15_2_contours.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37714aaa-3e6f-48f3-b3c4-99dd473cf5d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# exibir imagens em grid\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "def showImages(imgsArray, titlesArray, size, grid=(1,1)):\n",
    "    y, x = grid\n",
    "    fig, axes = plt.subplots(y, x, figsize=size)\n",
    "    axes = axes.ravel() if isinstance(axes, np.ndarray) else np.array([axes])\n",
    "\n",
    "    if len(imgsArray) != len(titlesArray):\n",
    "        print(\"ERRO: O n&uacute;mero de imagens e t&iacute;tulos deve ser o mesmo!\")\n",
    "        return\n",
    "\n",
    "    for idx, (img, title) in enumerate(zip(imgsArray, titlesArray)):\n",
    "        if len(img.shape) == 2:  # A imagem &eacute; tons de cinza\n",
    "            axes[idx].imshow(img, cmap='gray')\n",
    "        else:  # A imagem &eacute; RGB\n",
    "            axes[idx].imshow(img)\n",
    "        axes[idx].set_title(title, fontdict={'fontsize': 18, 'fontweight': 'medium'}, pad=10)\n",
    "        if len(title) == 0:\n",
    "            axes[idx].axis('off')\n",
    "\n",
    "    plt.tight_layout()  # ajusta automaticamente o layout para evitar sobreposi&ccedil;&atilde;o\n",
    "    plt.show()\n",
    "    \n",
    "#Para exibir uma &uacute;nica imagem:\n",
    "#showImages([img1], [\"T&iacute;tulo 1\"], size=(10,10), grid=(1,1))\n",
    "\n",
    "#Para exibir duas imagens lado a lado:\n",
    "#showImages([img1, img2], [\"T&iacute;tulo 1\", \"T&iacute;tulo 2\"], size=(10,10), grid=(1,2))\n",
    "\n",
    "#Para exibir quatro imagens em uma grade 2x2:\n",
    "#showImages([img1, img2, img3, img4], [\"T&iacute;tulo 1\", \"T&iacute;tulo 2\", \"T&iacute;tulo 3\", \"T&iacute;tulo 4\"], size=(10,10), grid=(2,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc2c4ddd-4560-4177-a135-6d2868fdeb4a",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/datasets/vishalsubbiah/pokemon-images-and-types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1509b04b-37e3-45f7-9c4a-c27b2a1f9ccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 900x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FOLDER = 'pokemon_dataset/images/'\n",
    "POKEMON = 'abra'\n",
    "\n",
    "img = cv2.imread(FOLDER + POKEMON + '.png')\n",
    "assert img is not None, \"file could not be read, check with os.path.exists()\" # Verifica se a imagem foi carregada corretamente\n",
    "img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Converte a imagem de BGR para escala de cinza\n",
    "ret, thresh = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # Aplica um threshold para binarizar a imagem\n",
    "\n",
    "contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "# Criar uma imagem em branco (preta)\n",
    "img_contours = np.zeros_like(img)\n",
    "\n",
    "# Desenhar os contornos na imagem em branco\n",
    "cv2.drawContours(img_contours, contours, -1, (255, 0, 0), 1)\n",
    "\n",
    "showImages([img_gray, thresh, img_contours], [\"Original\", \"Binariza&ccedil;&atilde;o\", \"Contorno\"], size=(9,7), grid=(1,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0407e6ba-e0d4-4792-acef-ed50f5eb02eb",
   "metadata": {},
   "source": [
    "# Momentos de Imagens\n",
    "\n",
    "Os momentos de uma imagem s&atilde;o caracter&iacute;sticas num&eacute;ricas que descrevem propriedades da forma ou da distribui&ccedil;&atilde;o de intensidade dos pixels na imagem.\n",
    "\n",
    "- Analogias:\n",
    "   - Centro de Massa: Pense nos momentos como uma maneira de encontrar o \"centro de massa\" de uma forma em uma imagem, assim como fazemos na f&iacute;sica para objetos s&oacute;lidos.\n",
    "   - Assinatura da Forma: Assim como sua assinatura identifica voc&ecirc;, os momentos podem identificar e descrever a forma de objetos em uma imagem.\n",
    "     \n",
    "- Para que servem?\n",
    "    - Identifica&ccedil;&atilde;o de Formas: Os momentos podem ser usados para reconhecer e diferenciar entre diferentes formas em uma imagem.\n",
    "    - An&aacute;lise de Movimento: Eles ajudam a rastrear a posi&ccedil;&atilde;o e a orienta&ccedil;&atilde;o de um objeto em movimento.\n",
    "    - Descri&ccedil;&atilde;o de Propriedades: Momentos podem descrever propriedades como &aacute;rea, centroide (o \"ponto m&eacute;dio\" da forma), e outras caracter&iacute;sticas importantes da forma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf6d732e-8797-4591-a7a4-5d2c9ff79e18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "&Aacute;rea: 303705.0\n",
      "Centroide: (58.733837111670866, 62.54911838790932)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAACCoAAALQCAYAAABclgNIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB0OUlEQVR4nOzdeZxWdd0//vcFg8CwDAOyKgglIiqJSyA3llq46y0uuWR3Un61zMo10nIty63SW9MWvW/Tcs0Sl1zDrRBTEfeVRAGVUYGZYROBOb8//DG3eM7oNczMuWaG5/PxOI9HvebzOed9nWuGeTO+OVNIkiQJAAAAAAAAAIAcdCh1AQAAAAAAAADA+sOgAgAAAAAAAACQG4MKAAAAAAAAAEBuDCoAAAAAAAAAALkxqAAAAAAAAAAA5MagAgAAAAAAAACQG4MKAAAAAAAAAEBuDCoAAAAAAAAAALkxqAAAAAAAAAAA5MagAkDOJk2aFIVCISZNmtTs5955552jUCjEWWed1eznboqWfM0AAOuTP/zhD1EoFGLo0KGlLmWdDR06NAqFQvzhD38odSlN8t5778Vee+0V3bp1i8985jNx5plnRlVVVfz2t7+NQqEQzz77bKlLBAAAgFbLoALA/+/999+P3/3ud7HPPvvEkCFDomvXrlFRUREjR46Mo48+Oh544IFSlwgAQBt31llnRaFQyDzKy8tj+PDhccQRR8QjjzxS6lL5FOedd17cddddUVZWFq+//nr85Cc/iQEDBsQxxxwTW265ZYwcObLUJQIAtAmrV6+Om266Kb7+9a/HZpttFr169YoNNtgg+vXrFzvuuGOceuqp8dxzz5W6zHqvv/56nHXWWa3uH4sBtDVlpS4AoDW477774pvf/GbMmzevPuvZs2esWLEiXnrppXjppZfiiiuuiD333DP++Mc/Rp8+fdb5WgMHDowRI0bEwIEDm6P0tQwZMiRGjBgRG264YbOfGwCA5tW/f//6/11XVxcLFy6MWbNmxaxZs+Kaa66JM888M/XDz4qKihgxYkRstNFGOVfbfD772c9Gly5doqKiotSlNMnIkSPjZz/7WfzoRz+K+fPnxz333BMvv/xy9O3bNyZNmhRlZX7kAgDwaR599NE44ogj4pVXXqnPOnXqFD169IgFCxbEtGnTYtq0aXHeeefFAQccENdff31ssMEGJaz4w0GFs88+OyLCsAJAExSSJElKXQRAKd14443xta99LVatWhUbbbRRnH322XHAAQdEZWVlRES89NJL8bvf/S5+/etfx6pVq2LTTTeNadOmRb9+/UpcedsxadKkuPrqq+OII45o84/4BQBoirPOOqv+h5of/+v46tWr49FHH43jjjsuZsyYERER06ZNi//4j//IvU4AAGhpt99+e3zlK1+JFStWRJ8+feLkk0+OAw88MIYPHx4RH/bHM2fOjL/85S9x+eWXR21tbSxatCh69epV0roffPDB2GWXXSIi3dMDUDy/+gFYr7344ovxzW9+M1atWhWjRo2KmTNnxpFHHlk/pBARsfnmm8dFF10Ut956a2ywwQYxa9as+OpXv1rCqgEAaI86duwY48ePjylTptRnt956a+kKAgCAFvLqq6/G1772tVixYkVsscUW8dRTT8Upp5xSP6QQ8WF/vP3228e5554bs2fPjv3226+EFQPQ3AwqAOu10047LZYtWxadO3eOP//5z9G3b98G1+61115x2mmnRUTE1KlT429/+1v9x15//fX63y38+uuvx7///e84+uijY9iwYdG5c+cYOnRo/dpJkyZFoVCISZMmZV4nSZK46qqrYty4cdGjR4+oqKiIsWPHxu9///tIkuQT9++8885RKBQyHzk2dOjQKBQK8Yc//CE++OCDuPDCC2PrrbeObt26RUVFRXzpS1+Ku+++u8HXP3v27Dj//PNjjz32iM022yy6desW3bt3jy222CKOP/74mDNnToN7AQAo3sYbb1z/q8aWLFmy1sf+8Ic/RKFQWKu/XOOss86KQqEQO++8c0R82LPuvffe0bdv3+jSpUuMHDkyzj777Hj//fczr7ts2bK4/vrr4+tf/3qMHj06+vbtG507d45BgwbFxIkT46677mqw5o/X9cADD8TEiRNj4MCB0bFjx7V614/2pR+1ppf9tOPjr33RokXxP//zP3HwwQfHqFGjonfv3tGlS5fYZJNN4qtf/Wo8+uijDda9xtKlS+NXv/pV7LTTTrHhhhvGBhtsEBtvvHHstNNO8ctf/jKqqqrWWl9dXZ15zSFDhsRhhx0WjzzyyKde89///nccc8wxMXz48OjatWv07Nkztt122/jJT34StbW1n7ofAKAtO+2006K2tja6dOkSt9xyS2y88cafuL53794xZcqU1K8Pmz9/fvzgBz+ILbfcMrp16xbdunWLLbfcMiZPnpzq4db4+M9yq6qq4rjjjothw4ZFly5don///nHooYfGSy+9lNo7dOjQ+qcpRESqV836me269H0PPvhg/TkjImbOnBmHH354bLzxxtGpU6f6nv/jffiMGTPi4IMPjoEDB0bnzp3jM5/5TJx44omxaNGiT7y/elOgJBKA9dRbb72VdOjQIYmIZNKkSUXtWbx4cdKjR48kIpI999yzPp89e3YSEUlEJNdee23SvXv3JCKS8vLypFu3bskmm2xSv/aII45IIiI54ogjUudftWpVcsghh9Sfq1AoJJWVlfV1HnbYYZ+4f6eddkoiIjnzzDNTH9tkk02SiEguvfTSZOzYsUlEJJ06daqvdc31/ud//ifzta85d0QkG2ywQdKnT5/6uiIiqaioSP7xj39k7v2kmgEA1idnnnlmff/UkHnz5tWv+e///u+1PnbVVVclEbFWf/nxc++0007JBRdckBQKhaRQKCS9evVKCoVC/Tl32WWXZNWqVan9a869pi+sqKhIysvL67OISE466aTMmj9a18UXX1x/vYqKiqRTp05r9YFr+tKrrrpqrXPsv//+Sf/+/Rs8OnfunPnaP3pPO3bsmFRWVtavXfNaPn4fP2rGjBnJ4MGD69d36NAh6d2791rnuOiii9ba89Of/vRTr/mrX/2qwWveeOONa63v0aPHWv9/8ODByQsvvNDgfgCAtmz+/Pn1P1c88sgj1/k8Dz74YNKrV6/6Hqpbt25Jt27d6v9/ZWVl5s8rP/qz3DvuuCPp169f/c9yP9qT9ezZM3nqqafW2rv99tsnlZWV9Ws+3rN+//vfX2v9uvZ9DzzwQP2am2++OenUqVN9TV26dEl22mmnJEnW7sOvvfba+nUVFRVr/ex2yy23TBYvXpx5H/WmQKl4ogKw3nrwwQejrq4uIiIOPPDAovZ07949dtttt4iI+Mc//hGrVq1KrfnWt74VW265ZTz++OOxdOnSWLJkSdx7771Fnf/CCy+MG2+8MSIiTjzxxHj33Xdj4cKFsWjRovj5z38eN9xwQ9x2221FnashZ5xxRsybNy+mTJkSS5cujcWLF8dLL70UO+ywQyRJEscdd1zU1NSk9o0ePTouu+yyeOWVV2L58uXx3nvvxYoVK+Jf//pX7LHHHlFTUxOHHHJILF++vEn1AQCsr1avXh3Tp0+P/fffPyIi+vXrF1//+tcbfZ6nn346TjnllDjllFPinXfeiUWLFkV1dXWcccYZEfHh0w6uvvrq1L7Kyso4+eST45///GcsWbIkqqurY+nSpfHWW2/F2WefHZ06dYpf/vKXn9iPVlVVxUknnRRHHHFEzJkzJ6qrq2P58uVx+umnf2rdf/3rX2P+/PmZx5133hllZWUREbH33nuvtW/QoEFx5plnxhNPPBHLli2LhQsXxvLly+O1116L4447LiI+7K1nzpyZuubcuXNj9913j7lz58bgwYPjhhtuiMWLF8eCBQti+fLl8fzzz8dZZ52VevLawIED44wzzvjEa5588skxY8aM1DWffPLJ+sccjx8/Pp555pmora2NZcuWxW233RYDBw6MuXPnxr777pt6ogYAQHvwwAMP1P9cdk3v21hz586NiRMnRnV1dWyxxRb1PeySJUvi4YcfjhEjRsSiRYtiv/32izfffLPB8/zXf/1XDB8+fK2f5d53330xcODAqK2tje9973trrX/88cfjr3/9a/3//3jf+t///d/1H2uuvm/SpEmx6667xosvvhg1NTWxfPnyuOKKK9Za8+6778Y3v/nNtfrwxYsXx69//evo1KlTPP/883HBBRekzq03BUqq1JMSAKXy4x//uH4qdN68eUXv++i/npo1a1aSJGtP4W6yySYNTqcmScNPF1iyZEnSs2fPT5wk/ui/FlvXJyp07tw5efHFF1Mff+edd5IuXbokEZH86U9/avgGZFi1alXyuc99LomI5I9//GPq456oAADwoY/2cx/9l1d9+/ZNOnbsWP+vpA4//PDk9ddfT+0v5okKDfWDSZIkBxxwQBIRyYQJExpd+4UXXphERPLlL3+5wboiIjnggAM+8TwNPVGhIfPmzUsGDRpUX/fKlSsbVfexxx7bYI/9ta99LYmIpE+fPsmcOXMadd5irvnNb34z9bE99tgjiYhk0003TZYuXZr6+JNPPpmUlZUlEZFceOGFzVYTAEBrcdppp9X3jm+++eY6nePb3/52/VMT3n777dTH586dW/+z1mOPPXatj330Z7mbb755smzZstT+2267rX7N3Llz1/rYR5928Ema0vd99BpjxozJfCJakqzdhzf0s9cTTzyxvo7mrBGgqTxRAVhvLViwoP5/r/kdwMXYcMMNM8+xxne/+93o3r17o+u5995763/f149//OPMNSeddFKUl5c3+twfddBBB8Xmm2+eyvv27Rvjxo2LiIhnnnmmUefs2LFj7LHHHhER8c9//rNJ9QEArC+qqqrqj3fffTdWr14dERHLli2LmpqaBn+n7qfp3LlznHzyyZkf22+//SKi8f1exP89yWD69On1tWY59dRTG33uhixZsiT22WefeOutt2LkyJFx88031z9ZoVhr6v54n7p06dL6p5mdcsopMXjw4OYp+hOuWV1dHffcc09ERPzgBz/I7O232WabOOCAAyIi4vrrr2+2mgAAWouP/ky1d+/ejd6fJEncdNNNERHx7W9/OwYMGJBas/HGG8e3v/3tiIi44YYbGjzXSSedFF27dk3le+65Z2ywwQYREfHss882usbm7Pt+8IMfRMeOHT/1mqeddlpmvubvALNmzYply5a1SI0A68KgAkAzGz9+/Drte/LJJyMiYsiQITFs2LDMNT169IjttttunWuLiBg7dmyDHxs0aFBERCxcuDDz4//4xz9i0qRJsfnmm0f37t2jUCjUH2seHTZv3rwm1QcAsL5IkmStY/ny5TFz5sw44ogj4o477ogvfvGLMWXKlEafd8stt2xwcPbT+r2qqqo488wzY9y4cdGnT58oKyur7/e22GKLiPhwkGLRokWZ+7t27Rrbbrtto2vOsnr16jj00EPjqaeeig033DDuuOOOqKioyFz72muvxcknnxzbbbdd9OrVKzp27Fhf91577RUR6T71iSeeiJUrV0ZExL777tvo+oq55scfM/zkk09GkiQRETFhwoQGz73rrrtGxIcDJWtqBADgQ7Nnz67vZ4vpqRYsWBCzZ8/OXNPQz0rLysrqf/1XQ73zJ2nOvq+Ynzf37t07Nt1008yPrfk7QESs1cfrTYFSa9w/QwBoRz76FIUFCxbERhttVNS+9957L/Mca/Tr12+d6nn33XcjYu3GMUuxdTakR48eDX5szb9Oy2o4f/jDH671e8w6duwYlZWV9ZPFS5YsiaVLl8bSpUubVB8AwPqqS5cuMXr06Ljyyitj4cKFccstt8SkSZNizpw50bNnz6LPU0y/t2rVqtTHpk+fHnvttVdUV1fXZ927d4/y8vIoFAqxevXq+l546dKlaz1pbI0+ffpEhw7N828iTjjhhPjb3/4WnTt3jilTpsRnPvOZzHW33HJLHHbYYbFixYr6rGfPntGlS5coFArxwQcfxKJFi1J96vz58+v/9yabbNKo2tb1mu+88079//6kvn7jjTeOiA/fp4ULF0b//v0bVR8AQGv20Z+pLly48FN/Hvpxje2p1uzJ+sdh6/qz0uau8ZP6vmJ+3lzM64hY+7XoTYFS80QFYL215l+ERfzf0wyKMXPmzIj48Ie2WT/QLOYxXJ+kUCg0aX9LuO++++qHFL7zne/Es88+GytWrIiFCxfG/PnzY/78+XHCCSdERNRP4QIAsO6OOuqoiIioqamJO++8s8Wvt2rVqjjssMOiuro6Ro8eHXfeeWfU1tbG4sWLo6qqKubPnx+PPvpo/fqGer6m9sJrXHrppXHppZdGRMSVV17Z4L8iW7BgQUyaNClWrFgRX/rSl+LBBx9c61dnzJ8/P/785z9n7l3Xvrsp1wQA4MMngK2x5metNKy5emyA1sagArDe2mWXXer/tddf/vKXovYsWbIk7rvvvoiI+MIXvtDo34/7SdY8Suytt976xHUff3xsHtb8Hrfdd989Lrvssthqq61SDfJH/0UaAABN89GB2IYeU9ucpk+fHm+88UZ07Ngx7rjjjthzzz1T/yorr37vzjvvrB+CPe200+JrX/vaJ66tra2NysrKuP3222OnnXZK/Y7hhur+6O8yfuONNxpV37pe86P/Gu6TfmXamo+VlZWt0+9tBgBozT76c9lbbrml0fsb21N9fE8e2kLf1xZqBNo3gwrAemvgwIGx3377RcSH/yH+5Zdf/tQ9F110USxevDgiPnyyQHNa87t833jjjXj99dcz1yxZsiRmzJjRrNctxty5cyMiYptttsn8eJIkcf/99+dZEgBAu/bRHxR269atxa+3pt/r27dvg499/fvf/97idTz99NNxyCGHxOrVq+Pggw+On/zkJ5+4fk3dI0aMiPLy8sw1DdW9/fbb1/8as9tvv73oGptyzW233bb+h/JTp05t8Bpr9m+99dbRqVOnomsDAGgL+vfvHwceeGBERFx33XXxyiuvFL03SZIYNmxY/X8wL6an6tOnT+avfVhXH/1VZw09aawt9H1toUagfTOoAKzXfvrTn0bXrl1jxYoV8ZWvfKX+d+5mueuuu+Kcc86JiA+nfvfee+9mrWW33Xar/93DP//5zzPXXHTRRbFs2bJmvW4xKioqIuLDHxxn+e1vfxuvvfZaniUBALRr1113Xf3/3n777Vv8emv6vaqqqqiqqkp9fN68eXHJJZe0aA1vvfVW7LPPPrFkyZIYO3ZsXH311Z/66xnW1P3KK6/E+++/n/r4U089tda9/Kjy8vI49NBDIyLivPPOqx9A+DRNuWavXr1i9913j4iICy+8MLO3f/rpp+uf+HbYYYcVVRMAQFtzzjnnRPfu3WP58uVxwAEHfOpTZBctWhQHHnhg1NTURKFQiEMOOSQiIn73u99lPs3qrbfeit/97ncR0fw91Zqf4UZEVFdXZ65pC31fW6gRaN8MKgDrtS233DKuvPLK6NixYzz77LOxzTbbxP/+7/+u1WC+8sorceKJJ8Z//ud/xgcffBCf+cxn4rrrrlvn32nbkG7dusUPf/jDiIi44oorYvLkybFw4cKIiFi8eHGcf/75cdZZZ0VlZWWzXrcYe+yxR0R8OKzx05/+NJYuXRoRHzbiP//5z+N73/te9OnTJ/e6AADam/nz58dpp50WV199dURE7LDDDjFu3LgWv+6OO+4Y3bp1iyRJ4uCDD67/V22rV6+Oe+65J3beeedm738/asWKFbHvvvvGvHnzYsiQIXHrrbdGly5dPnXfbrvtFh06dIiFCxfG4YcfXv8D7g8++CBuuumm2G233VK/wuKjfvazn8WGG24YCxYsiPHjx8dNN90Uy5cvj4gP/3Xcc889Fz/4wQ/ij3/8Y7Nd85xzzolOnTrFrFmzYvfdd49nn302IiLq6urizjvvjL322itWrVoVn/3sZ+Nb3/rWp988AIA2aLPNNos//vGPscEGG8Tzzz8fo0ePjvPPPz9mzZpVv2b16tUxc+bMOOOMM+Izn/lM/PWvf63/2I9+9KPo1atXLFy4MCZMmBCPPPJI/cemTZsWEyZMiOrq6ujdu3eccsopzV77midzXXnllQ0+VaEt9H1toUag/TKoAKz3vvrVr8Ydd9wRgwYNinnz5sWRRx4ZlZWV0atXr+jatWuMGDEiLrrooli1alXstttu8eijj671+2yb0+TJk+Oggw6KiA+nWPv27Ru9e/eOysrKOOWUU+Lwww+PfffdNyKiqB/cNpevf/3r8YUvfCEiIs4444zo0aNH9O7dO/r06RM//vGPY4899ohjjjkmt3oAANqDAQMGrHX06tUrBg4cGD/72c8iImLUqFHxl7/8pUUHBNaoqKiIX/ziFxER8fDDD8eIESOiR48e0b1799hjjz2ipqYmrrrqqha7/ttvvx1PPvlkREQsWLAgtt5669T9WXN8/vOfr983fPjw+MEPfhAREX/9619j4403jl69ekX37t3jkEMOie7du3/ikyA23njjuOeee2KjjTaKuXPnxiGHHBI9evSIDTfcMMrLy2PUqFHxi1/8IhYsWNBs19x2223rfyj/z3/+Mz73uc9FRUVFdOvWLfbee+946623YvDgwXH77bdH9+7dm3RfAQBas4kTJ8b9998fm266abz33ntxyimnxPDhw6Nz587Rp0+f2GCDDWLbbbeNn/70p1FTUxOHHXZY/a9F23jjjWPKlClRUVERzz//fIwfPz66d+8e3bt3jx133DFefPHF6NWrV0yZMqXBX222rsrLy+O//uu/IuLDn+d27949Ntlkkxg6dGicfPLJ9evaQt/XFmoE2i+DCgDx4RMDZs2aFZdffnnstddesdFGG8X7778fnTp1is022yyOPPLI+Pvf/x733HNP9O3bt8XqKCsri5tuuimuvPLKGDNmTHTt2jVWrVoV22+/fVx55ZVxzTXX1D/toVevXi1Wx8d16tQp7r333jjzzDNjs802i06dOkWSJDFmzJj4zW9+E7fddlt07Ngxt3oAANqDNb9mYc2xbNmyGDBgQOy+++5xxRVXxBNPPBGDBg3KrZ5vf/vb8be//S123nnn6N69e6xatSo22mij+N73vhdPP/10jBo1Kpc6li5dmro3Hz3efffdtdafd955cc0119T3zytXroxNN900fvSjH8XMmTM/9R5uu+228eKLL8Z5550XO+ywQ3Tu3DkWLFgQ77//fuy8885x8cUXx9e+9rVmveYhhxwSzz//fHzrW9+Kz372s7FixYooKyuL0aNHx9lnnx3PPfdcjBw5ct1uIABAGzJ+/Ph46aWX4vrrr4/DDz88Nt100+jSpUssXrw4evfuHTvuuGP8+Mc/jhdffDGuu+666NSpU/3enXbaKV588cU46aSTYuTIkVFXVxdJksTIkSPj5JNPjhdffLH+H181t8suuyzOOuus+h55zpw58cYbb6R+tXBb6PvaQo1A+1RIGnomDQCtTpIkMWTIkJg3b15cc8019ZO7AABA8/nqV78aM2bMiGeeeSY6d+5c6nIAAACg3fFEBYA25I9//GPMmzcvysrKYsKECaUuBwAA2qXjjjsuXnnllfjjH/9Y6lIAAACgXTKoANDKHHbYYXHzzTev9ZiwqqqqOO+88+Koo46KiIivf/3rMXDgwFKVCAAA7c5bb70VK1asiIiIWbNmRUTEk08+WcqSAAAAoN0qK3UBAKztrrvuihtuuCEiIsrLy6NTp05RU1NT//EvfOELcdFFF5WqPAAAaJeuu+66OPXUU6NHjx6xaNGiiIjYcsstS1wVAAAAtE8GFQBamUsuuSTuuuuumDlzZrzzzjuxZMmS6Nu3b4wePToOPfTQ+K//+q/o1KlTqcsEAIB2Zfjw4TFkyJCYN29eDBgwIA488MA4+uijS10WAAAAtEuFJEmSUhcBAAAAAAAAAKwfOpS6AAAAAAAAAABg/WFQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADITVmpLnzZZZfFhRdeGPPnz4+tt946Lr300hgzZkxRe+vq6uKtt96KHj16RKFQaOFKAQBobZIkicWLF8egQYOiQ4fWMXu7rv2t3hYAYP3W2npbP7cFAGBdNaq3TUrghhtuSDbYYIPkf//3f5Pnn38+Oeqoo5JevXolVVVVRe2fO3duEhEOh8PhcDgcjvX8mDt3bgt3rsVpSn+rt3U4HA6Hw+FwRLSO3tbPbR0Oh8PhcDgczXEU09sWkiRJImdjx46Nz3/+8/HrX/86Ij6ctB08eHB873vfi1NOOeVT99fU1ESvXr1auEoAAFq76urqqKioKHUZTepv9bYAAES0jt7Wz20BAGgOxfS2uT9L7IMPPogZM2bEhAkT/q+IDh1iwoQJMX369Mw9K1asiNra2vpj8eLFeZULAEAr1hoeJ9vY/lZvCwBAllL3tn5uCwBAcymmt819UOG9996L1atXR//+/dfK+/fvH/Pnz8/cc+6550ZFRUX9MXjw4DxKBQCAT9XY/lZvCwBAa+TntgAA5Cn3QYV1ceqpp0ZNTU39MXfu3FKXBAAA60RvCwBAe6G3BQBgXZXlfcENN9wwOnbsGFVVVWvlVVVVMWDAgMw9nTt3js6dO+dRHgAANEpj+1u9LQAArZGf2wIAkKfcn6iwwQYbxHbbbRdTp06tz+rq6mLq1Kkxbty4vMsBAIAm0d8CANAe6GsBAMhT7k9UiIg48cQT44gjjojtt98+xowZExdffHEsXbo0vvGNb5SiHAAAaBL9LQAA7YG+FgCAvJRkUOGQQw6Jd999N84444yYP39+jB49Ou6+++7o379/KcoBAIAm0d8CANAe6GsBAMhLIUmSpNRFNFZtbW1UVFSUugwAAEqspqYmevbsWeoymkRvCwBAhN4WAID2o5jetkNOtQAAAAAAAAAAGFQAAAAAAAAAAPJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgN80+qHDuuefG5z//+ejRo0f069cvJk6cGC+//PJaa95///049thjo0+fPtG9e/c48MADo6qqqrlLAQCAJtHbAgDQXuhtAQBoTZp9UOGhhx6KY489Nh599NG47777YuXKlbHbbrvF0qVL69eccMIJcfvtt8ef//zneOihh+Ktt96KAw44oLlLAQCAJtHbAgDQXuhtAQBoVZIW9s477yQRkTz00ENJkiRJdXV10qlTp+TPf/5z/ZoXX3wxiYhk+vTpRZ2zpqYmiQiHw+FwOBwOx3p+1NTUtEgP2xC9rcPhcDgcDoejpQ69rcPhcDgcDoejvRzF9LbN/kSFj6upqYmIiN69e0dExIwZM2LlypUxYcKE+jWbb755DBkyJKZPn555jhUrVkRtbe1aBwAA5E1vCwBAe6G3BQCglFp0UKGuri6OP/74GD9+fGy11VYRETF//vzYYIMNolevXmut7d+/f8yfPz/zPOeee25UVFTUH4MHD27JsgEAIEVvCwBAe6G3BQCg1Fp0UOHYY4+N5557Lm644YYmnefUU0+Nmpqa+mPu3LnNVCEAABRHbwsAQHuhtwUAoNTKWurE3/3ud+OOO+6Ihx9+ODbeeOP6fMCAAfHBBx9EdXX1WtO5VVVVMWDAgMxzde7cOTp37txSpQIAwCfS2wIA0F7obQEAaA2a/YkKSZLEd7/73bjlllvi/vvvj2HDhq318e222y46deoUU6dOrc9efvnlmDNnTowbN665ywEAgHWmtwUAoL3Q2wIA0Jo0+xMVjj322Ljuuuvi1ltvjR49etT//rKKioro2rVrVFRUxJFHHhknnnhi9O7dO3r27Bnf+973Yty4cbHDDjs0dzkAALDO9LYAALQXelsAAFqVpJlFROZx1VVX1a9Zvnx58p3vfCeprKxMysvLk/333z95++23i75GTU1Ng9dxOBwOh8PhcKw/R01NTXO3s2tp6Lp6W4fD4XA4HA5Hcx96W4fD4XA4HA5HezmK6W0L/3+T2qbU1tZGRUVFqcsAAKDEampqomfPnqUuo0n0tgAAROhtAQBoP4rpbTvkVAsAAAAAAAAAgEEFAAAAAAAAACA/BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAclNW6gIAaP2eeOKJVLbddttlri0UCi1dDgAArLMkSYpeq7cFAKA1K76zjdDZAq2NJyoAAAAAAAAAALkxqAAAAAAAAAAA5MagAgAAAAAAAACQG4MKAAAAAAAAAEBuDCoAAAAAAAAAALkpK3UBAJTGAw88kMqWLFmSufa2225LZf3792/2mgAAYF0kSVLqEgAAoFk0prMtNHE/QCl5ogIAAAAAAAAAkBuDCgAAAAAAAABAbgwqAAAAAAAAAAC5MagAAAAAAAAAAOSmrNQFANDyLr/88lT23nvvpbIvfOELmfurqqpS2b333tv0wgAAoJGSJCl1CQAA0CyyOttC7lUAlIYnKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG7KSl0AwEc98cQTqeyZZ57JXLt48eJU1qVLl1S22WabZe7fZZddGlld65J1r1588cXMtdtvv30q69OnTyp7++23M/dXVVWlstGjR39KhQAA67ckSXK7VqFQyO1aLSHPewUAQOPl2a217c62cfeqrb9WgKbwRAUAAAAAAAAAIDcGFQAAAAAAAACA3BhUAAAAAAAAAAByY1ABAAAAAAAAAMhNiw8qnHfeeVEoFOL444+vz95///049thjo0+fPtG9e/c48MADo6qqqqVLAQCAJtHbAgDQXuhtAQAopbKWPPnjjz8ev/vd7+Jzn/vcWvkJJ5wQf/vb3+LPf/5zVFRUxHe/+9044IADYtq0aS1ZDtAGzJ07N5VVVlZmrt1rr71S2auvvlpUFhHx3//936lso402SmV9+vTJ3P/BBx+ksvfffz9zbV1dXSrr0KH4WbGamppUVl1dncp23XXXzP1Z18q61wsWLMjc/8UvfjGV1dbWZq4FaK/0tkBrliRJqUsAoA3R2wLNodBC523rnW1L3ReA9qbFnqiwZMmSOPzww+OKK65Y6z8y1tTUxP/8z//Er371q/jSl74U2223XVx11VXxyCOPxKOPPtpS5QAAwDrT2wIA0F7obQEAaA1abFDh2GOPjb333jsmTJiwVj5jxoxYuXLlWvnmm28eQ4YMienTp2eea8WKFVFbW7vWAQAAedHbAgDQXuhtAQBoDVrkVz/ccMMN8eSTT8bjjz+e+tj8+fNjgw02iF69eq2V9+/fP+bPn595vnPPPTfOPvvsligVAAA+kd4WAID2Qm8LAEBr0exPVJg7d24cd9xxce2110aXLl2a5Zynnnpq1NTU1B9Zv1cdAACam94WAID2Qm8LAEBr0uxPVJgxY0a88847se2229Znq1evjocffjh+/etfxz333BMffPBBVFdXrzWdW1VVFQMGDMg8Z+fOnaNz587NXSrQCu2///6p7Pzzz89cu80226SyESNGpLKRI0dm7q+rq0tlt912WyorFAqZ+wcPHpzKhg4dmrm21LJe67///e9UNmbMmMz97777bipbsmRJ0wsDaOX0tkBTZPWRSZKUoBIA0NsCTZP1E9KGOtvsn6Y27VoAtD/NPqjw5S9/OZ599tm1sm984xux+eabxw9/+MMYPHhwdOrUKaZOnRoHHnhgRES8/PLLMWfOnBg3blxzlwMAAOtMbwsAQHuhtwUAoDVp9kGFHj16xFZbbbVW1q1bt+jTp099fuSRR8aJJ54YvXv3jp49e8b3vve9GDduXOywww7NXQ4AAKwzvS0AAO2F3hYAgNak2QcVinHRRRdFhw4d4sADD4wVK1bE7rvvHpdffnkpSgEAgCbR2wIA0F7obQEAyEshaYO/ILO2tjYqKipKXQaQk/PPPz8zP+SQQ1JZeXl5KuvQoUPm/rq6ulR22223pbLKysrM/YMHD05lQ4cOzVxbalmv9eGHH05lY8aMydz/7rvvprL33nsvc+2ee+7ZyOoA1l1NTU307Nmz1GU0id4W1i9t8K/g67VCwW9IBvKjtwXamoY6Wx1UaXlfgNagmN62JE9UAGiM3r17Z+b33ntvKps4cWIqy/qP9BHZAwz/+Z//WXRdDQ1A5KWh15Xl7bffTmVZP3RtzFBHY64PAAAAAAAAa5T2v7IBAAAAAAAAAOsVgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJCbslIXAPBpjjrqqMz8sssuS2VJkqSy5cuXZ+4vLy8v6vodOrTOma7G1DV48OBUtnr16lQ2bdq0zP01NTWpbNiwYUVfHwCADxUKhcw8q48FAIDWLLuzjcjqbBtaC8D6q3X+1zcAAAAAAAAAoF0yqAAAAAAAAAAA5MagAgAAAAAAAACQG4MKAAAAAAAAAEBuykpdAMC6qqurS2V/+tOfUtlmm22WuX/s2LHrfJ2IiA4d2vas15AhQ4rKIiKmTZuWymbMmNHsNQEAAAAAAND+te3/ygYAAAAAAAAAtCkGFQAAAAAAAACA3BhUAAAAAAAAAAByY1ABAAAAAAAAAMhNWakLAFhXe+65Zyq77LLLUtk+++zTItevq6srem2HDqWdC2tMrVnGjx+fyp5++unMtTNnzkxl22yzTZOuDwAApZAkSSorFAolqAQAAIrTULea7mwbXguQB09UAAAAAAAAAAByY1ABAAAAAAAAAMiNQQUAAAAAAAAAIDcGFQAAAAAAAACA3BhUAAAAAAAAAAByU1bqAgDW1aabbprKHnzwwVRWUVGRQzWfrK6urtQlNLt99903M7/jjjtyrgQAoO0rFAqpLEmSElQCAABNk+5sI7I626x1AKw/PFEBAAAAAAAAAMiNQQUAAAAAAAAAIDcGFQAAAAAAAACA3BhUAAAAAAAAAAByU1bqAgDW1b333lvUug4dzGS1hMGDB2fmAwcOzLkSAIC2L0mSUpcAAADNIquzLeReBQCtnf96BwAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkpqzUBQA0p7q6ulKXsN7r27dvqUsAAAAAAACgFfNEBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyE1ZqQsAWFcrV65MZZtuumkJKml5HTqk58qWLFmSyt58883M/cOHD2/2mmprazPzRYsWNfu1AAAAAAAAaD88UQEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJTVuoCANbVK6+8kspGjBiRyl544YXM/VtssUVR11m2bFlmXl5eXtT+xujQIXt+bObMmals2rRpqWznnXdu0vUb81qfffbZzLX33HNPKvvlL3+ZyjbeeOPM/QcffHAqKxQKmWsBAKAlZfWhSZI0+zkBAKC5NNStZnWhTetss88JUCxPVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAclNW6gIA1tWWW26Zyv7973+nsrfffjtz/0MPPZTKjjjiiFRWXl6+DtV9ug4d0rNi06ZNy1z7wAMPpLLjjz8+lXXv3j1z/5IlS1JZ1utq6LVm1ZVVU0TE2LFjU9mCBQtS2VNPPZW5/4Ybbkhljz32WCobM2ZM5n4AAGguSZIUta5QKDTpnI3ZDwDQFmV1VTqgltHQfS2us23c++J9BZrCExUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3ZaUuAGBd3X///als3rx5qexXv/pV5v4777wzlV199dWp7JhjjlmH6j7d3LlzU9kLL7yQufbYY49NZeXl5alsyZIlmfuz1tbV1aWyG2+8MXP/v//971Q2YMCAzLVZ+vTpU/T+1157LZX98Ic/TGWPPfZY5v4xY8YUXRcAQGtWKBRSWZIkJaiET9OY96Ux72vWWgCAtiirq2mog9IBpTXHvSp2bWP+xuF9BZrCExUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3ZaUuAODTXH755Zn5Sy+9lMqWLFmSyk499dTM/eeee24qe/PNN1PZtGnTMvePHz8+My/W66+/nso233zzzLUVFRWpbNmyZamsvLw8c/8LL7yQyq699tpUlnX/IiJGjRqVyrp06ZK5tq6uLjP/uBUrVmTmW221VSrLel1nnnlm5v7q6upU1qtXr6JqAgBoaUmSlLqE9UahUMjMS/0eZF2/MbU2tBYAIG8t1VVlnbe9dkDF3sOGXn9L3KvG7G/M9den9xUojicqAAAAAAAAAAC5MagAAAAAAAAAAOTGoAIAAAAAAAAAkBuDCgAAAAAAAABAbgwqAAAAAAAAAAC5KSt1AQAfdfnll6eyxx9/PHPtqFGjUtnIkSNT2cMPP5y5/+STT05l3/3ud1PZ1ltvnbm/qqoqlfXo0SNzbZcuXVLZkCFDUllFRUXm/rq6ulRWXl6eyqZMmZK5/4c//GEqy7pXBx98cOb+RYsWpbIVK1Zkri0rS39r6datWyrr0CF7Vq6mpiaVvffee6nskUceydx/ww03ZOYAAHlLkqTZ9xcKhdyu31oVew/a6+sHACiFrM6q+M609PtLraHOtNjX0NT9zUF3DTQ3T1QAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgNwYVAAAAAAAAAIDcFJIkSUpdRGPV1tZGRUVFqcsAmuCWW27JzP/617+mss022yxz7apVq1JZWVlZKuvRo0fm/qeffjqVPf/886nsxz/+ceb+cePGpbJly5Zlru3QIT0XllVX1rqGnHHGGans0ksvLXr/IYccksoautdNVVlZmcqqqqoy1951112p7Kmnnir6WtXV1amsV69eRe8H2paampro2bNnqctoEr0ttH0N/bW6UCgUvbYllPr6TZVVf2O0htfaEq+hqecEWi+9LdAaNNRBZXUgjVnbmGvldf2WklVXU2tqqdfa1Pegqdcq9XsFtJxieltPVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAclNW6gKA9dN1112Xmb/11lupbLvttiv6vIsWLSoqi4gYOnRoKnv11VdT2Q9/+MPM/TNnzkxllZWVmWuXLFmSyrp3757Kysqy/1ieMmVKKrv00ktTWXl5eeb+Tp06pbLZs2enss022yxzf2O89957qeyxxx5LZXfddVfR58y6V7fcckvm2l69ehV9XgCA5lAoFEpdQtEaqjVJkpwrWVt7uIdN0dD9b0v3BQBoH5qj+8jqbFqiq2nonHldP+9rFXv9xmiJWhuqSWcLfJwnKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4KSZIkpS6isWpra6OioqLUZQBN8Nhjj2Xmv//971PZgw8+mLl2++23T2Vdu3ZNZQsWLMjcv3LlylS2aNGiVPboo49m7v/KV76Syi699NLMtX369Ells2bNSmWvv/565v5LLrkklS1btiyV1dTUZO5/4403MvOPGzFiRGbemD9zd9xxx1TWpUuXVJb1+iMiDjrooFS26667Fn19YP1SU1MTPXv2LHUZTaK3hbavMX+tLhQKTTpvY/Y3VUv9uCDP19BULXEP2tLrB/KltwVag8Z0P43parLOm2dX1NDrKraGpu5vDVqiu29Lrx/IVzG9rScqAAAAAAAAAAC5MagAAAAAAAAAAOTGoAIAAAAAAAAAkBuDCgAAAAAAAABAbspKXQCwfhozZkzRa88777zM/MILL0xlo0aNSmXHHnts5v7Ro0ensnPOOSeVPfroo5n7//73v6ey448/PnPtF77whVQ2c+bMVPbCCy9k7u/UqVMq22qrrVJZx44dM/fX1NSksueeey6Vvfrqq5n7FyxYkMpuueWWzLXjx49PZS+//HIqmzhxYub+4cOHZ+YAAK1VoVAoem2SJEWfIytraH9T62rM/mJraI770tTztsT1G6MlagUAaEmN6V4a6qCyzpGVNaYDa2pX1dD+YmtojvvS1PM29frFXqup+wGK5YkKAAAAAAAAAEBuDCoAAAAAAAAAALkxqAAAAAAAAAAA5MagAgAAAAAAAACQG4MKAAAAAAAAAEBuykpdAMCnOeWUU4pee9xxx6