDq1KkAgDfeeAMjR47E5s2b5aqjXYXWxoqnCrlLXVNTg6KiIrl/PO+LQ6sCuVTRRuf5X+obkYxIRUQJBoNobW2VapwMZrKtaAY3FYzjKomrYLrXPINAnh21od9o6DaRiF83jTBtbW2V94xeUO4QdAe62FhlZWXIy8vD4MGDMW/ePBw9ehQAsG3bNrS1taG0tFS2HTFiBAoLC89Y1DYYDMLr9cZ8CHy4DBmv9CB47CUcDuP48eOySIhW/QCxEXkukXiSl9tU9HAp/sONbh7HamhoQGNjI4LBYMzoTYvFgpSUFJmyoRfEZrPJnB8Rz+l0wu12IyMjAxkZGfB4PHJKGJGND3+h+BkFOalv8bxJuhZu/PPYFb2w2sDxmdDjEqukpATLly/H8OHDcfLkSSxevBiTJk3C999/j6qqKlit1hjDFWgvaltVVdXpMTurQQrEDjumgWta15tQV1eH5uZmZGZmxn0DuTelDSrSX61BzsGDp2RAC9FeVTAQCCAajSIjI0PaVgaDAdnZ2TKISw+aV6xJS0tDamqqrFBI10REaWtrQ2NjY8z+NL2LOyRkV5Enyq+Tou+kRjmxSAp2dzngHicWXxj7sssuQ0lJCYqKivDee+/FLLrdHXRWg5TeSnqg9FZpjW+gnRTNzc04deoUMjMz407A1Bqv9L82Sg7ETn7gD5F7X8Dp0Z/RaBT19fUwmUzIzc2FyWRCS0sLrFYrLrvsMpSVlaG2tlaqILvdjgEDBiAzMxPNzc1Sqmmnz6empsLtdiMlJQW1tbUyx8ftqlAoFEM2bSSfyEZ2FScwR0IllhZpaWkYNmwYDh48iOuvvx6hUAiNjY0xUquzorYEUg1aUF6Oi3PgdJEybYAzFArh6NGjKC4ulp5evHSFNvQAnI4t0YOhB8jnMsaLjRGBiVwWiwWDBw9Gc3MzDh48KBfHzMvLw549e1BbWwuz2Yy8vDzYbDbU1NTICRGU7yRVGg6H4ff7YbPZkJOTA7PZjNraWjkuje6RyWSSQ4mof3xuYryQBh8cqL2/XYHucazm5mYcOnQIubm5GDt2LCwWS0xR2/379+Po0aNxi9qeDTz3x9UXt4O0UfWamho5wpJLO/52cmOY5//IDqGHxKUCSRHuRdFD1Rrf+fn5yMnJkYFHg8GAfv36Yfjw4ZgwYQKuvvpq9OvXT448oIF7fHgLtyNJIqWlpcHj8cSMx9JKOm4zArEjI2ib1WqNSW7H0wBnQ49LrIcffhgzZ85EUVERTpw4gUWLFsFkMmHu3LnweDy45557sGDBAmRkZMDtduOhhx7CxIkTu+0REri7z70mbojz701NTTHqkN5I7Tgnnhoh+4MeSrwpYhw8RsbbRCIReL1eRCIR9O/fHyNGjJB92rt3LywWC4qKimC329HY2Civj0ow8T7yGBMFfp1OJ1JTU6WDQBWQjUYjXC6XnBFNdh/ZpGazWc7D5Cky8h7J0+yOZ9jjxDp27Bjmzp2Luro69O/fH9dccw02b96M/v37AwBeeOEFGI1GzJ49G8FgENOnT8crr7xyTufiM2iA2JvNJRa/KaFQCDU1NTE2RGcJXiIpPVAKdGpLInG7RRtnoocnRPtsmOHDh8Pj8QBoX7n+2LFj+Prrr9HY2Cgn2xYUFMhRr1pvk1I3PLPAa6vSqIdAIAC73S6rxbhcrhgD3Ol0SnXp9XolwWi0K49t0XV0Bz1OrHfeeeeMv9vtdixbtgzLli3rkfPxWc5UGYWnabQinDwwHiOKB22ODogtOMvVLoGfk0/XIjWWnZ2NMWPGSKly8uRJbN26FbW1tQDaiVteXi7Hc9H18HgczyvycxAo5EBko8oypE75PaHK0kRUqq5Mw4T4PdZG/s+GpE5Ca/Np9IZp83w8XkXB0nA4LB8AL0Km9Qj5cfhwZG1pH1LJ2vHidEyHwwGHw4GTJ0/CZDKhsrISmzdvRmNjY0yIpKmpCcePH0e/fv06DFgkt19reJPKDofDcDqdMr4ViURkBT+6hngvHJ2fpCGAmHH6dK7uIKmJBcSvasdDDlrCGAynM/lau4V+J8nAx4vz88UbQaANLNLDtFgscLlcstpgRUUFKioqUFVVhcbGxhi7EECMwe33+yWRtTlOLq3IwA6FQnJkqcVikcTkmQHt/aDjcgmYkpICu90On88nbTI+SaUrSGpi8TeQ/68NNfD/LRYLsrOz5QPjxOJvMpc6/Bh8SIn2YfHzkvTIzc3FgAED4PV6UV1dLSPwVLqRp4f4dWm9WoPB0CFIShKT23eRSCRmRC0RlcfatAFSXhbSZrMhJSVFlr2ke0QSs6tIamKR8U43hds3WmOcHpbL5UJeXp60YehmcTuC5wb5Q+Mqg6QijwsRGXhpxsbGRpjNZgQCARnZ5mSlc/AEM48zBQIB2TfeL/JoeV6Qx8rsdrscZEgjPrSrpdEARMoSpKSkyOEz1EfyFrlD1BUkNbFIjAOnPTOuLrS2hNFoRGZmJjIyMjoERvmoBW2YghvqdA7tZFV+DlJDAFBTU4P6+noZyLXb7cjIyJAhDC4JgdMEp75pvV5yBHiYhKtluifkmJBTQ6tV0PEp5EDzCKkaDalhAHI/7Xm6gqQmFofWrtK+XfRA8vPz5QQE/hZq7Q+tjcYlEn0naO0wLmGCwSD8fr+skZqZmYnRo0fj4MGDOHjwYIz9RP/TmHhOKlJpXFUTAXk/iQDa64pGo2hoaOig6rnT4ff7pfozGo0x0ooHaLuCpCcWvaVnE9WkBinHSEU24rXjHhJJLwocArGD+wjc9iLXnaZVkcq12+0YP348Ro4ciaysLDQ0NMiYGtAeKkhPT0d2drYclcBtLW1aiQd+gdOTc7VDfMg+44sxcclOhPT5fDHn04YcLprCa0Ds+HPtzaK/9Onfvz88Hk/M7BOg4+oU2jACfaf4Dp8mRcfmapHbX3Q8ki4UqMzPz8fEiRORkZEhhwTn5eWhuLhYplS4hNCui6ONK1H+ktde0HqRNEmCrjkajcaEWqLR0/MOqbY7DVvubknupJdYWmNaC7ppNpsNAwcOlNHteJ4YEZSrPR5ppxnInDDcI+XRcQKRxmg0YuDAgUhPT0dLSwvq6+uRkZGB0tJSnDhxAhaLBWlpaVJy8EkWnFRc/ZH9w8fw0zWTF0dSipL4/KXSLufLHQf+ssS7rrMhqYnFjVoimDYASA89NTUV2dnZHdzteHEdbqRzieN0OmNSLVziEbThCBqRUFxcjJKSEgDA3r17cfjwYYRCIRQXF6OoqAhOpxM+nw91dXWyRj13MEhicpuLn4MP4iP7iC9Rp5XIpOZdLpeUwtrr1g6UvGgi71pwD46+002iyRt8oJs2qMpjYcBp4vIHx2evcGLzcAcPF5DdlJWVhcbGRhw+fBgnT56U0iIQCMjkcTTaPrm2paUlZmCeNgPA7S3qG10P2Vc0h5GkD79WOg7VxifDnq9zyO0+uvbuDPZLamJpJQW3KYDYxQVyc3PhdDo7ffM621cbuyJo82da+44i4g6HA2lpaTAajTh+/Diqq6tlnjIjIwOpqamoq6tDWVkZAMgF0Wtra6X9w6UWcDqxzb1Tbks2NjbKCjtcqnJpzGtZkGPCU2L8peHVZ7qKpCYW0HHwWTw7wOl0IisrS0690kbqOcim4clt7fFpP26DaIOz9IDcbjeys7MlWeh4JCm++OILGUsyGo3IyclBfn4+XC6XVH2cEFqng0tRoH34tdfrlcTRmgZ0DXycltPphNPplOEFOjafpEsxra4iqYmltYu4J8TfrvT0dLjd7ph1boDYCDjQcZIqd+lpmzagyffTGsEA5LrULpdLzsQJBoMIBoM4duwYqqurYyTGsWPH0NLSIgfsUaAzHA7LyD0RivcpEAhI9adVzTxpTcFVckaoLc9/EiGpHRG6O4nopCYWf+g8LMAJYjQakZWVBZfL1cHw7eyYXOrxmSs80s/BJRilWuhN9/v92LlzJ0aMGIHU1FSZovF6vfD5fHIUAXmcAGKqvzgcDjmZgr84FBsLh8Pw+XwxEyGA2IXK+ShQnhvkQVUhTq9QS0Sk6tPaSbhdQVITiwcKuQFLiEajMoVC48C1I0VpH619xI/Bp6zzkQu8H9QmXqpHCCElE9ldtbW1Moiq9caINEajEc3NzWhubgYQuwgBD0FQLpAkjzY8wIvg8gwFqTrKuWozCFzCU8ilq0h6YnGi8CEg9N1ut8Pj8cQkaXmYAYg1vLn9wuNIvGqgVmpxb4x+5yEHPlOH8nHc1uMVjmk/PrqCvFA+vY36RvlGnpCnPtC+1JbuAdVroMF/2vtAapKIz0dLdBVJTSwg1q6JF8ei6VHUVhuZJ3BJwx8yty84+bTBQ94f4LTd5/f7ZQwMaA9UHj9+vIOkBU5XP+a2DR2Tk4YkI/WDT5nn5SxJivJ9SX2S7cbtSGrPz0P946MzuoKkTunQzeeJVf4GmkwmuN1uOTtYGw+iY9BfrR1FYYZ4UXoCf/haSUVBR6fTCZfLBaB91hLPA/KHCMSuNqFVyTzxTRKEbC8y7klFUslMeinIq6PAKZfq/IXkcTP6n0vdriKpJRZXVzyCzlMuGRkZci6e1u7h+1B7PlyF/tKbrR0cqJ0ASiDpZja3L7dLsSm/3w+v1xsTQuDpI4LW1qFtBOoz9ZdygBSeoInB4XBYLqagHfbC+0D3QyvFKVEfzxM+G5KaWCT6ucQCTrvLlH/T3kCgYzon3sOkICe1J3BXn885pH241HM4HMjIyEBbWxsaGho6FOSgPmhrasWLz1F8jQc3iTTazAHZVJSCoj7xCbZ8O714fEw/kYr6RYs5dQVJTyyuOvgYdSHaK7ikpKTEqByuKjlB6Hj82JTGoH242tMSkRvJ3P6iWdxUzY+8PX5+kiacVFrPkgdtqc/08ElNkT3l9/tjbDWelqFrMRqNsi98kgjdU5JWNLrholOFPL3BbSSj0SjrUWmlBO3L/9eSDTg99FnbPl70XWtYUxtKKHPbip+DG/v0XWvnEYmA0zYYPWhODhrB4Pf7ZYiBwgT85SD7jgx/akuk4lqAwh/8Je4KkppYAGJuAq9lZTAY5HDbeDEuAldd3F7ieTjaziUG0HGlr3iqsbW1FYcOHergjcazV/i5uRrijgYRl9QcteGBTVrckgjOa2ZxFUiguBknnrZf3bGvgAvAK6S3iYcI6ON0Ojvky/iD1Ubpz5bqOdMbq92Hb+cjL7kk4ukWrmq1diD3IMmmomn02rQWmQTc0SAppE05cfLya+MzerTxta4i6SUWxXCA0wtz082lUY9aF14bHOXgKok/CD6agXuPXLXFIx6laahv2sg/EYPsGp6aIQeEVCGPWXEvjwx6PgKB7gG3zXjQlPrOPUwiEu3L7ytt6yq6LbE2bNiAmTNnyilUq1ativldCIHHH39clpwuLS2VQ0II9fX1mDdvHtxuN9LS0nDPPffItEV3oVWFZCtQ1J2X0OaIJyGoHX+bufrQSjjaBsQ6Ato33GKxxMTLCCRl2tra0NLSIqvG0JQtqk9PY7S4ncbVI01u5ePMaGgx5S95RB+ILctE+/EXUGsKAIhbSqozdJtYLS0tGDNmTKe1F5YuXYoXX3wRr732GrZs2YKUlBRMnz49Jmo7b9487N69G5999hk++OADbNiwAfPnz+9uVzrYBHQT6cbyNAy1594XcOaENNky2v21UifejB0u3bRSkx6q0dg+qYNmxwCnx3mRZOUerTZ9pe0rL+LRWYiF2pLE4/3jfadr5bXftS/VmdBtVThjxoyYqn0cQgj86le/ws9//nPMmjULAPCf//mfyM7OxqpVq3DHHXdg7969+Pjjj/H1119j3LhxAICXXnoJf/VXf4V///d/R15eXpf7IoSQox7JNabx5Tzuoo2O077x/idw9aMdAw7EFjDjRj9XNdyG0g4P5m495eVI4mrJRMfgHi/tT+fii0cJIWJmIfE+crVH0p2PmafzUTtOsu4Y8D1qvB85cgRVVVUxxWs9Hg9KSkpk8dpNmzYhLS1NkgoASktLYTQasWXLlrjH7ay4LTfeAcjEKpGBbA6tXcWlAX+IPDZF2/n8w3g3Nt523h8uJYmIZGvRwDnyXqlwCFUx5iqMS0atFDWZTDFlt/l1kG3HyWS322V9Br50HNl0vKguj/+dSbpr0aPEogK12dnZMdt58dqqqipkZWXF/G42m5GRkdFpgdslS5bA4/HIT0FBAYDT9gVfzJtuJJc02pvDA5n0VxuQJGjzZ/GSznx/bTutpANOVyEWQsjaVS6XC06nUwZ1+Yob/Fza8wKQIRWuukkCaklGdic/Pt0nejFpOA0nUjz78kxIinDDwoUL0dTUJD+VlZUATqczKFhIQVF60Nxt5nEcfpO4miTwNAaRgIcMOCG5PQV0LCZC4J4ZnYPm61FZRi5VOvNatX3g10dt6X6QaqXy43y5Fz46ggKpRDaaW0gFQmh9RK3Neib0KLGoQK12CRNevDYnJwfV1dUxv4fDYdTX13da4NZms8Htdsd8gNO2CgUKyRMEECP+tbEqbSxLCy7ZqH803IT2i7e/lmTxQMfihj3vI7e7tCkdShGlpKR0uJ54uU7qI5GHvxAGg0HmEfm9og+AGJuvu+hRYg0aNAg5OTkxxWu9Xi+2bNkii9dOnDgRjY2N2LZtm2yzdu1aRKNROe+uO4hGo3IlByFEzMLj3GjV2iUcnZGLn4OIxT0tbXqGSyMAMQ+JwFUrN8Ip+k0VYrQxMboekix83FQ875U7CiR16TyhUAgtLS3SG+UhCS4F+aoXXM13Bd32CqmMNOHIkSP49ttvkZGRgcLCQvyf//N/8NRTT2Ho0KEYNGgQHnvsMeTl5eHWW28FAIwcORI33ngj/uEf/gGvvfYa2tra8OCDD+KOO+7olkcInJZY9CAMBoMkFhAb5KT28Y5Bqk/7l6QA7U+SkYrNdmaXac9DbYmMNN+vtbU1poRjJBKJmTBBnil5btFoVFZS5tfPSajNNfKgKF0Hl7jUF64iaTufR9iZ89IZuk2sb775BlOmTJHfqbD/nXfeieXLl+ORRx5BS0sL5s+fj8bGRlxzzTX4+OOPY/TzW2+9hQcffBDTpk2D0dhe6PbFF1/sblfkjSKbQgghvR4+6E27T2cezpkkF0ka2kbDT7QhC7LHeP154LTa5GPcucdJbXgCm0jNJZ828q51TuJdL3cuSCX6/X6Ul5cjHA5jyJAhciAiSVNSx/QSdTfc0G1iXXfddWcUiQaDAU8++SSefPLJTttkZGRgxYoV3T11B9DN9vv9Mm5jtVphtVrlKu3csKXv1M8zgaSedqaxdkgJqRA+2oAkANlR1Fft3DyuWqmfNA1MGzMjaRwMBmX5bT6uiksqvi9PzfAXoaamBt9++y3C4TBSUlJkpoTITaqWYmzawPLZkNS5QpISfEUrSmUAp99urhripVbo5vOoN0/p8Bwfj7xzL4xIog2QApDeZbxCaxSIjBdo5ecAEFPggzQA9YXsJG5vkaTi9hzZWJQGam1txffffw8AKCgokGEKIhW9WDze1RUkNbEIRCx6u/i6Mtr4izbKTr9rE7TUNp6E494oHwiolSA8T8nLUmr7wNM/HPzcJEkoeBoOh2Oq9FFgk0tIUmH0wlBQlghOs7FbWlrkaAmSivX19dIrTE1N7dC3syGpiUU3nh4aFTcje4E/SC49tHZWvLFPBK2HxUMMJL20HhW97SRVyOjm6RFtv7i6pn4S0flITkoEExEMBoM0+Gn1CTonXRt5hbTM3NGjR2U9+WHDhsHj8cDtdst74/P54PV640rGriKpiZWZmYm6ujoZfQ8Gg3JWjslkionIA7Glf7SqkR4qPSzaxttrPS1uw/EYkDY0wQvyc4OcS0n6TnYQr1BM6p3UERnYJGGIuFSwzWQyyaVOtJMhAoEA6urqUFNTA4vFIsfkO51OOYaLTAo+1CcQCOg7bKYvYdSoUR2mqJOIp1kr3MY6kzSKZ3tp41/aNtxT0g4xiUaj0obRTn/noP3oQZLzQf/zPCJwegk4vuahzWZDc3MzKioqpJqjBD19qJ9WqxWZmZnIy8tDQUGBTCM5nU5JakoR8dQYX9ipK0hqiVVQUIB9+/ahoaFB2lk0c5cmMESj0Q5xH04yrU0VT0pxNamVdvQA6Rza9WdIFcULlFKfaEBeU1MT+vXrJ9MqJMFoXz4SQhvO6N+/P1paWmIi6/wloRCIy+XC4MGD0a9fPxnzIyLTdVK+kqQupZ26M2YuqYlFeTCKvpN6IBHf2Ngol+o1mUxyXDeBG+f8IWjVI5dU8X6j81KYgQ8D5t4e0HFlC5IIvAQlH0ZDbcgjJPVEMTsiOy3rq70e7tHSOWw2G9LT02XIgmxC7j1TTXgiGveGu/RsutyyD4JGV5LtAEAWNaPCG4FAAG63u4OKAzqqQq104tvjBSK5VCMbiiLppH7OFDfjfXK73UhNTZUPkn4nO4lQV1cHl8sljW3qM0lMChFoQx8ApJdMwVWSphSioLAHeYPUb1LrF03V5MOHD0vxTDUSKABJ3hHZHPxt1KY8ziaRSEJoA8Pa5DFV4KPRBAStZ6lVw7wvPIhJf4ksgUAADQ0Ncs1D3geShLSCF/U3Xo6UQiWBQCBmqDOdh0spatudqDuQ5BLr0KFD8i3SlsomN7mpqQn5+fkdpA4nDIETkLv99FtnqpNLOq7GtA+Ve4H8wZPE4KEFIDanR7ZOfn5+BylG/aFjdSWvx0mm3c6XB+YR9+7EspJaYtECjvRWkRcYjUblgLaGhgYA6FCYNd6N1YYgOHi+7kyJbXqw5N2RiuHFz7TSLxKJyBQUH4kQDxRK4OtEc3VNzgP3aLX95i+X1obkbfhxuzsFLKmJxS+eDHgS5xTc8/v90lvi4NKLvvP4FFdZ2pwd34eDkrekjrlK5GpPG3mnUAlViNHacnxGNhnyVBZSayPy/molMrXThlD4S6JNf2klbFeRlKowngdHN9vj8aC1tVV6T01NTairq5P5QLIhtDeYqzgeZtCGBXjgUhu+0Eb0+Tl5WoVPWOAPjTwzCiXwelm8f7QWEKl98hgByGlgWpUPQF47J7U2l0oeKpkWvC29MF0hmEF0h4Z9BIcPH0ZxcXGiu3HRorKyEvn5+Wdsk5QSKyMjAwBw9OhRuXC3Qkd4vV4UFBSgsrJSDuc+H1AesSsDMpOSWKQWKHmqcGbweQLni66+yEltvCv0XShiKeiCpCSWzWbDokWLulWk4mJEIu9TUnqFCn0fSSmxFPo+FLEUdIEiloIuUMRS0AVJSaxly5Zh4MCBsNvtKCkpwdatWxPdpV5DXyvV2RmSjljvvvsuFixYgEWLFmH79u0YM2YMpk+f3qGCzYWKvlSq84wQSYbx48eLBx54QH6PRCIiLy9PLFmyJIG9SgwAiJUrV8rv0WhU5OTkiGeffVZua2xsFDabTbz99ttCCCH27NkjAIivv/5atvnoo4+EwWAQx48f77G+JZXECoVC2LZtW0wpSqPRiNLSUlmK8mKGXqU6zwVJRaza2lpEIpEzlqK8mKFXqc5zQVIRSyF5kFTE6tevH0wm0xlLUV7M0KtU57kgqYhltVoxduzYmFKU0WgUa9askaUoL2YkolRnp+gxN6CX8M477wibzSaWL18u9uzZI+bPny/S0tJEVVVVorvWK/D5fGLHjh1ix44dAoB4/vnnxY4dO0RFRYUQQoinn35apKWlidWrV4tdu3aJWbNmiUGDBgm/3y+PceONN4orrrhCbNmyRXz55Zdi6NChYu7cuT3az6QjlhBCvPTSS6KwsFBYrVYxfvx4sXnz5kR3qdewbt06AaDD58477xRCtIccHnvsMZGdnS1sNpuYNm2a2L9/f8wx6urqxNy5c4XL5RJut1vcfffdwufz9Wg/1bAZBV2QVDaWQvJAEUtBFyhiKegCRSwFXaCIpaALFLEUdIEiloIuUMRS0AWKWAq6QBFLQRcoYinoAkUsBV2giKWgCxSxFHSBIpaCLlDEUtAFilgKukARqxtYvnw5DAYDysvLu7Xfddddh9GjR/doXwYOHIi77rqrR4/Zk1DEUtAFilgKukARS0EXKGKdB1avXo2bbroJeXl5sNlsKC4uxr/92791unLXtm3bcPXVV8PhcGDQoEF47bXXOrQJBoNYtGgRhgwZApvNhoKCAjzyyCNyHZtkQVKuTNFXsHz5crhcLixYsAAulwtr167F448/Dq/Xi2effTambUNDA/7qr/4Kt99+O+bOnYv33nsP999/P6xWK/7+7/8eQPus7ltuuQVffvkl5s+fj5EjR+K7777DCy+8gAMHDnQostan0aOzFC9wvPHGGwKAOHLkiBBCiNbW1g5tfvzjHwun0ykCgYDcdu211woA4rnnnpPbgsGguPzyy0VWVpYIhUJCCCH+8Ic/CKPRKL744ouYY7722msCgNi4caPcVlRUJCep9kUoVXgecDgc8n+fz4fa2lpMmjQJra2t2LdvX0xbs9mMH//4x/K71WrFj3/8Y1RXV8s6Cu+//z5GjhyJESNGoLa2Vn6mTp0KAFi3bl0vXFXPQKnC88Du3bvx85//HGvXroXX6435rampKeZ7Xl6eXKeaMGzYMABAeXk5JkyYgLKyMuzduxf9+/ePe75kKoepiHWOaGxsxLXXXgu3240nn3wSxcXFsNvt2L59O372s591e3FuoN3GuvTSS/H888/H/b2goOB8u91rUMQ6R6xfvx51dXX405/+hMmTJ8vtR44cidv+xIkTaGlpiZFaBw4cANAeRQeA4uJi7Ny5E9OmTeuwLHCyQdlY5whaDlewmiqhUAivvPJK3PbhcBivv/56TNvXX38d/fv3x9ixYwEAt99+O44fP47f/va3Hfanta2TBUpinSOuvvpqpKen484778Q//dM/wWAw4A9/+EOn6yXn5eXhmWeeQXl5OYYNG4Z3330X3377LX7zm9/AYrEAAH70ox/hvffew3333Yd169bhBz/4ASKRCPbt24f33nsPn3zySUxR2j6NRLulyQRtuGHjxo1iwoQJwuFwiLy8PPHII4+ITz75RAAQ69atk/tde+214pJLLhHffPONmDhxorDb7aKoqEi8/PLLHc4RCoXEM888Iy655BJhs9lEenq6GDt2rFi8eLFoamqS7fp6uEHVx1LQBcrGUtAFilgKukARS0EXJJRYF/MqXhc6Ekasi30VrwsdCfMKS0pKcNVVV+Hll18G0J7OKCgowEMPPYRHH300EV1S6EEkJEBKq3gtXLhQbuvOKl7RaBQnTpxAampq0qc+kglCCPh8PuTl5cFoPLOySwixzrSKl3a4CdA+qpKPoDx+/DhGjRqlez8V4qOyshL5+flnbJMUXuGSJUvg8XjkR5EqsUhNTT1rm4QQq7ureC1cuBBNTU3yU1lZ2VtdVYiDrpgfCSFWd1fxstlscLvdMR+FPo5EJSnPZxWvpqamuAsVqU/vfHgyvDMkdHTDua7ipYjV94mVlKMbvF4vPB5Portx0aKpqems5khSeIUKyQdFLAVdoIiloAsUsRR0gSKWgi5QxFLQBYpYCrpAEUtBFyhiKegCRSwFXaCIpaALFLEUdIEiloIuUMRS0AWKWAq6QBFLQRcoYinoAkUsBV2giKWgCxSxFHSBIlYvIz09PdFd6BUoYvUy3nrrLdhstkR3Q3coYvUibr75Zlx66aVYsGBBoruiOxSxehHr16/H0aNHccstt1zwKlERqxfR3NyMYDCI/Px8uRrFhQpFrATAbrd3qRRQMqPHifXEE0/AYDDEfEaMGCF/DwQCeOCBB5CZmQmXy4XZs2d3KGd0oSI3Nxdutxv9+vXD1KlT4XQ6E90l/XB+ZT06YtGiReKSSy4RJ0+elJ+amhr5+3333ScKCgrEmjVrxDfffCMmTJggrr766m6dI9mKghgMBvHwww+LTz/9NOY6xowZk/C+ncsnIdVmFi1aJMaMGRP3t8bGRmGxWMT7778vt+3du1cAEJs2beryOZKNWEajUZw8ebLDdfzlL38Rdrs94f3Tg1i61CAtKytDXl4e7HY7Jk6ciCVLlqCwsBDbtm1DW1sbSktLZdsRI0agsLAQmzZtwoQJE+IeT1uDVLuaaV9HNBrFlClT8N///d8YOnSo3D558mQsX74cd9xxx1mPMWHChLgVXtavX49QKNSj/e0RdFlMdBEffviheO+998TOnTvFxx9/LCZOnCgKCwuF1+sVb731lrBarR32ueqqq8QjjzzS6TEXLVqU8Le0Jz7jx48XZWVlMdd24MABMWHChE73GTx4sHjuuec6LUj36quvilmzZvU5iaV74bWGhgbhdrvFf/zHf5wzsQKBgGhqapKfysrKhJPkXD9r167tcH3Hjx8Xo0aNimnncDjEm2++KQ4fPnzWe1xbWytuuummPkUs3cMNaWlpGDZsGA4ePIicnByEQiE0NjbGtOmsqC3hQq9BmpeXh/Xr12POnDkoLCzEX//1X+M3v/kN/u7v/g6DBg066/6ZmZlYtWoVbrrppl7obRdxVuqdJ3w+n0hPTxe//vWvpfH+xz/+Uf6+b98+AVzYxjv/xJNYHOvWrROBQOCc7vWpU6fELbfc0ickVo8T65//+Z/F+vXrxZEjR8TGjRtFaWmp6Nevn6iurhZCtIcbCgsLxdq1a+WKoxMnTuzWOS40YtXU1IhDhw6JQ4cOCZ/Pd173v6mpSVx55ZUXHrHmzJkjcnNzhdVqFQMGDBBz5swRBw8elL/7/X7xj//4jyI9PV04nU7x13/913Fd8TPhQiLWoUOHxDXXXCN/f++99877GfziF78QBoPhwiJWbyBZiWUwGGLWim5qaurgEZaUlIjGxsbzuj8+n0+YzWZFrO4iGYnlcDjEs88+K9ra2oQQ7Z5gZyrrfBcRV8Q6RyQbsQwGg3j22WeFEEKEw2GxdOlSMXny5E7b1tbWntf9CQaDYs6cOYpY3UWyECsnJ0d8+umnYv369SIUConly5eLyZMnn9H+6QliCSHE//zP/ySUWGrYjI44deoUXn31VUQiESxfvhz33nsvNmzYAJF8azZ0GwlZr/BigRACK1euxMqVKxPdlV6Hkli9jN/+9rf49ttvcf/998NkMnX4XQhxYYyJP29lngAki42l/QwfPlx89913QgghotGoeOihhzq0mTBhQpdsmLNB2VgXERwOBywWC4D2xSSfffZZ3HfffQCAjIwMvP7663jrrbcuiFyosrF6CRaLBTNmzEBRUZHcZrPZ8Nxzz+FnP/sZzGbzWddZTiYoYvUSLrnkEvzyl7/ssN3pdGLgwIE9eq5wOIzPP/+8R4/ZXShV2AswGo1YtGhRr53P7/dj2bJlvXa+eFASS2dkZGTgd7/7HWbNmhX3d6/Xi4qKCvm9sLDwvBf5vO222xAOh8/rGOeN83Y/EoBk8grvu+++Tq/D7/eLv/u7v4tp/7d/+7di+fLl53Rfjh8/Lp599llRWFio6zWplE4f+OTn54t169aJUCgUcw3hcFjcfffdcfdxuVyitLRUbN68WQSDQREMBkU0Gu1wH9ra2uTvjz76qBg3blyvXJNaE7qPwGq14sorr8SmTZvktp///Of45S9/ecb0js1mg9HYbga/9NJLMTN8AODpp5/G+vXrAbTPZIpGoz3f+TjoyprQysbqBYRCIQQCAfn98OHDWLNmzVlzhnzK27333qtb//SA8goTgPLycmzevDnR3dAVili9jGg0esGTCgCU8d4LH4PBID295557TphMpoT36Xw+yivsI59+/fqJmpoa8cwzzwiHw5Hw/vQGsZRXqDOGDx+OFStWwGAw4Morr0x0d3oEyivsA5gyZQquuOIK3HbbbYnuSq9CEUtnvPnmmxgzZgw+++yzRHelV6FUoUK30RVVqMINCrpAEUtBF3SbWBs2bMDMmTORl5cHg8GAVatWxfwuhMDjjz+O3NxcOBwOlJaWoqysLKZNfX095s2bB7fbjbS0NNxzzz1obm4+rwtR6FvoNrFaWlowZsyYTgeSLV26FC+++CJee+01bNmyBSkpKZg+fXpMrmzevHnYvXs3PvvsM3zwwQfYsGED5s+ff+5XodD3cD6BSgBi5cqV8ns0GhU5OTlyOrkQ7QVtbTabePvtt4UQQuzZs0cAEF9//bVs89FHHwmDwSCOHz/epfMmW4D0Qvv0+iydI0eOoKqqKqZ4rcfjQUlJiRwysmnTJqSlpWHcuHGyTWlpKYxGI7Zs2RL3uMFgEF6vN+aj0LfRo8SqqqoCAGRnZ8dsz87Olr9VVVUhKysr5nez2YyMjAzZRoslS5bA4/HIT0FBQU92W0EHJIVXuHDhQjQ1NclPZWVlorukcBb0KLGoQK12CRNevDYnJwfV1dUxv4fDYdTX13da4PZCL257IaJHiTVo0CDk5ORgzZo1cpvX68WWLVswceJEAMDEiRPR2NiIbdu2yTZr165FNBpFSUlJT3ZHIZHoogMo4fP5xI4dO8SOHTsEAPH888+LHTt2iIqKCiGEEE8//bRIS0sTq1evFrt27RKzZs0SgwYNEn6/Xx7jxhtvFFdccYXYsmWL+PLLL8XQoUPF3Llzu9wH5RX2fa+w28Rat25d3JNRecNoNCoee+wxkZ2dLWw2m5g2bZrYv39/zDHq6urE3LlzhcvlEm63W9x9993dqhasiNX3iaWS0ArdhkpCKyQMilgKukARS0EXKGIp6AJFLAVdoIiloAsUsRR0gSKWgi5QxFLQBYpYCrpAEUtBFyhiKeiCpCRWEubNLyh05f4nJbHq6uoS3YWLGj6f76xtkrIoSEZGBgDg6NGjavjMGeD1elFQUIDKysoeGc4thIDP50NeXt5Z2yYlsaiSsMfjUePfu4CenCfQ1Rc5KVWhQt+HIpaCLkhKYtlsNixatAg2my3RXenTSOR9Ssox7wp9H0kpsRT6PhSxFHSBIpaCLlDEUtAFSUmsZcuWYeDAgbDb7SgpKcHWrVsT3aVeQ7KU6kw6Yr377rtYsGABFi1ahO3bt2PMmDGYPn16hwo2FyqSplRnlwsm9BGMHz9ePPDAA/J7JBIReXl5YsmSJQnsVWIAJKZUZ1eQVBIrFAph27ZtMaUojUYjSktLY1YvvVihV6nOc0FSEau2thaRSOSMpSgvZuhVqvNckFTEUkgeJBWx+vXrB5PJdMZSlBcz9CrVeS5IKmJZrVaMHTs2phRlNBrFmjVrZCnKixl9qlRnj7kBvYR33nlH2Gw2sXz5crFnzx4xf/58kZaWJqqqqhLdtV5BXyjV2RUkHbGEEOKll14ShYWFwmq1ivHjx4vNmzcnuku9hr5QqrMrUMNmFHRBUtlYCskDRSwFXaCIpaALFLEUdIEiloIuUMRS0AWKWAq6QBFLQRcoYinoAkUsBV2giKWgCxSxFHSBIpaCLlDEUtAFilgKukARS0EXKGIp6AJFLAVdoIilM1555RUsX768V861Z88ePPHEEygvL++V850Jilg6o7eJtXjxYkUshQsYPTrn5wLB9u3bxY033ihSU1NFSkqKmDp1qti0aZP8fdGiRSLerXvjjTcEAHHkyBEhhBBFRUUdpmlde+21MW3/8pe/iPnz54uMjAyRmpoqfvSjH4n6+vqY4wIQixYt6nC+oqIiOe2Ljqf9rFu3riduSbeRlCtT6Indu3dj0qRJcLvdeOSRR2CxWPD666/juuuuw1/+8pduzRb+1a9+hYceeggulwv/+q//CqBjwY4HH3wQaWlpeOKJJ7B//368+uqrqKiowPr162EwGLp8rsmTJ+Of/umf8OKLL+Jf/uVfMHLkSACQf3sdCaFzH8att94qrFarOHTokNx24sQJkZqaKiZPniyE6LrEEkKISy65REqpeG3Hjh0rQqGQ3L506VIBQKxevVpuQxcklhBCvP/++wmVUhzKxmKIRCL49NNPceutt2Lw4MFye25uLv7mb/4GX375Jbxeb4+ec/78+bBYLPL7/fffD7PZjA8//LBHz9PbUMRiqKmpQWtrK4YPH97ht5EjRyIajaKysrJHzzl06NCY7y6XC7m5uX3CszsfKGKdAzqzfSKRSK/2o7fP1x0oYjH0798fTqcT+/fv7/Dbvn37YDQaUVBQgPT0dABAY2NjTJuKiooO+53NANdWNG5ubsbJkycxcOBAuS09Pb3DuUKhEE6ePNmtc/UmFLEYTCYTbrjhBqxevTpGFZ06dQorVqzANddcA7fbjeLiYgDtpbEJLS0tePPNNzscMyUlpQMpOH7zm9+gra1Nfn/11VcRDocxY8YMua24uDjmXLSfVmKlpKQA6Ej4RECFGzR46qmn8Nlnn+Gaa67BP/7jP8JsNuP1119HMBjE0qVLAQA33HADCgsLcc899+CnP/0pTCYTfv/736N///44evRozPHGjh2LV199FU899RSGDBmCrKwsTJ06Vf4eCoUwbdo03H777di/fz9eeeUVXHPNNbjllltkm3vvvRf33XcfZs+ejeuvvx47d+7EJ598gn79+sWc6/LLL4fJZMIzzzyDpqYm2Gw2TJ06tUPN0V5Bot3Svojt27eL6dOnC5fLJZxOp5gyZYr46quvYtps27ZNlJSUCKvVKgoLC8Xzzz8fN9xQVVUlbrrpJpGamnrGAGl6erpwuVxi3rx5oq6uLuZckUhE/OxnPxP9+vUTTqdTTJ8+XRw8eLBDuEEIIX7729+KwYMHC5PJlNDQg6qPlSAsX74cd999N77++uuY0tgXCpSNpaALFLEUdIEiloIuSCixLuZVvO666y4IIS5I+wpIILEu9lW8LnQkzCssKSnBVVddhZdffhlA+0IABQUFeOihh/Doo48moksKPYiEBEhpFa+FCxfKbd1ZxSsajeLEiRNITU3tU2mMCx1CCPh8PuTl5cFoPLOySwixzrSK1759+zq0DwaDCAaD8vvx48cxatQo3fupEB+VlZXIz88/Y5uk8AqXLFkCj8cjP4pUiUVqaupZ2ySEWN1dxWvhwoVoamqSn54eE6XQPXTF/EgIsbq7ipfNZoPb7Y75KPRxJCRDKc5vFa+mpqa4M1LUp3c+TU1NZ31GCR3dcK6reCli9X1iJeXoBq/XC4/Hk+huXLRoamo6qzmSFF6hQvJBEUtBFyhiKegCRSwFXaCIpaALFLEUdIEiloIuUMRS0AWKWAq6QBFLQRcoYinoAkUsBV2giKWgCxSxFHSBIpaCLlDEUtAFilgKukAR6zxwtkmb8WAymc5pv2TDhX+FOsFgMOCKK66Q37sy1w4AJk2ahKuuukqvbvUZqBqk5wG73Y5Ro0YhGo0iPz8fzc3NaG1txa5duzBgwABUV1fHFK4FAIvFAoPBgMzMTNTV1SWo5/pDEes8YDab4XQ6UVBQAK/Xi6ysLJhMJgwbNgxerxfZ2dn49ttvAQAZGRn4yU9+AqvVip07d8JkMuGrr75Ca2sriouLcezYsZgyAskOpQrPEUIIVFRU4NixYzh8+DA2btyIY8eOYd26dWhra0NxcTGKioowcuRIjBw5EsOGDcOQIUPQ2tqK6667DlVVVWhtbQXQLsW0dSySHYpY54Hy8nJUVVVh586dEELA4XCguLgYtbW1aGhogNvtxogRI1BcXIzq6mo899xzcLvdOHr0KPLy8mCz2QC0q9QLzaBXqrCHEAwG0dDQgFOnTqFfv344deoUjh07BoPBgGg0imAwiClTpsDhcMBoNOKqq65CMBjEX/7yFwghMGTIEJw4cQKhUCjRl9IjuLBekwRjz5498Pl8GDp0KFwuF9LS0uD3+6XttHPnTqxYsQLr1q1Dfn4+MjIy5PZIJJK4tQV1gCJWDyMcDuObb76Bx+OJWZoOAOrr61FdXY20tDTU19fDbDbDYDDAYDCgrKwsZnm5ZEePE+uJJ56QN4s+I0aMkL8HAgE88MADyMzMhMvlwuzZszuUM0pmRKNRVFVVoaqqKmZtw6ysLIwYMQKDBg3C/fffD4vFghMnTuDGG2/EzTffjObmZqSkpCRmeRIdoIuNdckll+Dzzz8/fRLz6dP85Cc/wZ///Ge8//778Hg8ePDBB/HDH/4QGzdu1KMrCQNfQSwlJQU/+clPcPPNN8Pv96OyshIHDx7EggUL8LOf/QxXXXUV0tLScPjw4QvGiNeFWGazOW4BtaamJvzud7/DihUr5EJFb7zxBkaOHInNmzdjwoQJenQnoRg4cCAmT56M/fv3o7q6GjNnzsSOHTuQl5eHb775Bvn5+aiqqsK1116LsrIyHDp0qNNj8XRQNBpFJBKB0WiEaK8a1FuX1CXoQqyysjLk5eXBbrdj4sSJWLJkCQoLC7Ft2za0tbWhtLRUth0xYgQKCwuxadOmTomlrUHa08vn9hScTieCwaBc7i0jIwMejwcWiwWXX345Nm7ciDfffBNCCKSlpaGtrQ2TJk3Ciy++CJ/Ph4EDByIajSIzMxNerxeBQABAewXEcDiM0aNHY+zYsRBC4JtvvsHnn3+OH/7wh9i+fTv27NmTyEvvgB4nVklJCZYvX47hw4fj5MmTWLx4MSZNmoTvv/8eVVVVsFqtSEtLi9knOzsbVVVVnR5zyZIlWLx4cU93tcdRWFiIY8eOobm5GQBw6aWXoqKiAm+//Tb++Mc/4s4774TNZoPVasW6devgdDoxYcIEzJ07F2+++Sb8fj/y8/PhcrlgNBqlHfrggw9i7969OHz4MCoqKmC322G1WjFv3jxMnjwZQgjU1tb2qRr5utfHamxsRFFREZ5//nk4HA7cfffdHVIX48ePx5QpU/DMM8/EPUY8iVVQUKBnt7sNj8eDCRMmYNu2bdLju/LKK7F582bZxu1246mnnoLdbkcgEMCHH36IESNGoK2tDUOGDMGTTz6JaDSKQCCACRMm4M4778TevXsxaNAgHDp0CN999x2OHTsGp9OJcDiMCRMmoLS0FAMGDEA0GsXSpUvx/fff4/Dhw7qqxq7Ux9I9QJqWloZhw4bh4MGDuP766xEKhdDY2BgjtTorakuw2WwySt1X0dTUhL179+Lqq6/GgQMHMGTIEEQiEeTm5qK+vh7BYBBerxe///3vUVhYiJKSEsyZMwevv/46ampqcPPNN+P666/HlVdeiW+//RYOhwMnT56Ey+XC73//ewDAsWPHkJWVhdbWVsyePRubN2+WKaLJkyfDbrdj7NixSE1NlTnKREF3F6S5uRmHDh1Cbm4uxo4dC4vFElPUdv/+/Th69GjcorbJBr/fj++//x5erxdtbW3IzMzE5ZdfLteQBtrTNx988AGee+45/Od//iduu+02BINB/PrXv8bKlSuxZs0aSZhJkyahpKQEEydOhNlsRlZWFqxWK5qamvCHP/wBlZWVqKiowJYtW7Bz507ce++9GDt2LKZNm5bwhRV6XGI9/PDDmDlzJoqKinDixAksWrQIJpMJc+fOhcfjwT333IMFCxYgIyMDbrcbDz30ECZOnHhBeIQOhwO5ubnw+/0IhULSyeBrNw8YMAAGgwH19fWYMGECvF4vcnJycNdddyESiciXbsiQIRBCICUlBaWlpaitrUVNTQ1CoRDS09MxadIk+P1+bN++Hd999x0GDRqE22+/HVu3bkU0GsV1112HL774AuFwGGazGampqZg3bx4GDhyIuro6rFixAs3NzTAYDGhubpaOQk+hx4l17NgxzJ07F3V1dejfvz+uueYabN68Gf379wcAvPDCCzAajZg9ezaCwSCmT5+OV155pae7kRAcPXoUFosFKSkpMBqNqK2txZEjR1BTUwOgfTDg119/LYn29NNP47LLLsPQoUOxdu1amM1mzJgxA9u3b0d5eTkikQhCoRDq6+uRmZkJo9EIt9uN8vJyfPbZZ2htbcW1116LPXv24Pjx4xBCYNSoUUhNTUVjYyNsNhvWr1+PUaNGYeHChbj88svl+LDbbrsNe/fuhdlsxosvvtjjccQeJ9Y777xzxt/tdjuWLVuGZcuW9fSp+wQoDuV2u9Hc3IxoNCp/czqdqK+vl9+zs7MxcuRIfPzxx2hsbITD4cCkSZMwe/ZsrFq1ChaLBVVVVTh27BjC4TCam5thMpngdrvRr18/HDlyBH6/H+FwWJ5n0KBBWL16NUaPHg2n04mdO3eiqKgIBoMBOTk5aGlpQSQSQUVFBfbs2YO0tDRdQhUXRpi3D8Lr9caQatSoUViyZAlmzZqF7OxsjBgxAqNHj8af/vQnNDY2Ami30f785z9j0qRJuOuuu7Br1y5UVFQgEAjg5MmTOHjwIPx+PzIyMpCTkwOHw4HDhw8jNTUV9fX1eP/99/G//tf/woEDB5CbmwsAuOOOO3DbbbfhBz/4ATZu3IgVK1bA7XbDZrNJ77SpqanHr18Nm9ERVqsVubm5SE9Px4wZM/DZZ5/h+uuvh8FgwPDhw/Hss892GCazc+dOVFRUoKqqCqtWrcLo0aNRVVWFrKwsHDt2DCaTCQBk3K+1tRXDhw9HKBRCeXm5XK6vvLwc/fv3RzAYRGpqKnw+H77//nvcc889cpjOyJEj8e677+py7Upi6YgbbrgBDocDs2bNwqRJk2CxWNDS0gKj0YjnnnsubgbBYDDAZDIhEAjIZdwCgQDC4TCuuuoq+ZvD4UB+fj4GDBiA2tpaqepGjRqFP/7xj9iwYQNSU1MRCARQXl6OYDCIadOm4bbbbsOnn34