Wy8ePHZ66tq6tLZf37909lu+22W+b+3r17p7IBAwZkrr3nnntSWWVlZSobM2ZM5v5iVVRUZOaf+cxnUtnQoUNT2aJFizL3P/HEE6nsmWeeyVz7hS98IZV99rOfTWUPPvhg5n4AgPasUCgUvTZJkiZdK2t/c1y/2HM0tf6WOm9j7gEAAA1rTFeV1cGVen9jztGYDrQ56mqJawG0Np6oAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG5aZFDhzTffjK997WvRp0+f6Nq1a4waNWqt32+eJEmcccYZMXDgwOjatWtMmDAhXn311ZYoBQAAmkRvCwBAe6G3BQCgtShr7hMuWrQoxo8fH7vsskvcdddd0bdv33j11VejsrKyfs0FF1wQl1xySVx99dUxbNiwOP3002P33XePF154Ibp06dLcJQHtVHV1dSr7xz/+kcoWLVqUuf/tt99OZT/60Y9S2Uf/wv5Rl1xySSqrqanJXLv11ltn5s2todf69NNPp7KhQ4emsj59+mTu79evXyp79tlnM9f+9a9/TWW9evVKZQsWLMjcT+v02muvpbLrr78+lR177LGZ+7M+B6At0NsCeUmSpKTXKRQKTT5HXrJqbWpNDb3+Ur9WWkZLfb5Aa6e3BfKS9Z22Md89i/1O3dC6lrhWoYkXa8z2pnagDZXU1PeF1qmlPl8gD80+qHD++efH4MGD46qrrqrPhg0bVv+/kySJiy++OE477bTYb7/9IiLimmuuif79+8eUKVPi0EMPbe6SAABgnehtAQBoL/S2AAC0Js3+qx9uu+222H777eMrX/lK9OvXL7bZZpu44oor6j8+e/bsmD9/fkyYMKE+q6ioiLFjx8b06dMzz7lixYqora1d6wAAgJamtwUAoL3Q2wIA0Jo0+6DCa6+9Fr/5zW9i+PDhcc8998QxxxwT3//+9+Pqq6+OiIj58+dHRET//v3X2te/f//6j33cueeeGxUVFfXH4MGDm7tsAABI0dsCANBe6G0BAGhNmn1Qoa6uLrbddtv4+c9/Httss00cffTRcdRRR8Vvf/vbdT7nqaeeGjU1NfXH3Llzm7FiAADIprcFAKC90NsCANCalDX3CQcOHBhbbLHFWtnIkSPjL3/5S0REDBgwICIiqqqqYuDAgfVrqqqqYvTo0Znn7Ny5c3Tu3Lm5SwXauHvuuSeVVVdXp7K33347c//DDz+cyv70pz+lsieeeCJzf9++fVPZzjvvnLl2q622SmWVlZWZa4tVXl6eyl544YXMtVk/KMi6VzfeeGPm/hNOOCGV7bTTTplra2pqUtnUqVNT2VNPPZW5n9Jq6PP91VdfTWWjRo1KZb169WrukqCk9LZAW1EoFIpalyRJo/LWqNhaG7onbem10jRNfa+L/bqCtkJvC7Q2DX2nLvY7cEP7s/JGfVdvTAtR5MUaun6xl2rq/k86B21DY75emvw1ADlp9icqjB8/Pl5++eW1sldeeSU22WSTiIgYNmxYDBgwYK3/aFVbWxv/+te/Yty4cc1dDgAArDO9LQAA7YXeFgCA1qTZn6hwwgknxH/8x3/Ez3/+8zj44IPjsccei9///vfx+9//PiI+nEY//vjj45xzzonhw4fHsGHD4vTTT49BgwbFxIkTm7scAABYZ3pbAADaC70tAACtSbMPKnz+85+PW265JU499dT4yU9+EsOGDYuLL744Dj/88Po1kydPjqVLl8bRRx8d1dXVseOOO8bdd98dXbp0ae5yAABgneltAQBoL/S2AAC0Js0+qBARsc8++8Q+++zT4McLhUL85Cc/iZ/85CctcXkAAGg2elsAANoLvS0AAK1Fh1IXAAAAAAAAAACsP1rkiQoAzemJJ57IzKurq1PZLrvskso6dMieydpkk01S2Z577pnKampqMvd/9NGIa2y11VaZa1tC1uvaYostMteWl5enshdeeCGVnXnmmZn7t9lmm6LrqqioSGUHHHBAKpszZ07m/sceeyyVjRkzpujrU7ybbrqp6LWDBg1KZS+++GJzlgMA64UkSdrUeduy1nBPsmooFAolqKT9aw3vNwCsbxr67ltst9PQumK/qzdLV9USLUTWORsottjX0NR73RiNeV90ti3DvWZ94YkKAAAAAAAAAEBuDCoAAAAAAAAAALkxqAAAAAAAAAAA5MagAgAAAAAAAACQm7JSFwDwUU888UQqW7RoUebaUaNGpbIOHYqfv9pmm21S2T//+c9U9tprr2Xu33777Yu+VlMtW7YslT3++OOprKamJnN/ZWVlKttnn32aXliGurq6VJb1vuy3336Z+1999dVmr4mI22+/PZX17ds3lQ0ZMiRz/7Rp04peCwB8KEmSUpcA7ZKvLQDIX9Z330ILXaulzlv0xZraarTAC8j1npCrPL+2oLXxRAUAAAAAAAAAIDcGFQAAAAAAAACA3BhUAAAAAAAAAAByY1ABAAAAAAAAAMhNWakLAPio1157LZV17Ngxc22HDk2btcra36NHj1R27733Zu7ffvvtm3T9LC+88EJmPmvWrFRWUVGRyiZOnNjcJTWorq6uSWufffbZzLUrVqxIZVnvQadOnTL377LLLkXX1dbNnTs3lT311FOZazfddNNU1qdPn1TW0Puada3999//UyoEAODjkiQpem2hUGjBSlqXxtyXlrA+3WsAaC8a6h6a/F096wQtdrG2ozG3pdj97VWe9yXrWuvTvaZt80QFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3ZaUuAOCjDj744FR2wgknZK4dP358KuvQoWnzV0OHDk1ljz/+eObaHXbYIZVttNFGRV/r6aefTmVvvPFG5tqJEycWfd6WUFdXV/Tau+++O5XNnj07lVVWVmbu79OnTyorK0t/u3rooYeKvv4ee+yRubateOyxxzLzqVOnprKGXmuxXxvPPfdcZj5w4MCi9gMA/6dQKKSyJElKUMn/yaopovR1kS3rfWnoPWwrfK4BQNuU1YE09F29JbqVrGs1dJ3GrC1a227BWkxjbkuLvC8llufXALRHnqgAAAAAAAAAAOTGoAIAAAAAAAAAkBuDCgAAAAAAAABAbgwqAAAAAAAAAAC5KSt1AbC+uuWWW1JZTU1N5tqKiopUNmfOnMy15eXlqeyoo45qZHXN65577kllb7/9dubaxYsXp7JFixZlru3QoflnrSorK1PZ+eefn7n27rvvTmV77bVX5toXXnghlS1btiyVTZw48VMqbD2yXn9D9t5771Q2dOjQzLV1dXVFnbO2tjYzf/fdd4uuq9QeffTRVPbqq6+msob+bNhjjz1SWWO+LrLuddbnakTE97///aLPC8D6J0mS3K5VKBRyu1aWPF9rS2jr9dN6taXPrVL/OQJA65bnd7RSf0dq6mvNs/6sazVUf6nvK21fsV8beX6u+XynPfJEBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgN2WlLgDakxdffDEzf/PNN1PZRhttlMoqKysz93fokJ4p2nrrrTPX3nrrrals8uTJqeyCCy7I3N9Ut99+eyqrqKhIZQ3V//DDD6eyUaNGZa7Nui8tYfTo0Zn5woULU9n06dMz19bU1KSycePGpbK6urqi68rr9UdEPPTQQ6ls2bJlmWsPOOCAos7ZmNeaZfXq1Zn5hhtu2KTzNtUTTzyRyhr6vJg7d24q+/KXv5zKysqa/u066/Ml630t9f0DoPVIkqTUJWTKqqtQKOR2LVif+BoAoL1ozHe0lukss2XV1VLXz/NaeWnr9ZOv1vrnAKzPPFEBAAAAAAAAAMiNQQUAAAAAAAAAIDcGFQAAAAAAAACA3BhUAAAAAAAAAAByU1bqAqAtuP3221PZqlWrUtnjjz+euX/mzJmp7Itf/GIqW7x4ceb+Tp06pbKKiorMtRMmTEhlf//731PZL3/5y8z9J510Umb+cTfccENmvummm6ayPn36FHXOiIi33347le28885F76+rqyt6bYcOxc1qvf7665n5G2+8kcrmzJmTuTbr/RoyZEhR6xqS9VqLfU0NyfpcjYh45JFHUtmpp57apGs1VUNfLz169Gj2a82dOzcznzp1aiq7+OKLU1nfvn2LvtYrr7ySyrbYYoui9zck6/M163WdeOKJTb4WAK1bkiSlLqHZNfSaCoVCk/ZDe9SYz/esr6G29PVS7J8BALRdxX5XaqnvCFnXb8y1stY29JqKPW9T90Nb0pjOtDFfb8Veq7X+2QJthScqAAAAAAAAAAC5MagAAAAAAAAAAOTGoAIAAAAAAAAAkBuDCgAAAAAAAABAbspKXQC0JrfffntmPnLkyFTWvXv3VLZs2bLM/XV1dansmWeeSWW9evXK3P/ggw+msl133TVz7cqVK1PZhAkTUtl1112Xuf/JJ59MZX379i1qXUREnz59MvNiVVZWprLf/OY3mWtHjx6dysaPH5/KlixZkrn/3nvvTWX9+/dPZeXl5Zn7d9lll1T2t7/9LXPtTjvtlMr+/ve/p7Knn346c3/Wfdljjz1S2bRp0zL3V1RUpLKNNtoolT3yyCOZ+wcNGpTK5syZk7l2yJAhqaxDh/RcXNbXRWM09L707t27qP0PPPBAZj5v3rxU9thjjxVd1+c+97lU9vbbb2eufe2114q61oknnpi5P+s9bOi+Zv35tu+++2auBaB9SJKk1CVkKhQKqaylam2t9wCaW2v9Gsrz6x2A9q2h7x7p7zSlv35Ta21oXbHfQfO6J3nLev3t9bWu7xrTLTbmc6Axn0PFri31n03Q1nmiAgAAAAAAAACQG4MKAAAAAAAAAEBuDCoAAAAAAAAAALkxqAAAAAAAAAAA5MagAgAAAAAAAACQm7JSFwClcv7556ey2bNnZ66tqqpKZW+//XYqGzp0aOb+7bbbLpWNGTMmlW244YaZ+/v165fKFi1alLm2rq4ulc2aNSuV7brrrpn7r7nmmlQ2fPjwVHbggQdm7i9Whw7Zc1Jdu3ZNZStXrsxc+/jjj6eyrPdq9OjRRddwySWXpLJzzz03c/+QIUNS2fvvv5+5tqH36+O23HLLzPzBBx9MZTNnzix6/1NPPZXK5syZk8qyXlNDss4ZETFlypRUdsQRR6SyioqKoq+V9Xk9d+7czLVJkqSyW2+9NZWtXr06c/+Xv/zlVFZW1rRvl1dccUVm/u6776ayPn36pLKbb745c/83vvGNVHb77bdnrt1pp51SWdafTQC0TVnf/1qrtlRrW1coFFKZ+9/2taX3MM9asz7fAWibsr57lPpP+Yaun2etpb4HTdWYe1Xs2oY6jfXpXrUlxb6HreFrqDGfb1la4vMd2iNPVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNyUlboAKJVhw4alsrq6usy1q1evTmUDBw5MZY8//njm/hkzZqSyPn36pLIvfelLmfuHDBmSyioqKjLXLlq0KDP/uE033TQz32yzzVJZWVn6j4qsLKLhe1jsuuXLl6ey73//+5lrO3RIz1odcMABqWzs2LGZ+7PWZt2/n//855n7Tz/99FQ2YsSIzLW33XZbKhs9enQq23bbbTP39+3bN5VNmTIllWW9poiIBQsWpLKzzz47lc2ZMydz/ymnnJLKKisrM9dm1fXQQw+lsokTJ2buX7JkSSq79dZbU1lDn8Obb755Ksv6esn6/Gkphx12WGb+q1/9KpVlvdcPPvhg5v6sP3Muu+yyzLXDhw//hAoBgNaoUCiksiRJit6ftTbrnI09b2vU1HtFfhr6HMzS0HvYmHMA0D601Hf1pn5H8R0pW9b7lXWvGnpfi13b0P1vzNqW0BK1NuZetSXrU/3t9T2EpvBEBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyE1ZqQuAUjn44INzu9bUqVNT2R133JHKbrrppsz9Bx10UCpbuXJlk2p65plnMvOtt946lXXv3j2VLVmyJHN/eXl5k+pasGBBKsu6VxERQ4cOTWV1dXWp7JZbbsncv++++xZV04YbbpiZb7LJJqns2WefzVzbq1evVFZdXZ3KZs+enbn/9ttvT2WzZs1KZTfffHPm/g022CCV7bbbbplrs1RWVqayrHsdETFhwoRU9vTTT6ey5557LnP/rbfemsp23nnnVDZ+/PjM/Q3VVUpZX0MREWPHjk1lV155ZSp77bXXMvf/8pe/TGXDhw9vZHUAtAeFQiG3ayVJktu1aH6t9f3L+hxuTK2t9XWt7xrzZ1PWe5jnn20AtB55/ulfbAfhO1L2vcrzvmRdq6H3r6l1Nea1Fru2MbXmea9L/b62Vk29L+4rFMcTFQAAAAAAAACA3BhUAAAAAAAAAAByY1ABAAAAAAAAAMiNQQUAAAAAAAAAIDcGFQAAAAAAAACA3JSVugBYH3z5y18uat3kyZMz85tvvjmVHXTQQUVff8mSJUWvrampSWXdu3cven+WadOmpbLnnnsuc+2rr76ayvbff/+ir3X44YensldeeSVzbY8ePVLZdtttl8qGDh2auf/FF19MZcuWLctcW11dncruu+++VDZhwoTM/ZWVlans7bffTmXTp0/P3P///t//S2UPP/xwKvvggw8y92fp0CF71u3xxx9PZdddd10q22WXXTL3f+c730llFRUVqayuru7TSmxxDb3fH1deXp6Z33///ansH//4Ryp79NFHM/ePGTOmqOsDQHMqFApFrUuSpIUraf2y7lVj7ktbuodNfa1tyfr0WltCQ/eq2D9bAKA5Ffvdp6Hv9O3xu1djXmtT1zb1/jW0vzHXKnZtnt1eS31eNfW1tsfP96Zyr6BpPFEBAAAAAAAAAMiNQQUAAAAAAAAAIDcGFQAAAAAAAACA3BhUAAAAAAAAAAByU0iSJCl1EY1VW1sbFRUVpS4DcjN58uRUtmLFisy1Bx10UCpbuHBh0deqq6tLZf369Utlw4cPz9z/3HPPpbJ77703lZ133nmZ++fOnZvKpkyZkrm2R48eqWzSpEmp7Oabb87cv3z58lQ2evToVPbII49k7r/rrrtS2fnnn5+5tmPHjkXVtXLlysz98+fPT2VZ78tXvvKVzP2LFi1KZU888UQqmz17dub+QqGQynr16pW5NusefvDBB6ns6aefztyf9RoGDhyYypYtW5a5v7y8PDMvdn9TZd3r+++/P3Pt0qVLU9k3vvGNVDZ48OCmFwbtVE1NTfTs2bPUZTSJ3pb1TRv8K+inyuqVIpr+WrPO21L3L89rsX5r6OsF0NtCW5TVLbWl73SNqb8l1jam22zM/qZeq9hzNqQtfV60pVrzVOzni3sFDSumt/VEBQAAAAAAAAAgNwYVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyE0hSZKk1EU0Vm1tbVRUVJS6DCipyZMnZ+YrVqxIZQcddFAqW7hwYeb+urq6VNa5c+dUtvnmm2fuv+OOO1LZ97///cy1xbrxxhsz8/PPPz+VPfnkk0Wf94tf/GIq++pXv5rKJkyYkLn/vvvuS2XHHHNM0dfP8sADD2Tmf/rTn1LZpEmTUtnixYsz9y9atCiVVVZWFl3X66+/nspqamoy12bd1xEjRqSyp556KnP/tddem8omTpyYysaNG5e5f9myZZl5sbJeV0Pfc2bMmJHKnn322VTWr1+/zP3f+c53Glkd8HE1NTXRs2fPUpfRJHpbiGhLfy0tFApN2t/Qa23qeRtzrWK11GulfWqJz2FY3+htoX1oqAMq9XfKrLqaWlOer7WpnWVbeq3kx/sKLaeY3tYTFQAAAAAAAACA3BhUAAAAAAAAAAByY1ABAAAAAAAAAMiNQQUAAAAAAAAAIDcGFQAAAAAAAACA3BSSJElKXURj1dbWRkVFRanLgFZp8uTJqWzFihWp7KCDDsrcv3DhwlRWV1eXyjbddNPM/Q888EAqGz9+fCrbbrvtMvc3xo033pjKDjnkkKL333vvvUWt22233Yo+Z1PdeeedmXl5eXkqW7x4cSpbtWpV0ddatmxZUdeJiOjcuXPR582S9Wf2iBEjMtcuWrQolV1yySWp7MADD8zcX1lZWVRNWZ/XERGvvfZaKnv22WeLOmdExB577JHKdthhh6L3A41TU1MTPXv2LHUZTaK3hYa1lb+uFgqFJp8j67U25rzF3qvmqLVYbeX9o3Hy/ByC9Y3eFtq3rM6o1N9VW6qmpp632C4yz/vXUE2lfg/J1ho/h2B9U0xv64kKAAAAAAAAAEBuDCoAAAAAAAAAALkxqAAAAAAAAAAA5MagAgAAAAAAAACQm0KSJEmpi2is2traqKioKHUZ0GZMnjw5la1YsSJz7UEHHZTKFi9enMoa+hp8+eWXU1nnzp1T2eGHH565f31y3XXXpbLu3btnrh0wYEAqq6qqavaamsOqVatS2bx581LZ5ptvnrl/m222SWVTpkxJZWVlZZn7sz43X3/99VRWW1ubuT/rvGPGjMlcu+uuu2bmQH5qamqiZ8+epS6jSfS20Dit8a+whUKh1CWUXGt8X2gcn8dQenpbWP9kdVB5fkcu9fVbq/XpvrSl19qYv3G01tcA65NieltPVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAclNW6gKAlnfBBRekssmTJ2euvfnmm1PZQQcdlMpqamoy93fq1CmV9e7d+9NKbPfuu+++VFZbW5vKhg4dmrn/wQcfTGWLFi1KZcuWLcvcX15e/skFNqPq6upU9swzz6Syk046qehzVlRUpLK77747c+12222Xyvr165fKsj6vIyIGDx5cdF0AQP4KhUIqS5KkBJWsv9b3+92WPgezagUAWo+s79QNdRW+q7eMrPvdEve6dXaL+X4ONvUe+BqA9scTFQAAAAAAAACA3BhUAAAAAAAAAAByY1ABAAAAAAAAAMiNQQUAAAAAAAAAIDdlpS4AKI0LLrggM588eXIqe/jhh1PZrrvumrl/wYIFqayurq6R1bU/8+fPT2U9evRIZbfffnvm/mHDhqWy/fffP5UNHjx4Hapreddff30qW7VqVebarPytt95KZQcffHDm/t13372R1QEAbV2hUMjMkyTJuRKaU0Pva6nl+XnVWu8BANByGvrun9WB6BRKr9jOsLW+Vy3xedXQPWmt9wAoHU9UAAAAAAAAAAByY1ABAAAAAAAAAMiNQQUAAAAAAAAAIDfNPqiwevXqOP3002PYsGHRtWvX+OxnPxs//elP1/odjkmSxBlnnBEDBw6Mrl27xoQJE+LVV19t7lIAAKBJ9LYAALQXelsAAFqTsuY+4fnnnx+/+c1v4uqrr44tt9wynnjiifjGN74RFRUV8f3vfz8iIi644IK45JJL4uqrr45hw4bF6aefHrvvvnu88MIL0aVLl+YuCWiECy64IJWde+65qayhr9WVK1emsuXLlze9sDaitrY2M7/ssstSWWVlZSr78pe/nLl/hx12aFphJdanT59Uduedd2aunT17dirbY489UtmIESOaXhjAp9DbQttWKBRS2Uf/YwyfrKXuVdb7sr5zT4A86G2hbcvqFrK6NV1FtsZ0to1Z2x7vd1P/FtAe7wnQMpp9UOGRRx6J/fbbL/bee++IiBg6dGhcf/318dhjj0XEhz/ouPjii+O0006L/fbbLyIirrnmmujfv39MmTIlDj300OYuCQAA1oneFgCA9kJvCwBAa9Lsv/rhP/7jP2Lq1KnxyiuvRETE008/Hf/85z9jzz33jIgP/6Xs/PnzY8KECfV7KioqYuzYsTF9+vTMc65YsSJqa2vXOgAAoKXpbQEAaC/0tgAAtCbN/kSFU045JWpra2PzzTePjh07xurVq+NnP/tZHH744RERMX/+/IiI6N+//1r7+vfvX/+xjzv33HPj7LPPbu5SAQDgE+ltAQBoL/S2AAC0Js3+RIWbbroprr322rjuuuviySefjKuvvjp+8YtfxNVXX73O5zz11FOjpqam/pg7d24zVgwAANn0tgAAtBd6WwAAWpNmf6LCD37wgzjllFPqf2fZqFGj4o033ohzzz03jjjiiBgwYEBERFRVVcXAgQPr91VVVcXo0aMzz9m5c+fo3Llzc5cKAACfSG8LAEB7obcFAKA1afZBhWXLlkWHDms/qKFjx45RV1cXERHDhg2LAQMGxNSpU+sb3Nra2vjXv/4VxxxzTHOXAzSDIUOGpLIuXbpkrn3mmWdS2UknndTsNbVWPXv2LHUJrdJuu+1W6hIA1oneFlifFQqFUpfQKrkvQFultwXWZzq4bO4LUErNPqiw7777xs9+9rMYMmRIbLnlljFz5sz41a9+Fd/85jcj4sO/0B9//PFxzjnnxPDhw2PYsGFx+umnx6BBg2LixInNXQ4AAKwzvS0AAO2F3hYAgNak2QcVLr300jj99NPjO9/5TrzzzjsxaNCg+Na3vhVnnHFG/ZrJkyfH0qVL4+ijj47q6urYcccd4+67727wX2gDAEAp6G0BAGgv9LYAALQmhSRJklIX0Vi1tbVRUVFR6jJgvXHttdemss9//vOZay+//PJUlvWrHwYPHtz0wgBY79XU1LT5X7ujt4V8tcRfgf0qAACag94WaKyszrYxnWlT9wNAQ4rpbTt84kcBAAAAAAAAAJpRs//qB6Btu/fee1PZSy+9lMr++c9/Zu4fO3ZsKvP0BAAASsHTEwAAaC88PQGA9sYTFQAAAAAAAACA3BhUAAAAAAAAAAByY1ABAAAAAAAAAMiNQQUAAAAAAAAAIDdlpS4AaF1WrVqVyubMmZPKDj744Mz9Y8aMafaaAACgFAqFQqlLAACABiWNWKuzBaC18UQFAAAAAAAAACA3BhUAAAAAAAAAgNwYVAAAAAAAAAAAcmNQAQAAAAAAAADIjUEFAAAAAAAAACA3hSRJklIX0Vi1tbVRUVFR6jKgXcr6I6FQKJSgEgD4dDU1NdGzZ89Sl9EkeltoOXpbANoSvS3wSbL+Q47OFoDWqpje1hMVAAAAAAAAAIDcGFQAAAAAAAAAAHJjUAEAAAAAAAAAyI1BBQAAAAAAAAAgN2WlLgBoXQqFQqlLAACAZqG3BQCgvdDZAtDeeKICAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuTGoAAAAAAAAAADkxqACAAAAAAAAAJAbgwoAAAAAAAAAQG4MKgAAAAAAAAAAuWn0oMLDDz8c++67bwwaNCgKhUJMmTJlrY8nSRJnnHFGDBw4MLp27RoTJkyIV199da01CxcujMMPPzx69uwZvXr1iiOPPDKWLFnSpBcCAACNpbcFAKC90NsCANCWNHpQYenSpbH11lvHZZddlvnxCy64IC655JL47W9/G//617+iW7dusfvuu8f7779fv+bwww+P559/Pu67776444474uGHH46jjz563V8FAACsA70tAADthd4WAIA2JWmCiEhuueWW+v9fV1eXDBgwILnwwgvrs+rq6qRz587J9ddfnyRJkrzwwgtJRCSPP/54/Zq77rorKRQKyZtvvlnUdWtqapKIcDgcDofD4XCs50dNTU1T2tm1ROhtHQ6Hw+FwOBylO/S2DofD4XA4HI72chTT2zb6iQqfZPbs2TF//vyYMGFCfVZRURFjx46N6dOnR0TE9OnTo1evXrH99tvXr5kwYUJ06NAh/vWvf2Wed8WKFVFbW7vWAQAALUlvCwBAe6G3BQCgtWnWQYX58+dHRET//v3Xyvv371//sfnz50e/fv3W+nhZWVn07t27fs3HnXvuuVFRUVF/DB48uDnLBgCAFL0tAADthd4WAIDWplkHFVrKqaeeGjU1NfXH3LlzS10SAACsE70tAADthd4WAIB11ayDCgMGDIiIiKqqqrXyqqqq+o8NGDAg3nnnnbU+vmrVqli4cGH9mo/r3Llz9OzZc60DAABakt4WAID2Qm8LAEBr06yDCsOGDYsBAwbE1KlT67Pa2tr417/+FePGjYuIiHHjxkV1dXXMmDGjfs39998fdXV1MXbs2OYsBwAA1pneFgCA9kJvCwBAa1PW2A1LliyJWbNm1f//2bNnx1NPPRW9e/eOIUOGxPHHHx/nnHNODB8+PIYNGxann356DBo0KCZOnBgRESNHjow99tgjjjrqqPjtb38bK1eujO9+97tx6KGHxqBBg5rthQEAwKfR2wLw/7V3ZyFal30fwH8zjo6WjdOCM5lZFoHtWJaYQQdKCxFtBMkUQwVRjaQFLRTWQZQt1EELthzUQXvQKnQgaoZgZmarNQVFhTpKi860l/f1Hrzvc/NMT+/btLzX/b9mPh+4wLnvP3LdfIfxe/BlBBgudFsAAIqS/qSVK1emiPiP093dnVJKqVarpUWLFqWOjo7U2tqa5syZk3p7ewf9HV999VWaN29eGj9+fGpra0sXXHBBGhgYGPIdduzY8bt3cBzHcRzHcUbW2bFjx5+ts7qt4ziO4ziOU8mj2zqO4ziO4zjD5Qyl2zallFIUpr+/PyZMmNDoawAA0GA7duwo/v/B1W0BAIjQbQEAGD6G0m2bM90FAAAAAAAAAMBQAQAAAAAAAADIx1ABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIxlABAAAAAAAAAMimyKFCSqnRVwAAoAKGQy8cDp8BAIC/bzj0wuHwGQAA+PuG0guLHCoMDAw0+goAAFTAcOiFw+EzAADw9w2HXjgcPgMAAH/fUHphUypw5lqr1WLz5s2x2267xcDAQOy7777xxRdfRFtbW6Ovxv+hv79fVoWQVVnkVQ5ZlUNW1ZdSioGBgZg0aVI0Nxe5va3Tbcvk50Q5ZFUWeZVDVuWQVfXptjSanxPlkFVZ5FUOWZVDVtX3Z7ptS6Y7/aOam5tj8uTJERHR1NQUERFtbW2+IQshq3LIqizyKoesyiGrapswYUKjr/CP0G3LJqtyyKos8iqHrMohq2rTbakCWZVDVmWRVzlkVQ5ZVdtQu23ZE10AAAAAAAAAoCiGCgAAAAAAAABANsUPFVpbW+PGG2+M1tbWRl+FPyCrcsiqLPIqh6zKISsaxfdeOWRVDlmVRV7lkFU5ZEWj+N4rh6zKIauyyKscsiqHrIaXppRSavQlAAAAAAAAAICRofjfqAAAAAAAAAAAlMNQAQAAAAAAAADIxlABAAAAAAAAAMjGUAEAAAAAAAAAyMZQAQAAAAAAAADIpuihwn333Rf7779/jB07NmbOnBmvv/56o6804i1evDiOOeaY2G233WLixIlxxhlnRG9v76Bnfvzxx+jp6Yk999wzxo8fH2effXZs3bq1QTfmX2699dZoamqKhQsX1l+TVbVs2rQpzjvvvNhzzz1j3Lhxcfjhh8cbb7xRfz+lFDfccEPsvffeMW7cuJg7d258/PHHDbzxyLRz585YtGhRTJ06NcaNGxcHHnhg3HTTTZFSqj8jq8Z59dVX47TTTotJkyZFU1NTPP/884PeH0o2X3/9dXR1dUVbW1u0t7fHRRddFN9++23GT8FwpdtWj25bLt22+nTbMui21abbUmW6bfXotuXSbatPty2Dblttuu3IVOxQ4amnnoorr7wybrzxxnjzzTfjyCOPjJNOOim2bdvW6KuNaKtWrYqenp547bXXYtmyZfHLL7/EiSeeGN999139mSuuuCJeeumleOaZZ2LVqlWxefPmOOussxp4a9atWxcPPPBAHHHEEYNel1V1fPPNNzF79uwYPXp0vPzyy7Fx48a48847Y/fdd68/c/vtt8fdd98d999/f6xduzZ23XXXOOmkk+LHH39s4M1Hnttuuy2WLFkS9957b3zwwQdx2223xe233x733HNP/RlZNc53330XRx55ZNx3332/+/5Qsunq6or3338/li1bFkuXLo1XX301Lr744lwfgWFKt60m3bZMum316bbl0G2rTbelqnTbatJty6TbVp9uWw7dttp02xEqFerYY49NPT099a937tyZJk2alBYvXtzAW/Fb27ZtSxGRVq1alVJKafv27Wn06NHpmWeeqT/zwQcfpIhIa9asadQ1R7SBgYF00EEHpWXLlqUTTjghLViwIKUkq6q55ppr0vHHH/+/vl+r1VJnZ2e644476q9t3749tba2pieeeCLHFfkfp556arrwwgsHvXbWWWelrq6ulJKsqiQi0nPPPVf/eijZbNy4MUVEWrduXf2Zl19+OTU1NaVNmzZluzvDj25bBt22+nTbMui25dBty6HbUiW6bRl02+rTbcug25ZDty2HbjtyFPkbFX7++edYv359zJ07t/5ac3NzzJ07N9asWdPAm/FbO3bsiIiIPfbYIyIi1q9fH7/88sug7KZNmxZTpkyRXYP09PTEqaeeOiiTCFlVzYsvvhgzZsyIc845JyZOnBjTp0+Phx56qP7+p59+Gn19fYPymjBhQsycOVNemR133HGxfPny+OijjyIi4u23347Vq1fHKaecEhGyqrKhZLNmzZpob2+PGTNm1J+ZO3duNDc3x9q1a7PfmeFBty2Hblt9um0ZdNty6Lbl0m1pFN22HLpt9em2ZdBty6Hblku3Hb5aGn2Bv+LLL7+MnTt3RkdHx6DXOzo64sMPP2zQrfitWq0WCxcujNmzZ8dhhx0WERF9fX0xZsyYaG9vH/RsR0dH9PX1NeCWI9uTTz4Zb775Zqxbt+4/3pNVtXzyySexZMmSuPLKK+O6666LdevWxeWXXx5jxoyJ7u7ueia/93NRXnlde+210d/fH9OmTYtRo0bFzp074+abb46urq6ICFlV2FCy6evri4kTJw56v6WlJfbYYw/58ZfptmXQbatPty2HblsO3bZcui2NotuWQbetPt22HLptOXTbcum2w1eRQwXK0NPTE++9916sXr260Vfhd3zxxRexYMGCWLZsWYwdO7bR1+EP1Gq1mDFjRtxyyy0RETF9+vR477334v7774/u7u4G345/9/TTT8djjz0Wjz/+eBx66KHx1ltvxcKFC2PSpEmyAiiYblttum1ZdNty6LYAw5NuW226bVl023LotlA9Rf7XD3vttVeMGjUqtm7dOuj1rVu3RmdnZ4Nuxb+bP39+LF26NFauXBmTJ0+uv97Z2Rk///xzbN++fdDzsstv/fr1sW3btjjqqKOipaUlWlpaYtWqVXH33XdHS0tLdHR0yKpC9t577zjkkEMGvXbwwQfH559/HhFRz8TPxca76qqr4tprr41zzz03Dj/88Dj//PPjiiuuiMWLF0eErKpsKNl0dnbGtm3bBr3/66+/xtdffy0//jLdtvp02+rTbcui25ZDty2Xbkuj6LbVp9tWn25bFt22HLptuXTb4avIocKYMWPi6KOPjuXLl9dfq9VqsXz58pg1a1YDb0ZKKebPnx/PPfdcrFixIqZOnTro/aOPPjpGjx49KLve3t74/PPPZZfZnDlz4t1334233nqrfmbMmBFdXV31P8uqOmbPnh29vb2DXvvoo49iv/32i4iIqVOnRmdn56C8+vv7Y+3atfLK7Pvvv4/m5sH/vI4aNSpqtVpEyKrKhpLNrFmzYvv27bF+/fr6MytWrIharRYzZ87MfmeGB922unTbcui2ZdFty6Hblku3pVF02+rSbcuh25ZFty2Hblsu3XYYS4V68sknU2tra3rkkUfSxo0b08UXX5za29tTX19fo682ol166aVpwoQJ6ZVXXklbtmypn++//77+zCWXXJKmTJmSVqxYkd544400a9asNGvWrAbemn854YQT0oIFC+pfy6o6Xn/99dTS0pJuvvnm9PHHH6fHHnss7bLLLunRRx+tP3Prrbem9vb29MILL6R33nknnX766Wnq1Knphx9+aODNR57u7u60zz77pKVLl6ZPP/00Pfvss2mvvfZKV199df0ZWTXOwMBA2rBhQ9qwYUOKiHTXXXelDRs2pM8++yylNLRsTj755DR9+vS0du3atHr16nTQQQelefPmNeojMUzottWk25ZNt60u3bYcum216bZUlW5bTbpt2XTb6tJty6HbVptuOzIVO1RIKaV77rknTZkyJY0ZMyYde+yx6bXXXmv0lUa8iPjd8/DDD9ef+eGHH9Jll12Wdt9997TLLrukM888M23ZsqVxl6but4VXVtXy0ksvpcMOOyy1tramadOmpQcffHDQ+7VaLS1atCh1dHSk1tbWNGfOnNTb29ug245c/f39acGCBWnKlClp7Nix6YADDkjXX399+umnn+rPyKpxVq5c+bv/TnV3d6eUhpbNV199lebNm5fGjx+f2tra0gUXXJAGBgYa8GkYbnTb6tFty6bbVptuWwbdttp0W6pMt60e3bZsum216bZl0G2rTbcdmZpSSun/93c2AAAAAAAAAAD8t+Y/fgQAAAAAAAAA4J9hqAAAAAAAAAAAZGOoAAAAAAAAAABkY6gAAAAAAAAAAGRjqAAAAAAAAAAAZGOoAAAAAAAAAABkY6gAAAAAAAAAAGRjqAAAAAAAAAAAZGOoAAAAAAAAAABkY6gAAAAAAAAAAGRjqAAAAAAAAAAAZPNf4gTQuldv9eMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2100x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcular os momentos da imagem bin&aacute;ria\n",
    "moments = cv2.moments(thresh)\n",
    "\n",
    "# Interpretar os principais momentos\n",
    "area = moments['m00']\n",
    "centroid_x = moments['m10'] / area\n",
    "centroid_y = moments['m01'] / area\n",
    "\n",
    "print(f\"&Aacute;rea: {area}\")\n",
    "print(f\"Centroide: ({centroid_x}, {centroid_y})\")\n",
    "\n",
    "# Desenhar o centroide na imagem de contorno\n",
    "cv2.circle(img_contours, (int(centroid_x), int(centroid_y)), 2, (0, 255, 0), -1)\n",
    "\n",
    "# Exibir a imagem original, binarizada e com contornos\n",
    "showImages([img_gray, thresh, img_contours], [\"Original\", \"Binariza&ccedil;&atilde;o\", \"Contorno\"], size=(21, 7), grid=(1, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "807ab50a-5d19-400d-be6d-3fb36fc7aa2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Per&iacute;metro: 281.39191579818726\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcular o per&iacute;metro (comprimento do arco) do contorno\n",
    "perimeter = cv2.arcLength(contours[0], False) \n",
    "\n",
    "# True indica que o contorno &eacute; uma forma fechada, e a fun&ccedil;&atilde;o considerar&aacute; a &uacute;ltima aresta conectando o &uacute;ltimo ponto de volta \n",
    "# ao primeiro ponto.\n",
    "# False Indica que o contorno &eacute; uma curva aberta, e a fun&ccedil;&atilde;o n&atilde;o conectar&aacute; o &uacute;ltimo ponto de volta ao primeiro ponto.\n",
    "\n",
    "print(f\"Per&iacute;metro: {perimeter}\")\n",
    "\n",
    "# Criar uma imagem em branco (preta) do mesmo tamanho da imagem original\n",
    "img_first_contour = np.zeros_like(img)\n",
    "\n",
    "# Desenhar o primeiro contorno na imagem em branco\n",
    "cv2.drawContours(img_first_contour, [contours[0]], -1, (255, 255, 255), 1)\n",
    "\n",
    "# Exibir a imagem original e a imagem com o primeiro contorno desenhado\n",
    "showImages([img_gray, img_first_contour], [\"Original\", \"Primeiro Contorno\"], size=(7, 4), grid=(1, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "24bfbed5-fe4a-4b9e-be8c-fc498007d9f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2100x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcular o convexHull\n",
    "hull = cv2.convexHull(contours[0])\n",
    "\n",
    "# Criar uma imagem vazia (preta) do mesmo tamanho da imagem original\n",
    "img_contour_hull = np.zeros_like(img)\n",
    "\n",
    "# Desenhar o primeiro contorno na imagem em branco\n",
    "cv2.drawContours(img_contour_hull, [contours[0]], -1, (255, 0, 0), 1)\n",
    "\n",
    "# Desenhar o convexHull na imagem em branco\n",
    "cv2.drawContours(img_contour_hull, [hull], -1, (0, 255, 0), 1)\n",
    "\n",
    "# Exibir a imagem original, a imagem com o primeiro contorno e a imagem com o convexHull\n",
    "showImages([img_gray, thresh, img_contour_hull], [\"Original\", \"Binariza&ccedil;&atilde;o\", \"Convex Hull\"], size=(21, 7), grid=(1, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "55b301cf-f401-4900-a8a4-e1fafc27b699",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2100x700 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcular o ret&acirc;ngulo envolvente\n",
    "x, y, w, h = cv2.boundingRect(contours[0])\n",
    "\n",
    "# Calcular o ret&acirc;ngulo envolvente rotacionado\n",
    "rect = cv2.minAreaRect(contours[0])\n",
    "box = cv2.boxPoints(rect)\n",
    "box = np.intp(box)\n",
    "\n",
    "# Criar uma imagem em branco (preta) do mesmo tamanho da imagem original\n",
    "img_combined = np.zeros_like(img)\n",
    "\n",
    "# Desenhar o primeiro contorno na imagem em branco\n",
    "cv2.drawContours(img_combined, [contours[0]], -1, (255, 0, 0), 1)\n",
    "\n",
    "# Desenhar o ret&acirc;ngulo envolvente\n",
    "cv2.rectangle(img_combined, (x, y), (x+w, y+h), (0, 255, 0), 1)\n",
    "\n",
    "# Desenhar o ret&acirc;ngulo envolvente rotacionado\n",
    "cv2.drawContours(img_combined, [box], 0, (0, 0, 255), 1)\n",
    "\n",
    "# Exibir a imagem original, a imagem binarizada, a imagem com o ret&acirc;ngulo envolvente e a imagem com o ret&acirc;ngulo envolvente rotacionado\n",
    "showImages([img_gray, thresh, img_combined], [\"Original\", \"Binariza&ccedil;&atilde;o\", \"Caixas Envolventes\"], size=(21, 7), grid=(1, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c4adcc-1f57-4664-8d97-eada664b1c1b",
   "metadata": {},
   "source": [
    "# Reconhecer objetos geometricamente semelhantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e86811b0-7557-443e-9c4a-32e95677a771",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A imagem mais semelhante &eacute;: abra.png com similaridade de 0.0\n",
      "\n",
      "Rank 1: abra.png com similaridade de 0.0\n",
      "Rank 2: sunkern.png com similaridade de 0.043976875145353145\n",
      "Rank 3: pignite.png com similaridade de 0.0685534298976277\n",
      "Rank 4: deoxys-normal.png com similaridade de 0.11661919485170458\n",
      "Rank 5: tapu-bulu.png com similaridade de 0.12431323277563305\n",
      "Rank 6: volcanion.png com similaridade de 0.12882279825691184\n",
      "Rank 7: hypno.png com similaridade de 0.13364877234497818\n",
      "Rank 8: tauros.png com similaridade de 0.13621736633230364\n",
      "Rank 9: probopass.png com similaridade de 0.1453889885688076\n",
      "Rank 10: musharna.png com similaridade de 0.14681877825283826\n",
      "Rank 11: aipom.png com similaridade de 0.1479295544471227\n",
      "Rank 12: wooper.png com similaridade de 0.154207623758586\n",
      "Rank 13: alakazam.png com similaridade de 0.1591065876789547\n",
      "Rank 14: victreebel.png com similaridade de 0.16006804624661353\n",
      "Rank 15: ho-oh.png com similaridade de 0.1722673967100083\n",
      "Rank 16: baltoy.png com similaridade de 0.17228527658592013\n",
      "Rank 17: wynaut.png com similaridade de 0.17458769978411393\n",
      "Rank 18: jirachi.png com similaridade de 0.17620925592889736\n",
      "Rank 19: porygon-z.png com similaridade de 0.17795810749965613\n",
      "Rank 20: swampert.png com similaridade de 0.17881371293717357\n",