Ki8UCq9WKhoYGXa5dSSwd8eWXX2LWrFkoLi7G3r17cdttt8Fut+Po0aOdDui77LLLEAgEkJ2djXA4jM8++wz9+/eHz+eDxWJBRkYGotEo8vLyUF1dDZvNJqPuJ06cgN1uR3V1NWbMmIH+/fvju+++w5dffgmn04lZs2bhrbfeQnNzM8LhMD788EO88cYbMSq7p6CIpSMaGxvx5ptvYsuWLRg2bBjKysowbdo03HzzzSgrK8OqVas67OPxeFBdXY2amhpEo1Hk5ubC4XAAABoaGpCSkoKcnBzU1NQgPT0dR48ehcPhQCQSQUpKCjweD9LS0pCdnY39+/ejqakJX3zxBTZv3owNGzZgxIgR8Hq9OHHiBNauXavbSmqKWL2Affv24cCBA7jsssuwf/9+hEIhzJw5E1u3bsWJEydkO4PBAIfDgVWrVuH6669HUVERzGYzrFYrvF4vvF4vWlpaEI1GkZ2dLdVYS0sLAoEAjEYjmpqa0NjYiP3792PXrl3o168famtrUVtbizfffFOSUO8h0MrG6iVEo1E0Nzdj165d2LhxI3bu3NlhxOjIkSPx0EMPob6+HiNGjMCll16KgoIC5Ofno7CwUEokIQSqq6tx6tQpGdIwGo0oLy9HdnY2fv7zn8Pv98NiscBut8ecg4K3ekNJrF5EY2OjjHC//fbbMJlMmDBhAioqKuDxeOB0OtHY2Ai73Y73338fWVlZMJvNCIfDOHToEIQQKCoqQjQaxcmTJ9Ha2iolWm1trUyAT548Genp6Th58qRUo70NJbF6CdnZ2bj55puRkZEhPT+HwwGHw4EBAwagqKgI3377Lb7//nvY7XZkZGQgPT0dVqsVJ06cQDgcRm5uLhoaGnDgwAG0tbVh9uzZCAQC8Hq9cDqdMJvNMJvN8Pv9cuzW//2//xf5+fm44447MHfuXEyZMiUmd6kXFLF6AUVFRZgzZw62b9+OyspKDBgwAD/4wQ9w3XXX4eabb8bBgwfxySefIBwOY/Xq1WhoaMBtt90GoD1eJYSAx+PBjh07sHXrVlRWVuLo0aPIzMzEjBkzUFRUBJ/PB5fLhdzcXGRlZWHbtm04deoUPB6PDFMA7c5Bbm6u7klqpQp1hNVqxZVXXgmPx4MpU6bg5MmTGDx4MKZNm4bW1lZs3rwZmZmZMUnqwsJC+P1+lJWV4eTJk9i/fz+i0ahM29jtdqSmpqK0tBR/+MMf8C//8i948sknZXyLxknV1NRgzZo1GDt2LFJSUuD3+xEIBOQwpgMHDiAcDut27YpYOmLChAmYOXMmKioqYDAYUFhYiLvuugunTp3C0qVLMWfOHDz11FNITU2V3t66deswY8YMrFy5Em1tbbBYLPjpT3+KBx54AHV1dYhGo1i2bBmcTifa2towb948NDU1IT8/HzabDQMHDsTo0aOxcuVKnDp1CoFAAGazGcFgEFlZWSgrK8Pu3btjyKwHlCrUEVu3bsVXX32FUCiEffv2oaSkBB988AH8fj88Hg/+53/+B8XFxSgpKYHVagUASYB77rkH9957L06dOoX7778fTU1NCIfDaGxsxH/8x3/gRz/6EXJycmA0GjF06FDk5eVhzJgxaG1txVNPPYUdO3Zg0qRJcLvd8Hq98Pl8iEQisNlsGDdunFSNekFJLB0RCATQ1taGBQsW4KuvvsKePXuQmZmJr7/+GkeOHMGxY8fijvUfOnQocnJysGLFCvzN3/wNbDYbDh48iHXr1iESieDTTz9FZmYm3n//fQwZMgThcBhTp07F7t278cEHHyAUCuGyyy7D3r17YbFYMGXKFITDYWzduhWTJ09GTU2N7rN6FLF0xocffoi1a9fi3/7t3+Dz+RAOh+H1ejF48GBUVFTE3eerr75CRUUF/vu//xtjx47FqVOnMHHiRMyYMQNutxsOhwPfffcddu3aBa/Xi6FDh+LXv/41GhoaEI1GYbFYMGPGDHi9XpSXl6OtrQ1msxmBQAD19fXYtm2b7qpQEUtnRKNR+P1+1NbWIhAIYOTIkfjiiy9w9OhROQBQi5UrV8r/a2pq4HA4UF1dLe2o1NRUnDhxArW1tXC5XB1GKFBkPT09HQcPHsTKlSsxadIkjBgxQo7h0huKWL2IcePGYdWqVbIYSFfg9/vh9/tRX1+PgwcPdmkfg8Egx1rR1Du3243q6mrs3r27Q5VBPaCM917CkCFDEAgEkJeXh7S0tE7VYE9ACIGTJ09iz549iEQiuOKKK+Dz+WC329G/f3/d1SCgJFav4LLLLkNFRQUGDhwoS0SePHlSt/NFIhE5n9FqtcJmsyEUCqGtrU3X83IoYukIg8GAlJQU3HXXXRg3bhxsNhuOHz+OrKwsFBYW4ujRo7r3Qa9hMWeDIpaOsFgsmDp1Kurq6nDgwAFUVFRg//792LNnT6+QKpFQNpaOCIVCOHr0KJxOJzZs2IBIJIJt27adsaLMhQJFLJ2xb98+tLS04KabbsLq1asvClIBvVAURA94vV54PJ5Ed6PLyMvLg9lsvmDUX58oCqKAmOHHFwuUKlTQBd0m1oYNGzBz5kzk5eXBYDB0mGkihMDjjz8uZ5eUlpairKwspk19fT3mzZsHt9uNtLQ03HPPPbJYmcKFgW4Tq6WlBWPGjOm09sLSpUvx4osv4rXXXsOWLVuQkpKC6dOnx1QzmTdvHnbv3o3PPvsMH3zwATZs2ID58+ef+1Uo9D2I8wAAsXLlSvk9Go2KnJwc8eyzz8ptjY2NwmazibffflsIIcSePXsEAPH111/LNh999JEwGAzi+PHjXTpvU1OTAKA+Cfo0NTWd9Rn1qI115MgRVFVVxRSv9Xg8KCkpwaZNmwAAmzZtQlpaGsaNGyfblJaWwmg0YsuWLXGPS9Xw+Eehb6NHiUWD1rQrWfHitVTggsNsNiMjI6PTArdLliyBx+ORn75Wf1ShI5LCK1y4cCGamprkJ1H5L4Wuo0eJRQVqtUuY8OK1OTk5HcpGh8Nh1NfXd1rg1mazwe12x3wU+jZ6lFiDBg1CTk5OTPFar9eLLVu2yOK1EydORGNjI7Zt2ybbrF27FtFoFCUlJT3ZHYVEoosOoITP5xM7duwQO3bsEADE888/L3bs2CEqKiqEEEI8/fTTIi0tTaxevVrs2rVLzJo1SwwaNEj4/X55jBtvvFFcccUVYsuWLeLLL78UQ4cOFXPnzu1yH5RX2Pe9wm4Ta926dXFPdueddwoh2kMOjz32mMjOzhY2m01MmzZN7N+/P+YYdXV1Yu7cucLlcgm32y3uvvtu4fP5utwHRay+TyyVhFboNrqShE4Kr1Ah+aCIpaALFLEUdIEiloIuUMRS0AWKWAq6QBFLQRcoYinoAkUsBV2giKWgCxSxFHSBIpaCLkhKYiVh3vyCQlfuf1ISq66uLtFduKjh8/nO2iYpp9hTqcWjR4+q4TNngNfrRUFBASorK3tkOLf4/wtz5uXlnbVtUhLLaGwXtB6PR41/7wJ6cp5AV1/kpFSFCn0filgKuiApiWWz2bBo0SLYbLZEd6VPI5H3KSnHvCv0fSSlxFLo+1DEUtAFilgKukARS0EXJCWxli1bhoEDB8Jut6OkpARbt25NdJd6DclSqjPpiPXuu+9iwYIFWLRoEbZv344xY8Zg+vTpHSrYXKhImlKdXS6Y0Ecwfvx48cADD8jvkUhE5OXliSVLliSwV4kBkJhSnV1BUkmsUCiEbdu2xZSiNBqNKC0tlaUoL2boVarzXJBUxKqtrUUkEjljKcqLGXqV6jwXJBWxFJIHSUWsfv36wWQynbEU5cUMvUp1nguSilhWqxVjx46NKUUZjUaxZs0aWYryYkafKtXZY25AL+Gdd94RNptNLF++XOzZs0fMnz9fpKWliaqqqkR3rVfQF0p1dgVJRywhhHjppZdEYWGhsFqtYvz48WLz5s2J7lKvoS+U6uwK1LAZBV2QVDaWQvJAEUtBFyhiKegCRSwFXaCIpaALFLEUdIEiloIuUMRS0AWKWAq6QBFLQRcoYinoAkUsBV3w/wCsyCyNVHKzRQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best IoU: 0.0637, Best Dice: 0.0018, Best Acc: 0.4333\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------\n",
    "# Region growing method application\n",
    "# -----------------------------\n",
    "\n",
    "# empty the cache for model and training process\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "# Do the segmentation to exteract the spine part\n",
    "# set the threshold and the seed point for the region growing method\n",
    "threshold = 328\n",
    "x_axis = 80\n",
    "seed_point = (x_axis,70)\n",
    "\n",
    "# the variables for evaluation\n",
    "total_correct_pixels = 0\n",
    "total_pixels = 0\n",
    "total_iou = 0.0\n",
    "total_dice = 0.0\n",
    "\n",
    "# set the hyper parameters and the paths 设置超参数和路径\n",
    "data_path = \"./dataset/Spineweb_dataset15/test/\"    # the path of the dataset\n",
    "file_list = os.listdir(data_path)  # list all files and folders in the folder(data path)\n",
    "file_count = len(file_list)  # get the numer of files\n",
    "print(\"Length of the dataset:\",file_count)\n",
    "resize_scale = (128,128)    # set the resize scale for images\n",
    "print(\"----------------  Loading the dataset and evaluate  ----------------\")\n",
    "data_count = 0\n",
    "poster_threshold = 200\n",
    "for index in range(file_count):     # iterate the whole dataset\n",
    "    metadata = np.load(data_path+str(index)+\".npy\")     # load the data\n",
    "    image = np.array(Image.fromarray(metadata[0]).resize(resize_scale))     # Using the PIL to resize the transfer into npy again\n",
    "    label = Image.fromarray((metadata[1] * 255).astype('uint8')).resize(resize_scale)     # Using the PIL to resize the transfer into npy again\n",
    "    label = np.array(label)\n",
    "    label[label < poster_threshold] = 0\n",
    "    label[label >= poster_threshold] = 1\n",
    "    \n",
    "    # print(type(image),image.shape,image)\n",
    "    # print(type(label),label.shape,label)\n",
    "    output = CTimageLIB.regionGrowing(image, seed_point, threshold)\n",
    "    # evaluations\n",
    "    current_correct_pixels,current_pixels = calculate_pixel_accuracy(output,label)\n",
    "    current_iou = calculate_iou(output,label)\n",
    "    current_dice = calculate_dice(output,label)\n",
    "    # add the results to total one\n",
    "    total_correct_pixels += current_correct_pixels\n",
    "    total_pixels += current_pixels\n",
    "    total_iou += current_iou\n",
    "    total_dice += current_dice\n",
    "    data_count += 1\n",
    "    # visualize the results\n",
    "    if index == 100:\n",
    "        visualize_results(image,label,output)\n",
    "    if index > 70 and index <225:\n",
    "        if index%5==0:\n",
    "            x_axis = x_axis - 1\n",
    "            seed_point = (x_axis,70)\n",
    "            \n",
    "\n",
    "final_acc = total_correct_pixels/total_pixels\n",
    "final_iou = total_iou/data_count\n",
    "final_dice = total_dice/data_count\n",
    "\n",
    "print(f\"Best IoU: {final_iou:.4f}, Best Dice: {final_dice:.4f}, Best Acc: {final_acc:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of the dataset: 3005\n",
      "----------------  Loading the dataset and evaluate  ----------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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Bg5Cbm4twOAyv14v9+/cn3C9pTSypdqR0kUFVoNPIBzo6RwaK4x2XdCnQZgFgkXr0jOfk5KClpcVybo/Ho10ZPD/jcfRjNTQ0oL29HdXV1WhsbERWVlaXTAOqI44IafOw45lxAHS6JZhLxeuSlMwd43OgTUpQpdIGi0ajaGlp0b4rjpRbW1stWRkHQ1oTSz4kvtUkGJ2ITqdTuyG4TdocACx2Gu0KSbx4VwUlB4+RoSEGkuO96Tw2OzsbkUgEbW1taGxs1J0XnxMmXScAtKHvcrk0qaRnXap3+tyky0DacvK+6M1n6Ic5ZZmZmWhra0NTU5N2ffQluyGtjXcAFgOTtpGMr0nHpgwM87sM0soRoFQtkngej0dnK3A0JcNFlA5Ap00nk/noKM3Ly9Nk4bVo84TDYU0E5kU1Nzdrqcr6VzJITVXXXWyUajxebXIE29raqglEyVpaWor8/HyLv6u3bJF4pLXEIuQQnqqIkE5JKaHkPkopnYVJqSXtMBJRGvD09chRIkeNXq/X4k+T+0mS+v1+HdohAd1utzbIY7EYPB6PlhhNTU3a10WJSGJ4vV4dXpL+LUpfZjawLVRv/PDeAehEyv379yMUCmkpmRb5WP2B+DeIdhGT9eI7oKcHQwJwBARYDW8AFgJRClIiyhEiO02qZjl8JwFkdgXTbkiiQCCgz0kCMM2FNhvzw6jC6urqUFhYCKDTkStfFD4DjlIbGhq0JJN5Z/S3tbS06GgB778vGQ5pTyxKH0oSABa7RsbyAKunPT7thR1FY1oGYeXQXHrHadBSXfDclB4kC2DNOZeOWXltr9erBwdtbW16Ukc0GtWpLTJsQynJfKrs7GyLOpSOW8CawyalKEkVi8UQDAa14U83hcvl6lMJyrS2saQjlPaRNMrl8FpmUwKw2EIyYEspEp/DJPfn8XR+hkIhhEIhtLS0aMLJgYUMcNNWkSk73If7U93ReGfWaHZ2trbLAHRpa0tLi8634vX50lHNUsozHihJxWcon5l0l/SlaG5aSyygM3NUppJIm0qOzuirkXlT8TNYpDNSxtmkFJKpKF6vF0opNDU1aVLFq0QZt+P1pO0m3QRSbZM4MhOW6dNAhy+KqpESidPAaLPJZyHzwmTiYjQaRU5OjrYzqbqbmpr0s6LRnyjSnlhAp8SR+VjsQD6YSCSinZHxoZTu7Aip7uJ9XtyXJMjIyIDf79fpK7I9ra2tOlNUhmJILBkuoYSgO8Hj8ehO5nXa29st8x0B6PmINLAbGhqQk5OjjXYAOotB2lkkisfjQXFxMQKBAJqbm9HW1oa9e/fql4XRgL6MCtNaFcq3UXrRJUHkKEzaHJRM0msv02BkMhxVirwOpRjPn52drTs+HtKLz2PZ6VJ6SruQRjdfFpnJKfPcZVyTqo7uA04n48iRWRHS3SDbHgqFNEkpoThQoZmQcN8kvGcKIt59IAPNfKDShgGs3nq5Pd6Q518ZGJb/S7DjSEgZa2S6i3RZOJ1OnbTHADJdAbRpZJinp/byOpRqJARJEK/K6evyeDxa6gUCARQWFqK1tRVff/01GhsbUVtbq10YTmfnJNgjxt1AtSYDytJA7y5dhvtIfxW/M9epO+Nbkqu5udkya1jmhHFyp0w0ZN47jV8GkaWRLKfNc5QniSZHvNJxSwLR3pJrLFId838+J5liM2jQIPh8Pj3w4Ox0vpQkFm3HRJHWxAI6O10ao1K1dacuAauEYgfFh3lk/JEdweE+PeAMvfB3Elbmc8lANn1L7HSSjd+lcc82sQ1yX/q3GA1gPhWP5QQPAFr98T7YBim9/X6/jjkyc4KjSU5n607N94S0JxYhsxLkSIpvONCZxSBDN0CnrwuAxf4iWSVBpT+H0srhcOjgNFVRLNY5e4ehlLy8PNTX1+sgL6UjSZOfn49AIKAlVjAYRGtrq8UZ63R2zLpm9oOcUk+PPwlK45/PRqbeUH0Gg0F9jqysLB1IZ1IiY4lA10UQekNaE4uinSqRnRwfMJW2knSkkiycnCnjhZKYlEQyXZnXkFkPUnVIRym92bymtH8yMzNx1FFHYcyYMRavPEd3rOvAnHN66qWBT1UrXSZUX1TZHLTE24gHDhzQzzA/P19L0+7CUUeMKuRNS5UlbZGeIN0NWVlZmkQyhiilVXyaDc/NTAZKEA7pZdxNZnzKCRKtra3Iy8tDWVkZysrKdHIf86hk+AfotCdbW1t1LJB2W0tLiyV7g7lWbKvcjy8D7UOOit1uNw4cOKBfLN4/sy/48iXcNwnvmSDu+P+XmZWfY489Vv/e2tqKG264QSeRzZo1q0s5o75AhiVog3TnyKOalCotPn8J6IztkTysPiP9WdyHHROLxZCXl4eCggI9F1BKNOlqYH6Ux+PB4MGDccwxx+iaVuxUGvdUwXLChSSarGhIqSVfqHiXBSWZrInl9XpRWFiIESNGoLS0FF6vV//Oe2NyYXdJkz3BFol1/PHH47XXXuu8iOjoH//4x3jhhRewevVq+P1+3HjjjbjkkkvwzjvvHNK1pHiXKk9mJwBW5yfJyM5gsTMZaKbhKgcFlIjdee7Hjh2LSy+9FB6PB2vXrsWbb76p7RpKKKCTqJRWLpdLz94hotGoVqfSe856DXRf0PZyOp16+pacWEqJ1dTUhLy8PH2urKwsi49ryJAh8Pl8WvIGg0Ftw/F4qvZEYQux3G53twXUGhoa8Pvf/x5//etfceaZZwIAnnjiCYwbNw7r16/Xq472Bd1lLUiDXSbo8TdJEqAz7YYdReOWEomuAKbkxA/5gY4VWCdPngyPxwOv14svv/xSr8LFN166IeiU5GQIn8+nVaDT6URLS4tlMMDgtEwAZNDa4XAgOztbjxIBq7qX2Q1UzQQHFkCn1JfxQgB6EML9EoEtDtKtW7eipKQEI0eOxJw5c7Br1y4AwMaNG9HW1obp06frfY899lgMGzas16K2nHokP0Bn8Fm6FOJJxtFPfEaBjOeRLNJ1wIfMgDDQdeVSuV9OTg4aGxtRX1+PkSNHYurUqcjKyrIk2dFZCkBnL1BasY1tbW1oaGhAY2MjmpubsW/fPp3GTMemzI7lPefm5sLv9yMzMxM5OTkYNWoUTjjhBIwePVqn09B1QZ8VJen+/ft1KKe2thYAtLdd+uLkqrEHQ78Tq7y8HCtXrsRLL72ERx99FNu3b8dpp52GYDCI6upqZGZmIhAIWI4pKipCdXV1j+dcunQp/H6//nC5XEbsAVgII9WKdHLKoK+0q+iR5oe2hRwdyn3k9TweD4qKilBYWIjq6mq9XNzUqVO1DZSZmQmv16szCoDO3DGSif9LcktjPBgMavVM94SUzE6nE3l5eSgqKkJZWRkKCwvh8/lQVFSE0tJSFBQUQCmlp8tL7NmzB1999ZWWsBxd84WSI+pE0e+qUC6MfdJJJ6G8vBzDhw/HqlWrLItu9wU91SCl/SJtLAAWVQFYSzPSdiEoLZilICdQyECxnHZPYrrdbvh8Pnz729+Gx+PB1q1b4Xa7NfGl70mmSnNun1StsmYWAD2ZgRKZGRS8rizQRukXDAYxePBg5OXlWWb4eDweBAIBhMNhtLS0wOPx6CJtlJT79+9He3u7tsVqamq0jcbrJ914lwgEAhg7diy2bduGs88+W89dk1Krp6K2hHwQEvEed+l6ADodonJURiOUwVqmtEgpIW0s6ZSkxMjMzMTw4cNRVlaGo446CgUFBdixYwccDgeKiooQDofxwgsv6Bk5MvwSn/vEUaIkDUsXSRVOSSYlMsnIewU6J3dIXxlfQLoMmECYl5dnsSVlmvPQoUNRV1eHYDBoKbKbKGwnVlNTE7744gtcccUVmDBhAjIyMlBRUYFZs2YBALZs2YJdu3Z1W9T2YJDJeUCn15yjOsA6g4VBVTkRglIE6By9MkuB/iin04ni4mLk5+ejuLgYRx99NEaPHo22tjZUVVVh165d2mk5bNgwfPbZZ6isrNT+JZkmw2vRa09JSF8TJSRzpygtuT0ajVpmLtOlED8C5nepLmWSIUeYNMilSREKhZCbm4v8/HzLy8tJKYmg34l10003YebMmRg+fDj27NmDxYsXw+VyYfbs2fD7/Zg3bx4WLlyIgoIC+Hw+LFiwAFOmTDmkEaH0CvPhshM5WpRuBBKKxrQMHlMdMa7GWlFHHXUUSkpKUFBQoEdmDQ0N2LVrl8U/lJeXh7y8PGRmZuosAUoHEpjkkBkMDDFRYtKhSVLK9BbafNKVIj3+tMcYOwQ6E/mam5u1gU4Hq5R8JBttP6Y5+/1+hEIhS+pOIuh3Yn311VeYPXs2ampqUFhYiKlTp2L9+vV6ZLJs2TI4nU7MmjUL4XAYM2bMwCOPPHJI15IuBUkkqjR2BAPAMsgr87CoJo499ljk5OTA5XLpst8ejwdNTU3YvXu3LpohZymzaP/gwYPh8/mQk5Oj6zMAnTYdj5Geb3ZuvK0V7yogiXgMScr9qGaZXx8MBnWdeqDDXbBv3z40NDRY6m7JnH7mXbGddJEwh6uxsbFP6rDfifXUU0/1+rvX68Xy5cuxfPnyw75WvPhnp/C7dIhKj7mUAEx1ycnJwbBhw+B2u7Fv3z49EqPB29DQAKUU8vPzMXjwYL2eTFNTkyXBTgZs5SgUgEUNAdC2Dn+XZM3OztbSi+qaFY4ZZpHuFkqiSCSC3bt3Izc3VweU29raUFdXB4fDgUAgYJGabJ8cEXPihlShcjCUCNI6VijVHEGbgg9Oxva6C+mwQ2KxGCorK3X6CbMwAevEi6+//lqnIp9wwgkYOXKk7hhKnhNPPBHvvfce9u7dayGY9Ow7HA5ty9FxSXICndVp2KmyVBHQGXGQkYfm5mbU1dWhvb2z6L9Mt5FmgpSE8lwOh0PXHqWfK94HlwjSmlgy2i7DDfFvlvTDUHXER+yj0Sh2796tg7+UbjLNhHE8km/v3r348ssvcdJJJ2HMmDHIysqCUgrjxo3DtGnT8Nxzz1kmX5D0JCBtJGnfORwd6+cww7O+vl4TgSosIyNDS1Deh8vl0pMfcnJy9OhOqn+WgIw36qXpIFOSY7GOUpK85hGTjyWndHWX8Sn/Ssem9CZTuvEcnPXC7dIIpqqRtkZVVRVqa2vhdrsxceJEDBo0CNnZ2Tj//PPx8ccfa98WR1tsN8nKUBJHZ1RfMrZIVc1RLSWpdClINwLDRZSMrMHg8Xi0cc/fpBrkC5Cbm6sry0ib9YhRhdKolRkJlAYU7fItlT4knoNvpnRdSIcmSRuLxbSTV8bTgsEg3n33XYwaNUoThfFAqjEm09EgpppiDSr5UlB90QnKAQX9aqFQyGLLUY1lZ2frMBKvzUkUHo9He9bjXzYa8jI7xOv1orm5GS6XS7s3kh6EHijInHUJaVeReDKeKIOyDLnwfzmaBKDVlsz0lHYcCSNjfQDw73//G3v27NFStampSTsaHQ6Htpk4epMpy06nEzk5OZYVL2j3NTc3W5zAbCPQOfdPqizmwCulkJ2d3W0SI5+hlEh8QejEpeRMFGk9S0fG/+JTYgg+MEqp+DgZVQAzGGT4giqDpJOOS5lvFgwGAUAfv3//frzzzjuor6/XUobnknYNjXO/3w+fz6djjwD0wKK5uRnBYFDnoXO0Ju+XhJfBcrpXpGHOF4jSmAQlMfks6QeTvjWeL1GktcSSdkF88JkPkPvRKSr9VzLZTzofZQYozy3/53mYbhyLxVBXV4eNGzfC4/Hgiy++QGVlpS6sRuNcVoEJh8PYt28fhg4dipycHB0bZJ5Uc3OzXrewtbVVZ0LQCcrz0N1BScp7kjXl+XLxOHlvMu2Iz0BOCqHLg5MwEkXaEyvecy1ntci3Feh8iNKZKFNogM64GtC5+ICsr0V7hB92Qm1tLV577TWsX78ewWBQqz7aUTJ4zZEaR5Vut1svDiojAjTiSSy+KCQ2Dfz4zNl4aUPVy2fGNsggPs8lncaAtQR5SgWh7YR8G6XRTX+VdIayM+V3aVOQYHIYTmdmfBYF/UGyAFskEkFTUxPq6+v1xIZBgwb1+qa3trZi165daG1tRUlJCQoLCy0DjAMHDmgPOKfgE5xgwXQZOZFCzprmNqp0WfGGpOF2GaFgSUpZ2umIcTfEp/NKyOCzzCiQPiXAuuSvPJbnlwXNpHsCsJYAlxWJac9wRg1fAI/Hoyvz8ZwtLS3YtWsX9u3bhyFDhiAQCGg7jvFGOQJVSul1BKmypSQj6SnV6H+S0l2OAKW0Zrvb29vR3Nys3S3x0iwRpD2xKJlk3DDe7oofIZJkhHy4fEPZadJPlJ2drSvcyURBp9Opl7vlOTgpQdYq5UiQbeG8P15/3759aGxs1IQArKlBfDHoTOVcxpaWFu3nam5u1qpbumLkS0E1Ll0cbCMAXSOVsUkG36WtedC+SXjPFIQc0cghNADLW0zEpynLTpNDbukQjA8cUxJRPfF6dEjm5uYiKyvLopqlK4JSkBNWuY/8n23gaAyARXJxJEmnKlOieX65tg6n3jNDQRazpcSlRKUtxcwKDiIo7ZKaNpMMdOd2kLaUdA9wdEhQlTBmKGfm0O5ih/Gtp3ecx/FadBXIQDM7j0Tn9fgiyBihDLWwrbwWAIuKY/iFxKmvr0d7e0eJIymlqTZ5HEM0dINQdcrUIhn45jkA9CkDOO2JFS+eZeyMYEyO0gboTJshMdhRdIjKUVz8+dnBMj0Y6BxByWE7AK1WeT1KGjpIZUIhiRUOh3VJbF5Xqn7pHJajOhbAlcXYAFgycElYkk26Gzj1SzqBuzMfDoa0VoXsdGlQy6AyO4CqTQaE5Tmo2uQAQIZMpAqQ6SNygkS8D40OUTo1pXSQ16JzlipQdp7M4+pu2B9v90jHJ4+X5GI7KcVIVpl1wZeMRCch+WIkirQmlhz5EXxQMotBjnpkkh3QKdnoK+I5JMmoFuKH2zSi5RvNzuExNLblm8/rylwymahIaUHVS7+cHHww4VC2GegIILNoiayyTJeDvHc5KJAZICQWZ19zIHHEEEu+mRylxTtE5ehK5i5JhyrVgXSs8ryEJEz8A5YDAek3YnJebm5ul2lqMuQSfy/yIyUkpW8kEkFDQ4OuxcAXh0SQapP3Lvfj9ejPki4Yttnn81mKuPH3RJH2Nhbf7ng1JlUjDXJKFAmZvsxUEpn5ICUNVRNVDO0xucop33R2AuOEckUuoJNAHExIo53/M4ogfWUALEXbOAKVtqWMA8Yb8gAsz4Gqj9tp7w0aNEiXm6QZccTECgn5JlFlyNEfJUy8fSX3i/eqS0gVKDufZOWQnA5GDhRkHJJSimpRSiF5PQ4m4mfg0A6k64Kef07hYkBbSmR5TdkO7iOlKtDp5JWZHdJ9c8SU45ZEkXaOdBfEG8Qyw4FqgPPt6NuRQWgp4SQZeH5KKgaYSVYpIfmXBKXKoRRjB1Iiyfgl2y8XZuKKYQUFBcjKytLxRLlyGW00qkBeP97rLm04zoOkt57/M835iFmvUAaF49UHYC3qKu0lOhlpDEsDWxKJ+8mwkDwP1RmXKCGJ2A5J/Pi8d8YWpY0n3Ra8P6AzUzYjIwODBw9Gfn6+vk5GRoYO/Uh3BEksC9NS0lLiyYA0nb38nf4xPg/5XBNBWhMrHvHxQABa1XWX9UBEIhFtYMtRG2ANdAOdncxzx6sHOmKlU5SSjVJJTkGToy6qOvqXZBiGBeLy8/Ph9/t1rJAe83A4jLy8PAtZ2H7ed/zIVGZAMIYZ7zbhtr6Ec4BvALHY8dJojXc/UAXEOzzZAXKoLdWkTCGhWpAjKSnRZAkk6UOTKdEkFUNDRx99tJ64wd+ZKQp0etqzs7N1O7Kzs+H1etHY2Kgdmqy+w/ujNIof9fF58F75EgSDQZ33xegDU36kxJLrCR0MaU8s+QZ25y2X36U6k8a6rAIjVSrfaJmOIlNS5PQtggTuzmsvJdDgwYN1KrEkqdvt1gU6MjIy4PP59HFU3ZzzyOsWFBR0KQsuU695fsBaVZrBdtpocvQnn6U8T6JIe2J1Z5/05G+RLgDpT5JxMRKMud5AZwkjeTyJJUdNPE+8HcZt9OL7/X49vYt5ViQEwzHyHAzPUBqSRPSK044KhUKWMuCAteKhdMFIm4mSiESS+WNsO1+ARPGNcJBKvxWALkSTM6S5jR0u7Rv+BlhdEdyfEoXSgz4tqjsZquF5eBwlks/nQyAQ6BKKYUqNy9VR7pvhIs6K5upfPC9Tj6VLgjHIeHLLZ8GBClUcXxpZz0EppUeZciEDWz3vb731FmbOnImSkhI4HA4899xzlt+VUrj99tsxdOhQZGVlYfr06di6datln9raWsyZM0c/5Hnz5um5cocCkoQf6VQEus98jA8HUV3IsEa85KPNIt0A3MZzyFglg968TlZWFnJycroN6LLTaahLCcEMCGk/8RjpuFRK6Yp/EiSEfA7SaSrbDHQOIijBZFgoUfSZWM3NzRg/fnyPtRfuvfdePPjgg3jsscewYcMG5OTkYMaMGZaiq3PmzMHmzZvx6quv4p///CfeeustzJ8/v69NsXjIJbqbCCHfRh4bTxDZYRz9xXvrmeMu1QMlmRzmywwH2jvs9PjcMXak9J5TklA9cjk5qW4pIUkAOSKV8Un5TOQKaLSxeJ8kKrfLdRoZVE8UDtUXP338wQ4Hnn32WVx00UUAOt6YkpIS/OQnP8FNN90EoCM3u6ioCCtXrsTll1+OTz/9FMcddxzef/99TJw4EQDw0ksv4bvf/S6++uorlJSUHPS6jY2N8Pv9GDFihMVu4JsFdLoZAFgevBxBSv8QJQpTiXk/8WnNfOhut1tPyGBHs7qwHMXRR+R0OpGbm6uLfVDSyTilXE+HlW2ysrKQm5trcWTy3kgsEp33yAmy0s6iapdqsa6uDk1NTZZUIOmr4n0wAyMSiaCyshINDQ163eie0K821vbt21FdXW0pXuv3+1FeXq6L165btw6BQECTCgCmT58Op9OJDRs2dHvenorbxquGeEkgR1JAZ2pvvKNPZm2yo+PFvrSdZHoJEW/DST8Y0FmVkMY3t0vvOPdnzA7oGvwmseRxlD7SIcy8KrksC++Dz5NB7HhVyOcVDof1p7m52aJ1DoZ+JRYL1BYVFVm2y+K11dXVGDJkiOV3t9uNgoKCHgvc9lTcVoZheB6Cb6/0wkvHoRz9SdUSTxDpGJV2G68vwy8EJ1Tww8C0VMssL8RRqbSrpHSULwklVDQa1flU0qCWLwhLL3EqGhcQZ458MBjUki9+BhIhXxyZVpQI0mJUuGjRIjQ0NOhPVVUVgK6jQZmDDqDbByEfpCSNnCsYnyYipYL8Kx+8tK9ISBKBM4ulPUcJw1QXHud0OrUXnKSXUoVk5L7xdiHQmV7DhL76+nocOHAAtbW1aGpqQnNzc5d2xj8jKXHjXTqJoF+JxQK18UuYyOK1xcXF2Ldvn+X39vZ21NbW9ljg1uPx6Ap7/BDSTyPVXryDsjtCSLdDvMdaPkgZEJbpNfJYmd/EDuYQXs5SlhKS98Z2UCrJa8TnQ8k2MRtBtoWkkj43SXigsz4Xj5WjQc4skpKrO2l2MPQrsUaMGIHi4mJUVFTobY2NjdiwYYMuXjtlyhTU19dj48aNep/XX38dsVgM5eXlfb4mOzw+nCPfOqI7svEv30o+XHYuj5OdQukk1SP9T7KGAiUW5+pJv1M8eZig53a79Xo8lFSs4UC/mZytw1QdzqKhGpR2HjM2SDLp1JVpO1y8gNeKH/Xa6nlvamrCtm3b9Pft27fjww8/REFBAYYNG4Yf/ehHuOuuuzBmzBiMGDECt912G0pKSvTIcdy4cTj33HNx9dVX47HHHkNbWxtuvPFGXH755QmNCCVIJD5EKY2k30eGNGgTcYTFfYHOEAcfPDtb2nH8X0oqOlrlIEGqWRlukZM4aFcBnTn6/MRiMWRnZ0MppW0i1gNlLI+k4kLqQCeZ+bIw7MR2yuRBuXIqiczSmDy3tEXjbcze0GdiffDBBzjjjDP0dxb2nzt3LlauXImbb74Zzc3NmD9/Purr6zF16lS89NJLlnVY/vKXv+DGG2/EWWedBaezo9Dtgw8+2NemWGwsmQkKdEqWeAM3/njAWkJSvtF8sPE5WZKcsgyjbAcJLtNxpNphR8b70Tj9SmaikrgkCtWrPK90eMqpZvJe5YsnJTvbk5eXh5qami5SLN7mTKhvDsePlSzQjzV69GgAnUaoy+XSsbf4oLK0E/jQJJkIkpOqjXabdE5Kv5AkEaUOnZAkPuf6UXJKVSgD4xy50TdFXxkdlZzxnJHRseJ8VlaW9ovRCOe6OLyn+HQhKdnli+hwdGRRsGwSl/GVKTexWAxffvllQn6stA5CS9LQ9pAzWYDe62PJ4DDtG/nAw+GwrhRDcP94VUpJx9RinqM79QxYKxHTjmIuO68hMx+kOpYOTZmNINUpIdspkxblOtFsJ6UWl66Tbpj4xMWDIa2JRRLJUZAU7dJYjzfmqZZkZwOwvM1USyQADWYpqWSAmvaNHBlKgvOatLekM5R1saTN5vF4tCTmgIKdzSJqTqdTr2pRV1enXQk8B++RWRNUo5KkJCjvhXagvG85nzERpDWxZIxL6n851I5PIYkP4sYb8NwubRZ2CGCtCcHOYcfQvmEHcegfiUSQl5enfVpyNErPtpSqjPUBVmNckpWjT26nE5RtlAVsOY2L8yBJboazKMllyrasCcaXKGme92RAeqblX2k6UirJcI0cDXV3Thk6obdahnN4XqnqGKCWGaZUKSQMXwS6NqT9IwPI8TOLZAipO3uNnnS2g+dj/VB+5MvFdsl0a2mbyWcr7zsRpLXEklJKGtjdxfoAWDpdqsD49Bn+pcQjORyOzlwmOZOHviNeg5JKOhxZHZkdKv1m0qXB6VqyLdKmAjoLqvFvKBTSNd6ZAEgbStpVshqhDN7Tkx/vlpHqktsSRVoTC4BFetDWkn6meEOe+8oRoVSJgDXTEoB2TDJEwo5n3XY5MJBqgxWOW1tbtTSRbgnWXpDSQroxZJtIGI54qQKDwSAOHDigCcHqftJBKtOVpd1JqRQfnpJhLbY5Puh+0H5JeM8UBR+AfAu787fEh264X3ymqAzPAJ2qId5rTTUpnZ4ypCPVCG0ppuTQPUCjW+5LSSHnOrJt0lMPdDiruZwuJZSsd0oJKP1c8aYCJZ/8nWSTI2Cq1USR1jZWfJK/JJQkT7xxH58qwofMTpQpKfI8csYLJQLVo7SbZP4VXRBM5pOecNo8gLWaiwz7ANAzZ3iN9vaOuly1tbV6cOHz+ZCXl6cluPS7SfUn7TlJUl6P9p1008Q7YRNB2kusnkaGEiSFfOvjHYjxEq07sS9Het2l6rAzeC75hnN9HKmGuYQK6y9IXxzQOd2eI1C2LxQKaWM9EAhYfpPqnv9LtSptSCm1gK7T6KmS+Xz64sdKa4kFWIPQ0qYgMdjx8Ql/QCdR4lVkdw8wvtNlCIbnz8jI0FmXVJUMZTHep5SyBI1pN1F6ycwJdibvgVVm6urqEIvFdNBbSlqgszw33Q5SesXn/0vnKUeH8STisUeMxOIDiDfO4zM7pStCxr7iY43yfExzlpkNVGsE94nPC5dBcWm3sbQ2R4eUoFICykhCfP45U5/liI8vFl0dvC9KJLlGEH1aUv1TKnE0KudJxqu/vszSSWtiJSKaZdBVDpfpeiCkSuTv0tEqbRPuK+N8QKfqig+/cqTX3NyMAwcOWDIXpH3H89JOk9Po2Sa5OICUUGwfbSraS/Ezc0jWeEcxv0sy8X5lvlaiSEtiyc6QIro7A13aEtyXvinAmqLMhyhtJZ5L+o0Y4pCGPa8TX6mGUo7nZa4TV9SiV5wEii/hLR2m8W6B+IEGJbAMKXGgwH34kvQ06qW/K34/KeUSyVtIS2JxebTPP/88yS05MhEMBuH3+3vdJy2JVVBQAADYtWvXQW/wSEZjYyNKS0tRVVV10DSXRKCUQjAYTCghMy2JRRXF5dgMekf8PIHDQaIvctq7GwxSE4ZYBrYgLYnl8XiwePFiy2oLBl2RzOeUljnvBqmPtJRYBqkPQywDW2CIZWALDLEMbEFaEmv58uUoKyuD1+tFeXk53nvvvWQ3acCQiqU6u0PaEevpp5/GwoULsXjxYmzatAnjx4/HjBkzulSw+aYilUp19gqVZpg0aZK64YYb9PdoNKpKSkrU0qVLk9iq5ACAevbZZ/X3WCymiouL1X333ae31dfXK4/Ho5588kmllFKffPKJAqDef/99vc+aNWuUw+FQu3fv7re2pZXEikQi2Lhxo6UUpdPpxPTp03UpyiMZdpXqPBSkFbEOHDiAaDTaaynKIxl2leo8FKQVsQzSB2lFrMGDB8PlcvVaivJIhl2lOg8FaUWszMxMTJgwwVKKMhaLoaKiQpeiPJKRjFKdPaLfhgEDhKeeekp5PB61cuVK9cknn6j58+erQCCgqqurk920AUEwGFSVlZWqsrJSAVD333+/qqysVDt37lRKKXX33XerQCCgnn/+efXxxx+r733ve2rEiBGqpaVFn+Pcc89V3/rWt9SGDRvU22+/rcaMGaNmz57dr+1MO2IppdRDDz2khg0bpjIzM9WkSZPU+vXrk92kAcPatWsVgC6fuXPnKqU6XA633XabKioqUh6PR5111llqy5YtlnPU1NSo2bNnq9zcXOXz+dRVV12lgsFgv7bTpM0Y2IK0srEM0geGWAa2wBDLwBYYYhnYAkMsA1tgiGVgCwyxDGyBIZaBLTDEMrAFhlgGtsAQy8AWGGIZ2AJDLANbYIhlYAsMsQxsgSGWgS0wxDKwBYZYfcDKlSvhcDiwY8eOPh03bdo0nHDCCf3alrKyMlx55ZX9es7+hCGWgS0wxDKwBYZYBrbAEOsw8Pzzz+P8889HSUkJPB4PRo0ahZ///Oc9LnG7ceNGnHrqqcjKysKIESPw2GOPddknHA5j8eLFGD16NDweD0pLS3HzzTcjHA7bfTv9irRcmSJVsHLlSuTm5mLhwoXIzc3F66+/jttvvx2NjY247777LPvW1dXhu9/9Li699FLMnj0bq1atwnXXXYfMzEz84Ac/ANAxq/vCCy/E22+/jfnz52PcuHH497//jWXLluHzzz/vUmQtpdGvsxS/4XjiiScUALV9+3allFKhUKjLPtdcc43Kzs5Wra2tetvpp5+uAKhf//rXels4HFYnn3yyGjJkiIpEIkoppf