      "Rank 21: snivy.png com similaridade de 0.18014833160535726\n",
      "Rank 22: spinda.png com similaridade de 0.18196125872406665\n",
      "Rank 23: octillery.png com similaridade de 0.18940365040421875\n",
      "Rank 24: greninja.png com similaridade de 0.18983341532842235\n",
      "Rank 25: brionne.png com similaridade de 0.19014608823771484\n",
      "Rank 26: cyndaquil.png com similaridade de 0.1907067828169709\n",
      "Rank 27: ekans.png com similaridade de 0.2027459844910463\n",
      "Rank 28: toxapex.png com similaridade de 0.20407811469324771\n",
      "Rank 29: gligar.png com similaridade de 0.20537562105714613\n",
      "Rank 30: charizard.png com similaridade de 0.20555237715256997\n",
      "Rank 31: trubbish.png com similaridade de 0.20626071811479457\n",
      "Rank 32: seadra.png com similaridade de 0.20639685583457013\n",
      "Rank 33: primarina.png com similaridade de 0.20640299847590443\n",
      "Rank 34: eevee.png com similaridade de 0.20664409720311305\n",
      "Rank 35: venomoth.png com similaridade de 0.2070552759236738\n",
      "Rank 36: remoraid.png com similaridade de 0.2108735338768994\n",
      "Rank 37: sandygast.png com similaridade de 0.21225134408827284\n",
      "Rank 38: trevenant.png com similaridade de 0.21267854493411292\n",
      "Rank 39: phantump.png com similaridade de 0.2147222990052236\n",
      "Rank 40: stakataka.png com similaridade de 0.2187754881144628\n",
      "Rank 41: furret.png com similaridade de 0.21918301746648194\n",
      "Rank 42: electabuzz.png com similaridade de 0.22131651811438124\n",
      "Rank 43: entei.png com similaridade de 0.22219645093590346\n",
      "Rank 44: darmanitan-standard.png com similaridade de 0.22281982403299205\n",
      "Rank 45: weepinbell.png com similaridade de 0.2242738978912649\n",
      "Rank 46: mankey.png com similaridade de 0.22515329761973962\n",
      "Rank 47: staryu.png com similaridade de 0.22527820372131813\n",
      "Rank 48: hydreigon.png com similaridade de 0.22625013396113863\n",
      "Rank 49: druddigon.png com similaridade de 0.22738228379698716\n",
      "Rank 50: meditite.png com similaridade de 0.2276874390498842\n",
      "Rank 51: passimian.png com similaridade de 0.2277719091647411\n",
      "Rank 52: heracross.png com similaridade de 0.22844440669923016\n",
      "Rank 53: noctowl.png com similaridade de 0.2311367140411686\n",
      "Rank 54: escavalier.png com similaridade de 0.2314976686319019\n",
      "Rank 55: milotic.png com similaridade de 0.23234726762734603\n",
      "Rank 56: dusknoir.png com similaridade de 0.23284168569576458\n",
      "Rank 57: trumbeak.png com similaridade de 0.23314033885646473\n",
      "Rank 58: gulpin.png com similaridade de 0.23640245884139094\n",
      "Rank 59: rattata.png com similaridade de 0.23665551472820973\n",
      "Rank 60: shiinotic.png com similaridade de 0.23744654990327074\n",
      "Rank 61: skitty.png com similaridade de 0.241790523616706\n",
      "Rank 62: mamoswine.png com similaridade de 0.24361503313269728\n",
      "Rank 63: seviper.png com similaridade de 0.2453867507061484\n",
      "Rank 64: electivire.png com similaridade de 0.24614171950940217\n",
      "Rank 65: drilbur.png com similaridade de 0.24782576631245834\n",
      "Rank 66: accelgor.png com similaridade de 0.2499601541384358\n",
      "Rank 67: pelipper.png com similaridade de 0.2504474835763838\n",
      "Rank 68: vileplume.png com similaridade de 0.25231410384824277\n",
      "Rank 69: hoothoot.png com similaridade de 0.2528461417465152\n",
      "Rank 70: maractus.png com similaridade de 0.253963955002405\n",
      "Rank 71: kadabra.png com similaridade de 0.2549532122306226\n",
      "Rank 72: dartrix.png com similaridade de 0.25620713853792354\n",
      "Rank 73: cosmog.png com similaridade de 0.25781399023557033\n",
      "Rank 74: misdreavus.png com similaridade de 0.2579637362841871\n",
      "Rank 75: nosepass.png com similaridade de 0.25883271674788444\n",
      "Rank 76: dewgong.png com similaridade de 0.2596271762905492\n",
      "Rank 77: seel.png com similaridade de 0.26087726562765573\n",
      "Rank 78: reshiram.png com similaridade de 0.2645780151584861\n",
      "Rank 79: golett.png com similaridade de 0.26605685884906577\n",
      "Rank 80: frogadier.png com similaridade de 0.2676852376210763\n",
      "Rank 81: larvitar.png com similaridade de 0.2688460596190918\n",
      "Rank 82: buzzwole.png com similaridade de 0.26894313075502657\n",
      "Rank 83: tapu-koko.png com similaridade de 0.2696127167261084\n",
      "Rank 84: gothita.png com similaridade de 0.2719429397300109\n",
      "Rank 85: hoopa-confined.png com similaridade de 0.2719435653970753\n",
      "Rank 86: chesnaught.png com similaridade de 0.2721697737901045\n",
      "Rank 87: barbaracle.png com similaridade de 0.2737400243860816\n",
      "Rank 88: porygon.png com similaridade de 0.2738611325425399\n",
      "Rank 89: snover.png com similaridade de 0.2756570147769314\n",
      "Rank 90: fennekin.png com similaridade de 0.27573221281075244\n",
      "Rank 91: porygon2.png com similaridade de 0.27599965043468766\n",
      "Rank 92: loudred.png com similaridade de 0.2770407295780333\n",
      "Rank 93: kingler.png com similaridade de 0.2792417170857342\n",
      "Rank 94: pikachu.png com similaridade de 0.2795936220721529\n",
      "Rank 95: pineco.png com similaridade de 0.27978999808571137\n",
      "Rank 96: bisharp.png com similaridade de 0.28238387365850015\n",
      "Rank 97: pichu.png com similaridade de 0.2868994495824119\n",
      "Rank 98: pinsir.png com similaridade de 0.28948119637212744\n",
      "Rank 99: archen.png com similaridade de 0.2929804053230046\n",
      "Rank 100: slowpoke.png com similaridade de 0.2948176882462842\n",
      "Rank 101: elekid.png com similaridade de 0.2952241439309782\n",
      "Rank 102: nuzleaf.png com similaridade de 0.29678966998222245\n",
      "Rank 103: braixen.png com similaridade de 0.29807986315360724\n",
      "Rank 104: froakie.png com similaridade de 0.29951690918551277\n",
      "Rank 105: crabrawler.png com similaridade de 0.2996717671059488\n",
      "Rank 106: zeraora.png com similaridade de 0.2997057956562045\n",
      "Rank 107: bruxish.png com similaridade de 0.301195811155533\n",
      "Rank 108: nidoran-m.png com similaridade de 0.30258664553899484\n",
      "Rank 109: huntail.png com similaridade de 0.3030720105027406\n",
      "Rank 110: uxie.png com similaridade de 0.3031923519563921\n",
      "Rank 111: diglett.png com similaridade de 0.303663823428694\n",
      "Rank 112: manaphy.png com similaridade de 0.30379240051344303\n",
      "Rank 113: minior-meteor.png com similaridade de 0.3070288613771628\n",
      "Rank 114: minccino.png com similaridade de 0.30763249624071914\n",
      "Rank 115: registeel.png com similaridade de 0.3099614023733798\n",
      "Rank 116: kartana.png com similaridade de 0.31026937651433806\n",
      "Rank 117: sharpedo.png com similaridade de 0.31079279297840395\n",
      "Rank 118: swadloon.png com similaridade de 0.31125157946697235\n",
      "Rank 119: luxio.png com similaridade de 0.311320319112587\n",
      "Rank 120: grumpig.png com similaridade de 0.3117293627920924\n",
      "Rank 121: machamp.png com similaridade de 0.3119263948658035\n",
      "Rank 122: hawlucha.png com similaridade de 0.3130746704783893\n",
      "Rank 123: cacturne.png com similaridade de 0.3158958199070645\n",
      "Rank 124: muk.png com similaridade de 0.31601259378874147\n",
      "Rank 125: blitzle.png com similaridade de 0.317450425072523\n",
      "Rank 126: turtwig.png com similaridade de 0.31766144467246804\n",
      "Rank 127: beedrill.png com similaridade de 0.31867971655396704\n",
      "Rank 128: cryogonal.png com similaridade de 0.3194823400694755\n",
      "Rank 129: stunfisk.png com similaridade de 0.3195594124292263\n",
      "Rank 130: giratina-altered.png com similaridade de 0.32293402263608734\n",
      "Rank 131: mandibuzz.png com similaridade de 0.32336828404095613\n",
      "Rank 132: clauncher.png com similaridade de 0.32495750599260953\n",
      "Rank 133: floatzel.png com similaridade de 0.3251022217106502\n",
      "Rank 134: volcarona.png com similaridade de 0.32559792182272795\n",
      "Rank 135: starmie.png com similaridade de 0.32635175400770067\n",
      "Rank 136: rapidash.png com similaridade de 0.33006255391605455\n",
      "Rank 137: voltorb.png com similaridade de 0.33036869412583525\n",
      "Rank 138: cubchoo.png com similaridade de 0.3308015980773408\n",
      "Rank 139: vivillon.png com similaridade de 0.331415146186352\n",
      "Rank 140: torracat.png com similaridade de 0.334151752322786\n",
      "Rank 141: rhyperior.png com similaridade de 0.3344384142905715\n",
      "Rank 142: chingling.png com similaridade de 0.33452595132075647\n",
      "Rank 143: conkeldurr.png com similaridade de 0.3347883267940869\n",
      "Rank 144: popplio.png com similaridade de 0.3353674329911214\n",
      "Rank 145: steenee.png com similaridade de 0.33647837295008015\n",
      "Rank 146: haunter.png com similaridade de 0.33939936673606347\n",
      "Rank 147: vulpix.png com similaridade de 0.3395687513256007\n",
      "Rank 148: herdier.png com similaridade de 0.34018730264130437\n",
      "Rank 149: delibird.png com similaridade de 0.3411310311100838\n",
      "Rank 150: oddish.png com similaridade de 0.34123238432173514\n",
      "Rank 151: mesprit.png com similaridade de 0.34181024153462697\n",
      "Rank 152: dragonite.png com similaridade de 0.3419336183993334\n",
      "Rank 153: whismur.png com similaridade de 0.34210232453542083\n",
      "Rank 154: gabite.png com similaridade de 0.3452514635671566\n",
      "Rank 155: gourgeist-average.png com similaridade de 0.3478914799829469\n",
      "Rank 156: bergmite.png com similaridade de 0.3481392300169495\n",
      "Rank 157: solosis.png com similaridade de 0.349205333969183\n",
      "Rank 158: sceptile.png com similaridade de 0.35278847754337755\n",
      "Rank 159: tangrowth.png com similaridade de 0.35332724902595725\n",
      "Rank 160: zoroark.png com similaridade de 0.3538203986864054\n",
      "Rank 161: teddiursa.png com similaridade de 0.354929767367759\n",
      "Rank 162: gigalith.png com similaridade de 0.35551136753481266\n",
      "Rank 163: politoed.png com similaridade de 0.356339356667969\n",
      "Rank 164: breloom.png com similaridade de 0.35700050575885717\n",
      "Rank 165: nidorino.png com similaridade de 0.35708089257305536\n",
      "Rank 166: aggron.png com similaridade de 0.35742679611741274\n",
      "Rank 167: makuhita.png com similaridade de 0.3584260085191169\n",
      "Rank 168: unown.png com similaridade de 0.3594330850906346\n",
      "Rank 169: heatmor.png com similaridade de 0.3600372382465252\n",
      "Rank 170: mewtwo.png com similaridade de 0.3609938776814888\n",
      "Rank 171: zapdos.png com similaridade de 0.3611903099412325\n",
      "Rank 172: beautifly.png com similaridade de 0.36124616721376973\n",
      "Rank 173: amoonguss.png com similaridade de 0.3620000613654776\n",
      "Rank 174: lurantis.png com similaridade de 0.3620222847835999\n",
      "Rank 175: abomasnow.png com similaridade de 0.36307946308815087\n",
      "Rank 176: manectric.png com similaridade de 0.36433480313077327\n",
      "Rank 177: golisopod.png com similaridade de 0.3644858453616813\n",
      "Rank 178: butterfree.png com similaridade de 0.36449895065551857\n",