70pz8pp9Op/vWvf1nO+dhjjykA6p133tHbhg8frieppiKMKjwMZGVl6f+5mvxpp52GUCiEzz77zLKv2+3GNddco79nZmbimmuuwb59+3QdhdWrV2PcuHE49thjceDAAf0588wzAQBr164dgLvqHxhVeBjYvHkzfvazn+H1119HY2Oj5beGhgbL95KSEuTk5Fi2jR07FgCwY8cOTJ48GVu3bsWnn36KwsLCbq+XTuUwDbEOEfX19Tj99NPh8/lw5513YtSoUfB6vdi0aRNuueWWPq2fTMRiMZx44om4//77u/29tLT0cJs9YDDEOkS88cYbqKmpwd///nd85zvf0du3b9/e7f579uzRK9kTXMizrKwMADBq1Ch89NFHOOuss/q0YnwqwthYhwi5mj0RiUTwyCOPdLt/e3s7VqxYYdl3xYoVKCwsxIQJEwAAl156KXbv3o3f/va3XY5vaWlBc3Nzf96CrTAS6xBx6qmnIj8/H3PnzsUPf/hDOBwO/OlPf+pxveSSkhLcc8892LFjB8aOHYunn34aH374IR5//HFkZGQAAK644gqsWrUK1157LdauXYtvf/vbiEaj+Oyzz7Bq1Sq8/PLLlqK0KY1kD0vTCfHuhnfeeUdNnjxZZWVlqZKSEnXzzTerl19+WQFQa9eu1cedfvrp6vjjj1cffPCBmjJlivJ6vWr48OHq4Ycf7nKNSCSi7rnnHnX88ccrj8ej8vPz1YQJE9SSJUtUQ0OD3i/V3Q2mPpaBLTA2loEtMMQysAWGWAa2IKnEOpJX8fqmI2nEOtJX8fqmI2mjwvLycpxyyil4+OGHAXSEM0pLS7FgwQLceuutyWiSQT8iKQ5SruK1aNEiva0vq3jFYjHs2bMHeXl5aR/6SCcopRAMBlFSUgKns3dllxRi9baKV3y6CdCRVSkzKHfv3o3jjjvO9nYadI+qqiocffTRve6TFqPCpUuXwu/3648hVXKRl5d30H2SQqy+ruK1aNEiNDQ06E9VVdVANdWgGyRifiSFWH1dxcvj8cDn81k+BimOZAUpD2cVr4aGhm4XKjKfgfnIYHhPSGp2w6Gu4mWIlfrESsvshsbGRvj9/mQ344hFQ0PDQc2RtBgVGqQfDLEMbIEhloEtMMQysAWGWAa2wBDLwBYYYhnYAkMsA1tgiGVgCwyxDGyBIZaBLTDEMrAFhlgGtsAQy8AWGGIZ2AJDLANbYIhlYAsMsQxsgSGWgS0wxDKwBYZYBrbAEMvAFhhiGdgCU+c9RVBWVga3u/vuqK2tRW1t7QC36DBxeHOZk4Nv2kzoadOmqZqamh7v9x//+IeaP3++cjgcSW8rEpwJbYiV5M/UqVPVjh07DnrPbW1t6h//+Ie64IILkt7mpBBr8eLFXRpyzDHH6N9bWlrU9ddfrwoKClROTo665JJLEioEIpGqxCotLVUTJ05Uo0aNSmj/yZMnq2Aw2Kd7b2lpUeedd96RSazjjz9eff311/qzf/9+/fu1116rSktLVUVFhfrggw/U5MmT1amnntqna6QisUaOHKlXQP3444/VySef3Ov+Z5xxhvr6668P6Rk3NDSomTNnHnnEGj9+fLe/1dfXq4yMDLV69Wq97dNPP1UA1Lp16xK+RioS68ILL7S08fPPP1dDhgzpcf977rnnkJ4vsXfvXnX++eenLLFscTds3boVJSUlGDlyJObMmYNdu3YB6FjFva2tDdOnT9f7HnvssRg2bFivRW3D4TAaGxstn1RCdnY2nnzyScu2MWPGwOPx2HbNIUOG4LnnnsOQIUNsu8bhoN+JVV5ejpUrV+Kll17Co48+iu3bt+O0005DMBhEdXU1MjMzEQgELMcUFRWhurq6x3PG1yBNtZVG58+fD6/XO+DXdbvdKVs1ut/9WOedd57+/6STTkJ5eTmGDx+OVatWWRbn7gsWLVqEhQsX6u+NjY0pQS632438/HxcfvnlBy1PbRcCgUCXWq6pANufRiAQwNixY7Ft2zYUFxcjEomgvr7esk9PRW2JVKxBOnXqVNxyyy3Yu3cvysvLu/z+3nvvIRQK2d6OZ5991vZrHAps97w3NTXhiy++wBVXXIEJEyYgIyMDFRUVmDVrFgBgy5Yt2LVrV7dFbVMN8+bNw6mnngoAOPvss3uVmo899hhqampsb9MRowpvuukmzJw5E8OHD8eePXuwePFiuFwuzJ49G36/H/PmzcPChQtRUFAAn8+HBQsWYMqUKZg8eXJ/N+WQUFZWptVaeXk57rnnHv3boEGDkJ2dfdBz/OMf/8CqVatsa2M6oN+J9dVXX2H27NmoqalBYWEhpk6divXr16OwsBAAsGzZMjidTsyaNQvhcBgzZszocYHugcbMmTOxevXqwx7NhUKhtFoY3BYcljMlSbDLj/XCCy8opZSKxWIqGo3qT01Njbr44ovVxRdfrA4cOKC394Sqqip11lln9Xqtw/VjEZs3b05JP5bJbhD4/ve/jxdffBFPP/00/vjHP+rtSik0NDQAAF5//XU4HA64XC4899xzGDVqFIYOHWo5z9FHH41hw4YNSJsvvPDCAblOn9Evr80AI5U872eccYaqqqrq0sZnnnlG5eXl2S6xioqKUlJimUS/w8TatWvxH//xH11cC7NmzcLKlSttvXYkEoFK0TL9hlj9gPXr1+Oyyy7rsv3kk0+29brz589P2RVpDbH6CZFIpMu2FStW9Lj/iy++iK+//vqQr7dx40Zs2LDhkI+3G4ZY/YS2tja0trYC6CDZokWLsGzZsh73f/PNN7Ft27ZDulZDQwPWrVvX7aKhKYN+sSAHGKlkvMvPlVdeqfbt26d+/OMfJ7T/oEGDenVbdIfKykpVUlKS1DRlk5qchE9fUoc9Ho+6+uqr1ZtvvpnQfX/00UfquOOOS/o9mtW/0gSDBg3CCy+8gKFDh2Lw4MHIzs5GMBhEXV0dAOB3v/sd/vSnPyEUCqWEsZ7I6l+GWCmGa665BhMnTsQrr7yC1atXJ7s53cIQy8AWmPUKDZIGQywDW2CIZWALDLEMbIEhloEtMMQysAWGWAa2wBDLwBYYYhnYAkMsA1tgiGVgCwyxDGyBIZaBLTDEMrAFhlgGtqDPxHrrrbcwc+ZMlJSUwOFw4LnnnrP8rpTC7bffjqFDhyIrKwvTp0/H1q1bLfvU1tZizpw58Pl8CAQCmDdvHpqamg7rRgxSC30mVnNzM8aPH4/ly5d3+/u9996LBx98EI899hg2bNiAnJwczJgxQ89gAYA5c+Zg8+bNePXVV/HPf/4Tb731FubPn3/od2GQejjkGQ0dmafq2Wef1d9jsZgqLi5W9913n95WX1+vPB6PevLJJ5VSSn3yyScKgHr//ff1PmvWrFEOh0Pt3r07oeum8mSKI+Ez4FPst2/fjurqakvxWr/fj/Lycl28dt26dQgEApg4caLeZ/r06XA6nT1OwEz14rYGXdGvxGKB2qKiIst2Wby2urq6S6Vft9uNgoKCHgvcpnpxW4OuSItR4aJFi9DQ0KA/VVVVyW6SwUHQr8Rigdr4Kr6yeG1xcXGXuXHt7e2ora3tscBtKha3Negd/UqsESNGoLi4GBUVFXpbY2MjNmzYoIvXTpkyBfX19di4caPe5/XXX0csFuu2+rBBmiLBAaBGMBhUlZWVqrKyUgFQ999/v6qsrFQ7d+5USil19913q0AgoJ5//nn18ccfq+9973tqxIgRqqWlRZ/j3HPPVd/61rfUhg0b1Ntvv63GjBmjZs+enXAbzKgw9UeFfSbW2rVru73Y3LlzlVIdLofbbrtNFRUVKY/Ho8466yy1ZcsWyzlqamrU7NmzVW5urvL5fOqqq67q0ypYhlipTywzE9qgzzAzoQ2SBkMsA1tgiGVgCwyxDGyBIZaBLTDEMrAFhlgGtsAQy8AWGGIZ2AJDLANbYIhlYAsMsQxsQVoSKw3j5t8oJPL805JYA7E6vEHPCAaDB90nLZfuLSgoAADs2rXLpM/0gsbGRpSWlqKqqqpf0rmVUggGgygpKTnovmlJLKezQ9D6/X6T/54A+nOeQKIvclqqQoPUhyGWgS1IS2J5PB4sXrwYHo8n2U1JaSTzOaVlzrtB6iMtJZZB6sMQy8AWGGIZ2AJDLANbkJbEWr58OcrKyuD1elFeXo733nsv2U0aMKRLqc60I9bTTz+NhQsXYvHixdi0aRPGjx+PGTNmpMTq7gOBtCnVmXDBhBTBpEmT1A033KC/R6NRVVJSopYuXZrEViUHQHJKdSaCtJJYkUgEGzdutJSidDqdmD59ui5FeSTDrlKdh4K0ItaBAwcQjUZ7LUV5JMOuUp2HgrQilkH6IK2INXjwYLhcrl5LUR7JsKtU56EgrYiVmZmJCRMmWEpRxmIxVFRU6FKURzJSqlRnvw0DBghPPfWU8ng8auXKleqTTz5R8+fPV4FAQFVXVye7aQOCVCjVmQjSjlhKKfXQQw+pYcOGqczMTDVp0iS1fv36ZDdpwJAKpToTgUmbMbAFaWVjGaQPDLEMbIEhloEtMMQysAWGWAa2wBDLwBYYYhnYAkMsA1tgiGVgCwyxDGyBIZaBLTDEMrAFhlgGtsAQy8AWGGIZ2AJDLANbYIhlYAsMsQxsgSGWzXjkkUewcuXKAbnWJ598gjvuuAM7duwYkOv1BkMsmzHQxFqyZIkhlsE3GP065+cbgk2bNqlzzz1X5eXlqZycHHXmmWeqdevW6d8XL16sunt0TzzxhAKgtm/frpRSavjw4V2maZ1++umWfd988001f/58VVBQoPLy8tQVV1yhamtrLecFoBYvXtzlesOHD9fTvni++M/atWv745H0GWm5MoWd2Lx5M0477TT4fD7cfPPNyMjIwIoVKzBt2jS8+eabfZot/MADD2DBggXIzc3F//t//w9A14IdN954IwKBAO644w5s2bIFjz76KHbu3Ik33ngDDocj4Wt95zvfwQ9/+EM8+OCD+J//+R+MGzcOAPTfAUdS6JzCuOiii1RmZqb64osv9LY9e/aovLw89Z3vfEcplbjEUkqp448/Xkup7vadMGGCikQievu9996rAKjnn39eb0MCEksppVavXp1UKSVhbCyBaDSKV155BRdddBFGjhyptw8dOhTf//738fbbb6OxsbFfrzl//nxkZGTo79dddx3cbjdefPHFfr3OQMMQS2D//v0IhUI45phjuvw2btw4xGIxVFVV9es1x4wZY/mem5uLoUOHpsTI7nBgiHUI6Mn2iUajA9qOgb5eX2CIJVBYWIjs7Gxs2bKly2+fffYZnE4nSktLkZ+fDwCor6+37LNz584uxx3MAI+vaNzU1ISvv/4aZWVlelt+fn6Xa0UiEXz99dd9utZAwhBLwOVy4ZxzzsHzzz9vUUV79+7FX//6V0ydOhU+nw+jRo0C0FEam2hubsYf/vCHLufMycnpQgqJxx9/HG1tbfr7o48+ivb2dpx33nl626hRoyzX4nHxEisnJwdAV8InA8bdEIe77roLr776KqZOnYrrr78ebrcbK1asQDgcxr333gsAOOecczBs2DDMmzcPP/3pT+FyufC///u/KCwsxK5duyznmzBhAh599FHcddddGD16NIYMGYIzzzxT/x6JRHDWWWfh0ksvxZYtW/DII49g6tSpuPDCC/U+//Vf/4Vrr70Ws2bNwtlnn42PPvoIL7/8MgYPHmy51sknnwyXy4V77rkHDQ0N8Hg8OPPMM7vUHB0QJHtYmorYtGmTmjFjhsrNzVXZ2dnqjDPOUO+++65ln40bN6ry8nKVmZmphg0bpu6///5u3Q3V1dXq/PPPV3l5eb06SPPz81Vubq6aM2eOqqmpsVwrGo2qW265RQ0ePFhlZ2erGTNmqG3btnVxNyil1G9/+1s1cuRI5XK5kup6MPWxkoSVK1fiqquuwvvvv28pjf1NgbGxDGyBIZaBLTDEMrAFSSXWkbyK15VXXgml1DfSvgKSSKwjfRWvbzqSNiosLy/HKaecgocffhhAx0IApaWlWLBgAW699dZkNMmgH5EUBylX8Vq0aJHe1pdVvGKxGPbs2YO8vLyUCmN806GUQjAYRElJCZzO3pVdUojV2ypen332WZf9w+EwwuGw/r57924cd9xxtrfToHtUVVXh6KOP7nWftBgVLl26FH6/X38MqZKLvLy8g+6TFGL1dRWvRYsWoaGhQX/6OyfKoG9IxPxICrH6uoqXx+OBz+ezfAxSHEmJUKrDW8WroaGh2xkp5jMwn4aGhoP2UVKzGw51FS9DrNQnVlpmNzQ2NsLv9ye7GUcsGhoaDmqOpMWo0CD9YIhlYAsMsQxsgSGWgS0wxDKwBYZYBhrZ2dm49NJL++VchlgGGpFIpN+SLQ2xDDTa29v7rWaEIZaBLTDEMugVS5YsQW5ubp+PMyEdgz7DhHQMkgZDLINuMXz48MM63hDLoFtccMEFh3W8sbEM+gxjYxkkDYZYBrbAEMtAIysrq9/OZYhlAAA49thjsXTp0n47nyGWAYCOuZ533nlnv53PFLc9wuFwOOD3+/H222/363mNxDrCkZWVhddeew3HHntsv57XEOsIRzgcxmuvvWapK98fMA7SFEFZWRncbjeWLFmCYcOG4aqrrgIAtLa24quvvrL12h6PB263G83NzQntn4iDNC3rvH/TZkJPmzatS213Ytu2bWrixIlJb6P8pPwU+0PFN4VYubm56vnnn1c7duzo9X4//fRTNXbs2KS3N6nE4iKR8nPMMcfo31taWtT111+vCgoKVE5OjrrkkksSKgQikYrE8vl8auLEierkk09O+JhTTz1VxWKxhO5579696phjjkmJtieNWMcff7z6+uuv9Wf//v3692uvvVaVlpaqiooK9cEHH6jJkyerU089tU