      "Rank 179: leafeon.png com similaridade de 0.36455492913575366\n",
      "Rank 180: venusaur.png com similaridade de 0.3655252373903709\n",
      "Rank 181: haxorus.png com similaridade de 0.3656020525753569\n",
      "Rank 182: kabuto.png com similaridade de 0.36630064907528326\n",
      "Rank 183: croagunk.png com similaridade de 0.36645110702458183\n",
      "Rank 184: kommo-o.png com similaridade de 0.366739587136257\n",
      "Rank 185: palkia.png com similaridade de 0.3676575719614425\n",
      "Rank 186: salamence.png com similaridade de 0.36856778083956654\n",
      "Rank 187: sableye.png com similaridade de 0.37024734043278423\n",
      "Rank 188: cherubi.png com similaridade de 0.37033549467144355\n",
      "Rank 189: vaporeon.png com similaridade de 0.3706653260417027\n",
      "Rank 190: corphish.png com similaridade de 0.3708560195914544\n",
      "Rank 191: mudkip.png com similaridade de 0.37202294759296295\n",
      "Rank 192: wigglytuff.png com similaridade de 0.3724207754271214\n",
      "Rank 193: corsola.png com similaridade de 0.37257075016638064\n",
      "Rank 194: castform.png com similaridade de 0.3727505409737118\n",
      "Rank 195: wurmple.png com similaridade de 0.3732150249323131\n",
      "Rank 196: dugtrio.png com similaridade de 0.37363089226192114\n",
      "Rank 197: primeape.png com similaridade de 0.3748523304802099\n",
      "Rank 198: farfetchd.png com similaridade de 0.37559718091283417\n",
      "Rank 199: rampardos.png com similaridade de 0.37652183242588966\n",
      "Rank 200: simisage.png com similaridade de 0.3776704242029071\n",
      "Rank 201: gliscor.png com similaridade de 0.3801984883818578\n",
      "Rank 202: slugma.png com similaridade de 0.3803643611978161\n",
      "Rank 203: cosmoem.png com similaridade de 0.3818551813949212\n",
      "Rank 204: bronzong.png com similaridade de 0.38274455392067797\n",
      "Rank 205: slowbro.png com similaridade de 0.3828653306311089\n",
      "Rank 206: azumarill.png com similaridade de 0.38302169170938954\n",
      "Rank 207: shroomish.png com similaridade de 0.3838314122684876\n",
      "Rank 208: solgaleo.png com similaridade de 0.3843542338306408\n",
      "Rank 209: poliwrath.png com similaridade de 0.3848849735331403\n",
      "Rank 210: toucannon.png com similaridade de 0.38565420879459583\n",
      "Rank 211: meowstic-male.png com similaridade de 0.38576428608624563\n",
      "Rank 212: thundurus-incarnate.png com similaridade de 0.38676247287974747\n",
      "Rank 213: lileep.png com similaridade de 0.38704433509790126\n",
      "Rank 214: jumpluff.png com similaridade de 0.38927435890099793\n",
      "Rank 215: espeon.png com similaridade de 0.3894227638067699\n",
      "Rank 216: phione.png com similaridade de 0.39006984464115657\n",
      "Rank 217: spritzee.png com similaridade de 0.3905901858945473\n",
      "Rank 218: snorunt.png com similaridade de 0.3908167897619119\n",
      "Rank 219: gastrodon.png com similaridade de 0.39099069947925064\n",
      "Rank 220: lopunny.png com similaridade de 0.3910609595622186\n",
      "Rank 221: groudon.png com similaridade de 0.3913248866673095\n",
      "Rank 222: parasect.png com similaridade de 0.391481233923861\n",
      "Rank 223: duskull.png com similaridade de 0.39154417454786267\n",
      "Rank 224: solrock.png com similaridade de 0.39177328211664475\n",
      "Rank 225: whiscash.png com similaridade de 0.39188457507466057\n",
      "Rank 226: feebas.png com similaridade de 0.39194968590546186\n",
      "Rank 227: shedinja.png com similaridade de 0.39221819993356977\n",
      "Rank 228: omastar.png com similaridade de 0.3922415209404063\n",
      "Rank 229: aegislash-blade.png com similaridade de 0.3923001789021337\n",
      "Rank 230: golduck.png com similaridade de 0.3927789096208008\n",
      "Rank 231: mudbray.png com similaridade de 0.39299751552755585\n",
      "Rank 232: charmeleon.png com similaridade de 0.3930211647283592\n",
      "Rank 233: ducklett.png com similaridade de 0.39374568101054347\n",
      "Rank 234: vespiquen.png com similaridade de 0.393747037803339\n",
      "Rank 235: sentret.png com similaridade de 0.394543883254782\n",
      "Rank 236: delcatty.png com similaridade de 0.395056917947832\n",
      "Rank 237: magmar.png com similaridade de 0.3953986618517249\n",
      "Rank 238: vigoroth.png com similaridade de 0.39612140723473094\n",
      "Rank 239: krookodile.png com similaridade de 0.39621199176810745\n",
      "Rank 240: swalot.png com similaridade de 0.3963362245010698\n",
      "Rank 241: silcoon.png com similaridade de 0.39647774526766916\n",
      "Rank 242: regigigas.png com similaridade de 0.39679648897337466\n",
      "Rank 243: wimpod.png com similaridade de 0.39832991366676324\n",
      "Rank 244: mime-jr.png com similaridade de 0.3994176322478567\n",
      "Rank 245: komala.png com similaridade de 0.39946847034602095\n",
      "Rank 246: hitmonlee.png com similaridade de 0.39954463450026256\n",
      "Rank 247: regirock.png com similaridade de 0.4006917250438669\n",
      "Rank 248: smeargle.png com similaridade de 0.4009371382927383\n",
      "Rank 249: togekiss.png com similaridade de 0.40106852396008624\n",
      "Rank 250: electrode.png com similaridade de 0.4020532306533832\n",
      "Rank 251: grubbin.png com similaridade de 0.4021256033188298\n",
      "Rank 252: togedemaru.png com similaridade de 0.4025728882692453\n",
      "Rank 253: lairon.png com similaridade de 0.4026179435525184\n",
      "Rank 254: prinplup.png com similaridade de 0.40339869847564086\n",
      "Rank 255: magneton.png com similaridade de 0.40347928614139394\n",
      "Rank 256: rayquaza.png com similaridade de 0.4039990470456005\n",
      "Rank 257: chinchou.png com similaridade de 0.4050045766780047\n",
      "Rank 258: lombre.png com similaridade de 0.40631513640819533\n",
      "Rank 259: seismitoad.png com similaridade de 0.4086378251608907\n",
      "Rank 260: lillipup.png com similaridade de 0.4091450968750672\n",
      "Rank 261: hitmontop.png com similaridade de 0.4092954323775273\n",
      "Rank 262: feraligatr.png com similaridade de 0.4100819270026651\n",
      "Rank 263: miltank.png com similaridade de 0.4106993413209355\n",
      "Rank 264: dusclops.png com similaridade de 0.41088423775827115\n",
      "Rank 265: cinccino.png com similaridade de 0.4109064225723693\n",
      "Rank 266: carracosta.png com similaridade de 0.41277520728771205\n",
      "Rank 267: magmortar.png com similaridade de 0.41357279962749216\n",
      "Rank 268: celesteela.png com similaridade de 0.41386198792147594\n",
      "Rank 269: walrein.png com similaridade de 0.41400413061907015\n",
      "Rank 270: kabutops.png com similaridade de 0.41409863196084074\n",
      "Rank 271: ponyta.png com similaridade de 0.4143219430503051\n",
      "Rank 272: horsea.png com similaridade de 0.41478901917267913\n",
      "Rank 273: nidoran-f.png com similaridade de 0.4147966582106713\n",
      "Rank 274: garchomp.png com similaridade de 0.4152948305143978\n",
      "Rank 275: litwick.png com similaridade de 0.4155241130634866\n",
      "Rank 276: jellicent.png com similaridade de 0.415701209913282\n",
      "Rank 277: torterra.png com similaridade de 0.4162091386122025\n",
      "Rank 278: roserade.png com similaridade de 0.4165169068941435\n",
      "Rank 279: poipole.png com similaridade de 0.4171562953098551\n",
      "Rank 280: toxicroak.png com similaridade de 0.41715976730850485\n",
      "Rank 281: quilava.png com similaridade de 0.4173053397054526\n",
      "Rank 282: cacnea.png com similaridade de 0.4180069132368101\n",
      "Rank 283: golem.png com similaridade de 0.4192268864705261\n",
      "Rank 284: salandit.png com similaridade de 0.4197508548114236\n",
      "Rank 285: stufful.png com similaridade de 0.42004647668776485\n",
      "Rank 286: grotle.png com similaridade de 0.42049409305193747\n",
      "Rank 287: bayleef.png com similaridade de 0.42132526846407625\n",
      "Rank 288: gible.png com similaridade de 0.42153887139988566\n",
      "Rank 289: volbeat.png com similaridade de 0.42204978357967227\n",
      "Rank 290: metapod.png com similaridade de 0.42365591811144376\n",
      "Rank 291: pancham.png com similaridade de 0.42397968892620685\n",
      "Rank 292: armaldo.png com similaridade de 0.4262614235578485\n",
      "Rank 293: tentacruel.png com similaridade de 0.42657080390518115\n",
      "Rank 294: klinklang.png com similaridade de 0.4267313891735034\n",
      "Rank 295: cresselia.png com similaridade de 0.4270045504563318\n",
      "Rank 296: lotad.png com similaridade de 0.4281856248279098\n",
      "Rank 297: suicune.png com similaridade de 0.42830592698784936\n",
      "Rank 298: zekrom.png com similaridade de 0.4284524900560102\n",
      "Rank 299: wormadam-plant.png com similaridade de 0.42882521339565327\n",
      "Rank 300: glameow.png com similaridade de 0.4289457116644107\n",
      "Rank 301: mareanie.png com similaridade de 0.4292396949800389\n",
      "Rank 302: simipour.png com similaridade de 0.4298187335487019\n",
      "Rank 303: simisear.png com similaridade de 0.4304187129458813\n",
      "Rank 304: monferno.png com similaridade de 0.4305227291632123\n",
      "Rank 305: rockruff.png com similaridade de 0.430563933330286\n",
      "Rank 306: typhlosion.png com similaridade de 0.4309069549396012\n",
      "Rank 307: kricketot.png com similaridade de 0.4309302299681198\n",
      "Rank 308: honedge.png com similaridade de 0.4317220520616528\n",
      "Rank 309: persian.png com similaridade de 0.43248209441692004\n",
      "Rank 310: pumpkaboo-average.png com similaridade de 0.4331108589976191\n",
      "Rank 311: litleo.png com similaridade de 0.43324178888753473\n",
      "Rank 312: carvanha.png com similaridade de 0.4332424749332045\n",
      "Rank 313: tepig.png com similaridade de 0.43387812016200134\n",
      "Rank 314: mienfoo.png com similaridade de 0.4354186912218414\n",
      "Rank 315: poochyena.png com similaridade de 0.43599458944715985\n",
      "Rank 316: timburr.png com similaridade de 0.43601526111653005\n",
      "Rank 317: hariyama.png com similaridade de 0.43618298191113025\n",
      "Rank 318: cloyster.png com similaridade de 0.437135480441762\n",
      "Rank 319: gogoat.png com similaridade de 0.43759054307674156\n",
      "Rank 320: quilladin.png com similaridade de 0.4384568047192831\n",
      "Rank 321: machop.png com similaridade de 0.4393027154351649\n",
      "Rank 322: croconaw.png com similaridade de 0.4394473317596798\n",
      "Rank 323: nidoking.png com similaridade de 0.4396887717276939\n",
      "Rank 324: sewaddle.png com similaridade de 0.4397448452716823\n",
      "Rank 325: tsareena.png com similaridade de 0.439806220996372\n",
      "Rank 326: joltik.png com similaridade de 0.4405788578660395\n",
      "Rank 327: exploud.png com similaridade de 0.4412504940074684\n",
      "Rank 328: mantyke.png com similaridade de 0.44134349086677327\n",
      "Rank 329: exeggutor.png com similaridade de 0.4413861721027145\n",
      "Rank 330: kangaskhan.png com similaridade de 0.4420300887573728\n",
      "Rank 331: zygarde-50.png com similaridade de 0.4423751430439328\n",
      "Rank 332: burmy.png com similaridade de 0.4423902329858147\n",
      "Rank 333: celebi.png com similaridade de 0.4427995728757838\n",
      "Rank 334: mudsdale.png com similaridade de 0.4430326341679347\n",