/XSEVinXPOOUoppZqbm9XFF1+c0DEffvhhwvfc2tqq/vM//zMl2p40Yo0fP77b3+rr61VGRoZavXq13vbpp58qAJbFvA+GVCOW1+tVlZWVun379+9X559/fq/HZGRkqI8//rjHe3zyySfVv/71L9Xc3KwikYhqb29Xv/rVr5Tb7e7Xtns8HkuVnz/84Q+pS6zs7Gw1dOhQNWLECPX9739f7dy5UymlVEVFhQKg6urqLMdwkaOe0NraqhoaGvSnqqoq6WSSn+zsbMu6zkop9etf/1p5vd4ej/nlL3/ZoxoMBoPq6quvVpMmTVJer1fNmzdPVVRUqGg0qn7zm9+orKysXtvjcrnU2Wefrc455xx1zjnnqBNOOKHX/cvLy9XLL7+sVq1alRBxk0KsF198Ua1atUp99NFH6qWXXlJTpkxRw4YNU42Njeovf/mLyszM7HLMKaecom6++eYez9md3ZZKnx/96EcqGo12aXdpaWmPx9xzzz093u/+/fvVzJkz1UUXXaQ72u/3q/b2dvW3v/1NBQKBXtvzk5/8xELaTz75pF9HlikxKqyrq1M+n0/97ne/O2RiparEcrvdqrCwsMeCcb0R67bbblPt7e093vOvfvUry/5Op1Mdf/zxqqCgoNc2XXfddSoUCnU53w033KCcTueAEct2z3sgEMDYsWOxbds2FBcXIxKJdFkBtKeitkSq1SAdPXo0LrvsMtxyyy3Yu3cvysvLu+zz3nvvIRQK9XiOn//85/i///u/bn979tln8eGHH1q2xWIxbN68GbW1tb22rbi4uNv0l4ceeqhL+XM7YXsQuqmpCV988QWuuOIKTJgwARkZGaioqMCsWbMAAFu2bMGuXbu6LWqbSpg8eTKuvvpqAMBxxx2HyZMn97jve++9hzlz5qCmpuaQrvX555/jz3/+8yEd2xMGeqGFfifWTTfdhJkzZ2L48OHYs2cPFi9eDJfLhdmzZ8Pv92PevHlYuHAhCgoK4PP5sGDBAkyZMqXXjhpIlJWVWVZdWLBgAWbNmoWcnBwUFBSgra0NVVVVCIVCyM7O7vYc27dvx7Zt2w7p+kopvPzyy4d0LADU1tYiHA7D4/FYtv/4xz/uUv7cTvQ7sb766ivMnj0bNTU1KCwsxNSpU7F+/XoUFhYCAJYtWwan04lZs2YhHA5jxowZeOSRR/q7GYeEmTNnYvXq1V06RaKurg733Xcfrr/+epx44on9du2mpiZs3LgRp59+On7zm9/gpJNOOqTz/OY3v0FmZiZGjhxp2b5u3TrEYrH+aGpiOKgVloKwy4/1wgsvKKU61mDm57e//a26+OKL9eeGG27Qv/WEp556KqHrSQdpKBRSmzZt0vc3d+7cLvtPnDhRzZgxI+mDlpQYFdoBu4jl9/vV448/rgKBgP54PJ4uo7NAIKAGDRqk/vWvf6k9e/Z0ad/LL7+sBg0a1CdixeOPf/yjXqCcn4yMDDV69OgubTLE6iekkuf9jDPOUFVVVV3a+Mwzz3QhhvxMmzatW1JKrFixoovDcvny5Wr06NGGWHYglYgFQE2ePFk1Nzd3aeff/va3Ho8ZOXKk2rZtW6/3GQ6HVVZWlnK73WrQoEHqlVdeUTt37kwLYpkM0n7A+vXrcdlll3XZ3pux/OWXX6KpqanX8zocDkybNg2///3vsXv3boTDYTzwwAO9+sdSBWYyRT8hEol02bZw4cLDOmdGRgZefPFF/T0Wi2HZsmWHdc6BgiGWTbjjjjtQXV3d6z6NjY0JnaulpQVXXnllei3GfsiGThKRajYWADV+/Hj1zDPPqH379qlnnnlGnXHGGQc9ZtCgQb26LZTqCEjPmjUr6fcnP8Z4T8LnggsuSHhfj8ejHn300R7vs62tLeVIZYiVJp/s7GxVVlam1q9fb5lQ0djYqGbNmqUcDkfS23goxDLTv1IIU6dOxdy5cwEAr7zyClavXp3kFnWPRKZ/GWIZ9Bmm8JpB0mCIZWALDLEMbIEhloEtMMQysAWGWAa2wBDLwBYYYhnYAkMsA1tgiGVgCwyx0hA/+9nPkt2Eg8IQK81QXFyM7373u8luxkFhiJVmuO2227Bhw4ZkN+OgMKnJaYbly5f320rzdsJIrDRDKBQa2Knyh4g+E+utt97CzJkzUVJSAofDgeeee87yu1IKt99+O4YOHYqsrCxMnz4dW7dutexTW1uLOXPmwOfzIRAIYN68eQedCmXQgXHjxvXrKl12oc/Eam5uxvjx47F8+fJuf7/33nvx4IMP4rHHHsOGDRuQk5ODGTNmoLW1Ve8zZ84cbN68Ga+++ir++c9/4q233sL8+fMP/S6OIKxZswZ1dXXJbsbBcTi55wDUs88+q7/HYjFVXFys7rvvPr2tvr5eeTwe9eSTTyqlOsoWAlDvv/++3mfNmjXK4XCo3bt3J3Tdb1LOezp+Bnwm9Pbt21FdXY3p06frbX6/H+Xl5Vi3bh2AjnI6gUAAEydO1PtMnz4dTqezx9FOOBxGY2Oj5WOQ2uhXYnGCZnxJwqKiIv1bdXU1hgwZYvnd7XajoKCgxwmeS5cuhd/v15/S0tL+bLaBDUiLUeGiRYvQ0NCgP1VVVcluksFB0K/EYoHa+JKEsnhtcXEx9u3bZ/m9vb0dtbW1PRa4TbXitgYHR78Sa8SIESguLkZFRYXe1tjYiA0bNujitVOmTEF9fT02btyo93n99dcRi8W6rT5skKZIcACoEQwGVWVlpaqsrFQA1P33368qKyv16hN33323CgQC6vnnn1cff/yx+t73vqdGjBihWlpa9DnOPfdc9a1vfUtt2LBBvf3222rMmDFq9uzZCbfBjApTf1TYZ2KtXbu224vNnTtXKdXhcrjttttUUVGR8ng86qyzzlJbtmyxnKOmpkbNnj1b5ebmKp/Pp6666ioVDAYTboMhVuoTy8yENugzzExog6TBEMvAFhhiGdgCQywDW2CIZWALDLEMbIEhloEtMMQysAWGWAa2wBDLwBYYYhnYAkMsA1uQlsRKw7j5NwqJPP+0JNahrg5v0D8IBoMH3Sctp9gXFBQAAHbt2mXSZ3pBY2MjSktLUVVV1S/p3EopBINBlJSUHHTftCSW09khaP1+v8l/TwD9OU8g0Rc5LVWhQerDEMvAFqQlsTweDxYvXgyPx5PspqQ0kvmc0jLn3SD1kZYSyyD1YYhlYAsMsQxsgSGWgS1IS2ItX74cZWVl8Hq9KC8vx3vvvZfsJg0Y0qVUZ9oR6+mnn8bChQuxePFibNq0CePHj8eMGTO6VLD5piJtSnUmXDAhRTBp0iR1ww036O/RaFSVlJSopUuXJrFVyQGQnFKdiSCtJFYkEsHGjRstpSidTiemT5+uS1EeybCrVOehIK2IdeDAAUSj0V5LUR7JsKtU56EgrYhlkD5IK2INHjwYLper11KURzLsKtV5KEgrYmVmZmLChAmWUpSxWAwVFRW6FOWRjJQq1dlvw4ABwlNPPaU8Ho9auXKl+uSTT9T8+fNVIBBQ1dXVyW7agCAVSnUmgrQjllJKPfTQQ2rYsGEqMzNTTZo0Sa1fvz7ZTRowpEKpzkRg0mYMbEFa2VgG6QNDLANbYIhlYAsMsQxsgSGWgS0wxDKwBYZYBrbAEMvAFhhiGdgCQywDW2CIZWALDLEMbMH/B0prJ0RdeoG9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best IoU: 0.4236, Best Dice: 0.5625, Best Acc: 0.9506\n"
     ]
    }
   ],
   "source": [
    "# -----------------------------\n",
    "# Threshold method application\n",
    "# -----------------------------\n",
    "\n",
    "# empty the cache for model and training process\n",
    "torch.cuda.empty_cache()\n",
    "\n",
    "# Do the segmentation to exteract the spine part\n",
    "# Threshold segmentation hyper parameters\n",
    "minThreshold = 250\n",
    "maxThreshold = 1500\n",
    "\n",
    "# the variables for evaluation\n",
    "total_correct_pixels = 0\n",
    "total_pixels = 0\n",
    "total_iou = 0.0\n",
    "total_dice = 0.0\n",
    "\n",
    "# set the hyper parameters and the paths 设置超参数和路径\n",
    "data_path = \"./dataset/Spineweb_dataset15/train/\"    # the path of the dataset\n",
    "file_list = os.listdir(data_path)  # list all files and folders in the folder(data path)\n",
    "file_count = len(file_list)  # get the numer of files\n",
    "print(\"Length of the dataset:\",file_count)\n",
    "resize_scale = (128,128)    # set the resize scale for images\n",
    "print(\"----------------  Loading the dataset and evaluate  ----------------\")\n",
    "data_count = 0\n",
    "poster_threshold = 200\n",
    "for index in range(file_count):     # iterate the whole dataset\n",
    "    metadata = np.load(data_path+str(index)+\".npy\")     # load the data\n",
    "    image = np.array(Image.fromarray(metadata[0]).resize(resize_scale))     # Using the PIL to resize the transfer into npy again\n",
    "    label = Image.fromarray((metadata[1] * 255).astype('uint8')).resize(resize_scale)     # Using the PIL to resize the transfer into npy again\n",
    "    label = np.array(label)\n",
    "    label[label < poster_threshold] = 0\n",
    "    label[label >= poster_threshold] = 1\n",
    "    \n",
    "    # print(type(image),image.shape,image)\n",
    "    # print(type(label),label.shape,label)\n",
    "    output = CTimageLIB.thresholdSegmentation(image,minThreshold,maxThreshold)\n",
    "    # evaluations\n",
    "    current_correct_pixels,current_pixels = calculate_pixel_accuracy(output,label)\n",
    "    current_iou = calculate_iou(output,label)\n",
    "    current_dice = calculate_dice(output,label)\n",
    "    # add the results to total one\n",
    "    total_correct_pixels += current_correct_pixels\n",
    "    total_pixels += current_pixels\n",
    "    total_iou += current_iou\n",
    "    total_dice += current_dice\n",
    "    data_count += 1\n",
    "    # visualize the results\n",
    "    if index == 100:\n",
    "        visualize_results(image,label,output)\n",
    "\n",
    "final_acc = total_correct_pixels/total_pixels\n",
    "final_iou = total_iou/data_count\n",
    "final_dice = total_dice/data_count\n",
    "\n",
    "print(f\"Best IoU: {final_iou:.4f}, Best Dice: {final_dice:.4f}, Best Acc: {final_acc:.4f}\")\n"
   ]
  }
 ],
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   "display_name": "dataEngineering",
   "language": "python",
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    "name": "ipython",
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