      "Rank 335: yungoos.png com similaridade de 0.4430433879990577\n",
      "Rank 336: landorus-incarnate.png com similaridade de 0.4431128433939421\n",
      "Rank 337: binacle.png com similaridade de 0.44333353472607634\n",
      "Rank 338: ledyba.png com similaridade de 0.4437932797272598\n",
      "Rank 339: foongus.png com similaridade de 0.44399771653238407\n",
      "Rank 340: bounsweet.png com similaridade de 0.4442979743787461\n",
      "Rank 341: drifloon.png com similaridade de 0.4457568607699493\n",
      "Rank 342: buizel.png com similaridade de 0.4458638950279123\n",
      "Rank 343: axew.png com similaridade de 0.4466390529351243\n",
      "Rank 344: tornadus-incarnate.png com similaridade de 0.4466774585958093\n",
      "Rank 345: inkay.png com similaridade de 0.44673401767254733\n",
      "Rank 346: drampa.png com similaridade de 0.44697189133465554\n",
      "Rank 347: wartortle.png com similaridade de 0.44712405558038637\n",
      "Rank 348: kricketune.png com similaridade de 0.44722020415613895\n",
      "Rank 349: kirlia.png com similaridade de 0.4478651190579749\n",
      "Rank 350: ivysaur.png com similaridade de 0.4499377993203693\n",
      "Rank 351: bastiodon.png com similaridade de 0.4504184323585067\n",
      "Rank 352: aron.png com similaridade de 0.4504673091073203\n",
      "Rank 353: tapu-fini.png com similaridade de 0.4505675565294608\n",
      "Rank 354: roselia.png com similaridade de 0.4510725831172833\n",
      "Rank 355: tyranitar.png com similaridade de 0.45235939151455123\n",
      "Rank 356: fletchling.png com similaridade de 0.45324931657269074\n",
      "Rank 357: vullaby.png com similaridade de 0.45386978238800074\n",
      "Rank 358: sandshrew.png com similaridade de 0.4550044451222025\n",
      "Rank 359: clawitzer.png com similaridade de 0.45549966414011983\n",
      "Rank 360: houndour.png com similaridade de 0.45556160935161993\n",
      "Rank 361: machoke.png com similaridade de 0.45601072684694877\n",
      "Rank 362: meganium.png com similaridade de 0.45644200234248844\n",
      "Rank 363: illumise.png com similaridade de 0.45671657833906043\n",
      "Rank 364: zangoose.png com similaridade de 0.4572567117176179\n",
      "Rank 365: petilil.png com similaridade de 0.45748510739110493\n",
      "Rank 366: fearow.png com similaridade de 0.4575035077411869\n",
      "Rank 367: budew.png com similaridade de 0.4576024663754141\n",
      "Rank 368: munchlax.png com similaridade de 0.4579561824569191\n",
      "Rank 369: chespin.png com similaridade de 0.45809883032427917\n",
      "Rank 370: magnemite.png com similaridade de 0.4586150063780213\n",
      "Rank 371: poliwag.png com similaridade de 0.45868658121169825\n",
      "Rank 372: paras.png com similaridade de 0.4599180648864354\n",
      "Rank 373: sealeo.png com similaridade de 0.46065735949749825\n",
      "Rank 374: squirtle.png com similaridade de 0.4609083788534045\n",
      "Rank 375: lycanroc-midday.png com similaridade de 0.4622662446019166\n",
      "Rank 376: zubat.png com similaridade de 0.46266785776673186\n",
      "Rank 377: turtonator.png com similaridade de 0.4634863335651602\n",
      "Rank 378: mimikyu.png com similaridade de 0.46377788089987854\n",
      "Rank 379: melmetal.png com similaridade de 0.46383913413140154\n",
      "Rank 380: beartic.png com similaridade de 0.46485747786709053\n",
      "Rank 381: poliwhirl.png com similaridade de 0.46576105914335886\n",
      "Rank 382: bellossom.png com similaridade de 0.4664147486794289\n",
      "Rank 383: grovyle.png com similaridade de 0.466625549621535\n",
      "Rank 384: garbodor.png com similaridade de 0.4667390898481155\n",
      "Rank 385: ambipom.png com similaridade de 0.4667585912994613\n",
      "Rank 386: sandslash.png com similaridade de 0.4674125465953381\n",
      "Rank 387: lanturn.png com similaridade de 0.4678497686809752\n",
      "Rank 388: lapras.png com similaridade de 0.4683132015217364\n",
      "Rank 389: nidorina.png com similaridade de 0.46835235427313604\n",
      "Rank 390: chimchar.png com similaridade de 0.4685741695637603\n",
      "Rank 391: audino.png com similaridade de 0.46871510633797403\n",
      "Rank 392: crustle.png com similaridade de 0.4689011765103451\n",
      "Rank 393: ribombee.png com similaridade de 0.4689632143337595\n",
      "Rank 394: rhydon.png com similaridade de 0.47007627900212445\n",
      "Rank 395: infernape.png com similaridade de 0.47017920815131925\n",
      "Rank 396: skiploom.png com similaridade de 0.47033232358510246\n",
      "Rank 397: goomy.png com similaridade de 0.47116707809776875\n",
      "Rank 398: piloswine.png com similaridade de 0.4714968809619396\n",
      "Rank 399: chansey.png com similaridade de 0.47364505085783304\n",
      "Rank 400: clefable.png com similaridade de 0.4740515725709891\n",
      "Rank 401: seaking.png com similaridade de 0.47419148857221527\n",
      "Rank 402: litten.png com similaridade de 0.4743259615384434\n",
      "Rank 403: sunflora.png com similaridade de 0.47449543819105433\n",
      "Rank 404: cofagrigus.png com similaridade de 0.4751320870760126\n",
      "Rank 405: zorua.png com similaridade de 0.47560492584637803\n",
      "Rank 406: hakamo-o.png com similaridade de 0.4761978261038613\n",
      "Rank 407: happiny.png com similaridade de 0.4763771369474732\n",
      "Rank 408: drifblim.png com similaridade de 0.47705995263137135\n",
      "Rank 409: karrablast.png com similaridade de 0.47765968858743624\n",
      "Rank 410: murkrow.png com similaridade de 0.4776970076337921\n",
      "Rank 411: nidoqueen.png com similaridade de 0.47773405594603724\n",
      "Rank 412: rhyhorn.png com similaridade de 0.47792332790876957\n",
      "Rank 413: wishiwashi-solo.png com similaridade de 0.47836156094325977\n",
      "Rank 414: marowak.png com similaridade de 0.4787538325885999\n",
      "Rank 415: kyogre.png com similaridade de 0.47891856717210407\n",
      "Rank 416: forretress.png com similaridade de 0.4793981456878882\n",
      "Rank 417: magby.png com similaridade de 0.4798402436588044\n",
      "Rank 418: swoobat.png com similaridade de 0.479977751335627\n",
      "Rank 419: rowlet.png com similaridade de 0.4802581636092751\n",
      "Rank 420: linoone.png com similaridade de 0.4802807169075423\n",
      "Rank 421: dustox.png com similaridade de 0.48057585957761645\n",
      "Rank 422: vanilluxe.png com similaridade de 0.480599593855923\n",
      "Rank 423: terrakion.png com similaridade de 0.4807033028782805\n",
      "Rank 424: luxray.png com similaridade de 0.48102174083065574\n",
      "Rank 425: jangmo-o.png com similaridade de 0.4810956983219377\n",
      "Rank 426: archeops.png com similaridade de 0.48112303507637033\n",
      "Rank 427: meowth.png com similaridade de 0.4817049134910798\n",
      "Rank 428: bibarel.png com similaridade de 0.48270884272000114\n",
      "Rank 429: granbull.png com similaridade de 0.48287822419898835\n",
      "Rank 430: espurr.png com similaridade de 0.48303870466609994\n",
      "Rank 431: nincada.png com similaridade de 0.4836083599944336\n",
      "Rank 432: jolteon.png com similaridade de 0.4839058605265643\n",
      "Rank 433: relicanth.png com similaridade de 0.4840282945925168\n",
      "Rank 434: bulbasaur.png com similaridade de 0.4842522816728835\n",
      "Rank 435: riolu.png com similaridade de 0.48431275871275553\n",
      "Rank 436: flaaffy.png com similaridade de 0.48444463207612465\n",
      "Rank 437: incineroar.png com similaridade de 0.48484090272335156\n",
      "Rank 438: pyukumuku.png com similaridade de 0.48494241512216335\n",
      "Rank 439: helioptile.png com similaridade de 0.48512387584542155\n",
      "Rank 440: purugly.png com similaridade de 0.48514798353029936\n",
      "Rank 441: palossand.png com similaridade de 0.48573725173581567\n",
      "Rank 442: pansear.png com similaridade de 0.48583984314429807\n",
      "Rank 443: skorupi.png com similaridade de 0.48597083816355463\n",
      "Rank 444: swinub.png com similaridade de 0.48600835259885306\n",
      "Rank 445: basculin-red-striped.png com similaridade de 0.48635248203378745\n",
      "Rank 446: totodile.png com similaridade de 0.48663994284909795\n",
      "Rank 447: zweilous.png com similaridade de 0.4871323402314143\n",
      "Rank 448: mr-mime.png com similaridade de 0.487607259179297\n",
      "Rank 449: fraxure.png com similaridade de 0.4877948796960694\n",
      "Rank 450: flygon.png com similaridade de 0.4878323680560677\n",
      "Rank 451: weavile.png com similaridade de 0.4880104562592071\n",
      "Rank 452: slowking.png com similaridade de 0.48812262347105\n",
      "Rank 453: carbink.png com similaridade de 0.48859279562898356\n",
      "Rank 454: decidueye.png com similaridade de 0.48864511041999187\n",
      "Rank 455: gengar.png com similaridade de 0.4893831059178073\n",
      "Rank 456: combusken.png com similaridade de 0.4899827050236104\n",
      "Rank 457: pidove.png com similaridade de 0.491672444239949\n",
      "Rank 458: pangoro.png com similaridade de 0.49176522400460915\n",
      "Rank 459: yamask.png com similaridade de 0.4918739685248208\n",
      "Rank 460: weedle.png com similaridade de 0.49213099775129343\n",
      "Rank 461: avalugg.png com similaridade de 0.49261976722631795\n",
      "Rank 462: banette.png com similaridade de 0.49325264558310117\n",
      "Rank 463: crabominable.png com similaridade de 0.49349614919963863\n",
      "Rank 464: snubbull.png com similaridade de 0.4943936588384222\n",
      "Rank 465: slurpuff.png com similaridade de 0.4952468667383384\n",
      "Rank 466: graveler.png com similaridade de 0.4953394787538945\n",
      "Rank 467: igglybuff.png com similaridade de 0.4962402232047471\n",
      "Rank 468: drapion.png com similaridade de 0.4964457867212775\n",
      "Rank 469: ferroseed.png com similaridade de 0.49673895436935356\n",
      "Rank 470: tropius.png com similaridade de 0.4968670188473113\n",
      "Rank 471: tranquill.png com similaridade de 0.4969270904466295\n",
      "Rank 472: scatterbug.png com similaridade de 0.4971255497546562\n",
      "Rank 473: mawile.png com similaridade de 0.4971435843380976\n",
      "Rank 474: spewpa.png com similaridade de 0.4974724344051072\n",
      "Rank 475: araquanid.png com similaridade de 0.4975764733748639\n",
      "Rank 476: shelmet.png com similaridade de 0.4982486616372843\n",
      "Rank 477: sudowoodo.png com similaridade de 0.4997158278276534\n",
      "Rank 478: latios.png com similaridade de 0.49976242294478634\n",
      "Rank 479: magikarp.png com similaridade de 0.5000889615527758\n",
      "Rank 480: psyduck.png com similaridade de 0.5019077395014151\n",
      "Rank 481: jigglypuff.png com similaridade de 0.5021884017152094\n",
      "Rank 482: blastoise.png com similaridade de 0.5026535804186774\n",
      "Rank 483: cherrim.png com similaridade de 0.5043156079117683\n",
      "Rank 484: slaking.png com similaridade de 0.505101574542555\n",
      "Rank 485: shaymin-land.png com similaridade de 0.5057886303998549\n",
      "Rank 486: snorlax.png com similaridade de 0.5059624291031031\n",
      "Rank 487: hippopotas.png com similaridade de 0.5067166810411804\n",
      "Rank 488: qwilfish.png com similaridade de 0.5077494240217878\n",
      "Rank 489: lickitung.png com similaridade de 0.5087777883076544\n",
      "Rank 490: tyrunt.png com similaridade de 0.5091443541928883\n",
      "Rank 491: grimer.png com similaridade de 0.5092940215683981\n",
      "Rank 492: klang.png com similaridade de 0.5092964250023783\n",
      "Rank 493: dialga.png com similaridade de 0.5093729362863351\n",
      "Rank 494: whirlipede.png com similaridade de 0.5098784497492292\n",
      "Rank 495: aromatisse.png com similaridade de 0.5106150394336175\n",
      "Rank 496: marshtomp.png com similaridade de 0.5110218214692263\n",
      "Rank 497: munna.png com similaridade de 0.511666265237923\n",
      "Rank 498: taillow.png com similaridade de 0.5117140118299841\n",
      "Rank 499: rufflet.png com similaridade de 0.5120119374872567\n",
      "Rank 500: dedenne.png com similaridade de 0.5121377461816099\n",
      "Rank 501: magearna.png com similaridade de 0.5121477530002969\n",
      "Rank 502: koffing.png com similaridade de 0.5127881394249033\n",
      "Rank 503: cleffa.png com similaridade de 0.5135752438949497\n",
      "Rank 504: ninetales.png com similaridade de 0.5138929847719284\n",
      "Rank 505: gloom.png com similaridade de 0.5144551008934446\n",
      "Rank 506: larvesta.png com similaridade de 0.5154324361279096\n",
      "Rank 507: florges.png com similaridade de 0.5156864536455095\n",
      "Rank 508: type-null.png com similaridade de 0.5160291901302121\n",
      "Rank 509: malamar.png com similaridade de 0.5173778376438547\n",
      "Rank 510: morelull.png com similaridade de 0.5176939692209518\n",
      "Rank 511: deino.png com similaridade de 0.5186135860717203\n",
      "Rank 512: skiddo.png com similaridade de 0.518998070687211\n",
      "Rank 513: pansage.png com similaridade de 0.5193610938972577\n",
      "Rank 514: diancie.png com similaridade de 0.5197824853792177\n",
      "Rank 515: spheal.png com similaridade de 0.5198423463288018\n",
      "Rank 516: arcanine.png com similaridade de 0.5198819773477239\n",
      "Rank 517: jynx.png com similaridade de 0.5199839738659567\n",
      "Rank 518: pheromosa.png com similaridade de 0.5199883123085254\n",
      "Rank 519: bouffalant.png com similaridade de 0.5202803884958684\n",
      "Rank 520: luvdisc.png com similaridade de 0.5210998715359677\n",
      "Rank 521: guzzlord.png com similaridade de 0.5211739036820746\n",
      "Rank 522: phanpy.png com similaridade de 0.5218434396066088\n",
      "Rank 523: dodrio.png com similaridade de 0.5222636228344677\n",
      "Rank 524: numel.png com similaridade de 0.5235568714511614\n",
      "Rank 525: lunatone.png com similaridade de 0.5238259693204362\n",
      "Rank 526: dwebble.png com similaridade de 0.5238982295355947\n",
      "Rank 527: gumshoos.png com similaridade de 0.5240703741323312\n",
      "Rank 528: shieldon.png com similaridade de 0.5244468781690282\n",
      "Rank 529: azelf.png com similaridade de 0.5249702285830696\n",
      "Rank 530: trapinch.png com similaridade de 0.5265625722933733\n",
      "Rank 531: furfrou.png com similaridade de 0.5266057972596018\n",
      "Rank 532: golurk.png com similaridade de 0.528144812364079\n",
      "Rank 533: spearow.png com similaridade de 0.5290013846320551\n",
      "Rank 534: dewpider.png com similaridade de 0.52935488540098\n",
      "Rank 535: ditto.png com similaridade de 0.5297964735470284\n",
      "Rank 536: natu.png com similaridade de 0.530588027636733\n",
      "Rank 537: pidgey.png com similaridade de 0.5306850638743588\n",
      "Rank 538: charmander.png com similaridade de 0.5309539993445429\n",
      "Rank 539: purrloin.png com similaridade de 0.5312932453923725\n",
      "Rank 540: ferrothorn.png com similaridade de 0.5317370820920814\n",
      "Rank 541: torkoal.png com similaridade de 0.5317705003024844\n",
      "Rank 542: oranguru.png com similaridade de 0.532176768662883\n",
      "Rank 543: tyrogue.png com similaridade de 0.5322209530912764\n",
      "Rank 544: lickilicky.png com similaridade de 0.5323390669436268\n",
      "Rank 545: drowzee.png com similaridade de 0.532978805211898\n",
      "Rank 546: duosion.png com similaridade de 0.5335474016323262\n",
      "Rank 547: weezing.png com similaridade de 0.5335791996825867\n",
      "Rank 548: togepi.png com similaridade de 0.5350078076247992\n",
      "Rank 549: shuppet.png com similaridade de 0.5354034257481215\n",
      "Rank 550: panpour.png com similaridade de 0.5356753011638262\n",
      "Rank 551: venipede.png com similaridade de 0.5360063988198212\n",
      "Rank 552: azurill.png com similaridade de 0.5360523137124917\n",
      "Rank 553: ludicolo.png com similaridade de 0.5363519776348982\n",
      "Rank 554: flareon.png com similaridade de 0.5366132850403004\n",
      "Rank 555: virizion.png com similaridade de 0.537016011767897\n",
      "Rank 556: shinx.png com similaridade de 0.5378197258763451\n",
      "Rank 557: clamperl.png com similaridade de 0.5378268835188843\n",
      "Rank 558: wailord.png com similaridade de 0.5380317022390936\n",
      "Rank 559: zigzagoon.png com similaridade de 0.5389147179999288\n",
      "Rank 560: bidoof.png com similaridade de 0.5391330749894463\n",
      "Rank 561: electrike.png com similaridade de 0.5392558995439561\n",
      "Rank 562: lampent.png com similaridade de 0.5399442441912032\n",
      "Rank 563: tynamo.png com similaridade de 0.5409947766494116\n",
      "Rank 564: clefairy.png com similaridade de 0.5410504238441453\n",
      "Rank 565: marill.png com similaridade de 0.5413622281688468\n",
      "Rank 566: shiftry.png com similaridade de 0.5421461528591343\n",
      "Rank 567: cubone.png com similaridade de 0.5422589761572311\n",
      "Rank 568: bewear.png com similaridade de 0.5423909188538092\n",
      "Rank 569: nihilego.png com similaridade de 0.5439832191189546\n",
      "Rank 570: regice.png com similaridade de 0.5448492860024543\n",
      "Rank 571: tyrantrum.png com similaridade de 0.5448746002851832\n",
      "Rank 572: cutiefly.png com similaridade de 0.5462098300115956\n",
      "Rank 573: wailmer.png com similaridade de 0.5463454540301194\n",
      "Rank 574: kakuna.png com similaridade de 0.5471332712312911\n",
      "Rank 575: samurott.png com similaridade de 0.5492192118929071\n",
      "Rank 576: marshadow.png com similaridade de 0.5495867250057243\n",
      "Rank 577: bellsprout.png com similaridade de 0.550128412839478\n",
      "Rank 578: donphan.png com similaridade de 0.5504144900609259\n",
      "Rank 579: raticate.png com similaridade de 0.5506442227707091\n",
      "Rank 580: camerupt.png com similaridade de 0.5515428719345824\n",
      "Rank 581: buneary.png com similaridade de 0.5521529658770397\n",
      "Rank 582: omanyte.png com similaridade de 0.5522904457656443\n",
      "Rank 583: sylveon.png com similaridade de 0.5524237200728125\n",
      "Rank 584: gurdurr.png com similaridade de 0.5525808299171757\n",
      "Rank 585: growlithe.png com similaridade de 0.5532308566240661\n",
      "Rank 586: lilligant.png com similaridade de 0.553261635816882\n",
      "Rank 587: raichu.png com similaridade de 0.5549802778956501\n",
      "Rank 588: emolga.png com similaridade de 0.5555399197272235\n",
      "Rank 589: pikipek.png com similaridade de 0.5559367257654183\n",
      "Rank 590: gastly.png com similaridade de 0.5574537175149195\n",
      "Rank 591: gyarados.png com similaridade de 0.557897522634931\n",
      "Rank 592: barboach.png com similaridade de 0.5579659375599748\n",
      "Rank 593: whimsicott.png com similaridade de 0.55822223632605\n",
      "Rank 594: starly.png com similaridade de 0.5597027760718698\n",
      "Rank 595: vanillite.png com similaridade de 0.5599351632845488\n",
      "Rank 596: kecleon.png com similaridade de 0.5601910564244439\n",
      "Rank 597: blissey.png com similaridade de 0.5604974917839656\n",
      "Rank 598: pupitar.png com similaridade de 0.5622705639388175\n",
      "Rank 599: tangela.png com similaridade de 0.5629062549410087\n",
      "Rank 600: vanillish.png com similaridade de 0.5635748680944875\n",
      "Rank 601: ursaring.png com similaridade de 0.563973071281182\n",
      "Rank 602: raikou.png com similaridade de 0.564069492553549\n",
      "Rank 603: dunsparce.png com similaridade de 0.5654874727675357\n",
      "Rank 604: quagsire.png com similaridade de 0.5658082470127686\n",
      "Rank 605: magcargo.png com similaridade de 0.5674100722730891\n",
      "Rank 606: durant.png com similaridade de 0.5678556333347269\n",
      "Rank 607: bonsly.png com similaridade de 0.567949258784569\n",
      "Rank 608: emboar.png com similaridade de 0.5684823239368143\n",
      "Rank 609: meltan.png com similaridade de 0.5688385466092046\n",
      "Rank 610: tympole.png com similaridade de 0.5715240789623028\n",
      "Rank 611: bronzor.png com similaridade de 0.5716017082000493\n",
      "Rank 612: kyurem.png com similaridade de 0.5737620645441868\n",
      "Rank 613: cradily.png com similaridade de 0.5749147708044828\n",
      "Rank 614: latias.png com similaridade de 0.575620008910803\n",
      "Rank 615: woobat.png com similaridade de 0.5765310196879421\n",
      "Rank 616: roggenrola.png com similaridade de 0.5766145003205734\n",
      "Rank 617: ledian.png com similaridade de 0.5796609893052367\n",
      "Rank 618: venonat.png com similaridade de 0.5805372182883395\n",
      "Rank 619: oshawott.png com similaridade de 0.5809783228962511\n",
      "Rank 620: klink.png com similaridade de 0.5815104531864059\n",
      "Rank 621: spoink.png com similaridade de 0.5815732980079845\n",
      "Rank 622: tapu-lele.png com similaridade de 0.5817738260377958\n",
      "Rank 623: charjabug.png com similaridade de 0.5819780129719654\n",
      "Rank 624: pyroar.png com similaridade de 0.5837757225093244\n",
      "Rank 625: comfey.png com similaridade de 0.5842705968351619\n",
      "Rank 626: darumaka.png com similaridade de 0.5854848476804578\n",
      "Rank 627: umbreon.png com similaridade de 0.5857952257014425\n",
      "Rank 628: magnezone.png com similaridade de 0.5858964084300546\n",
      "Rank 629: glalie.png com similaridade de 0.5860782342753472\n",
      "Rank 630: swirlix.png com similaridade de 0.5870772267706462\n",
      "Rank 631: patrat.png com similaridade de 0.5885996192879482\n",
      "Rank 632: minun.png com similaridade de 0.5890678324985466\n",
      "Rank 633: vikavolt.png com similaridade de 0.5896155524285014\n",
      "Rank 634: altaria.png com similaridade de 0.5904064283418058\n",
      "Rank 635: torchic.png com similaridade de 0.5918977796053262\n",
      "Rank 636: smoochum.png com similaridade de 0.5930129567683544\n",
      "Rank 637: floette.png com similaridade de 0.5956381144273455\n",
      "Rank 638: chatot.png com similaridade de 0.5956546646719729\n",
      "Rank 639: spiritomb.png com similaridade de 0.5960966743300653\n",
      "Rank 640: beldum.png com similaridade de 0.6002211172705674\n",
      "Rank 641: crawdaunt.png com similaridade de 0.6059102416700346\n",
      "Rank 642: fomantis.png com similaridade de 0.6074245390853186\n",
      "Rank 643: dhelmise.png com similaridade de 0.6082599280134119\n",
      "Rank 644: mareep.png com similaridade de 0.6098614942451446\n",
      "Rank 645: piplup.png com similaridade de 0.6100636119221132\n",
      "Rank 646: scyther.png com similaridade de 0.6122799891204005\n",
      "Rank 647: scizor.png com similaridade de 0.6123347800316266\n",
      "Rank 648: naganadel.png com similaridade de 0.6131347385893833\n",
      "Rank 649: pawniard.png com similaridade de 0.6138337429874914\n",
      "Rank 650: boldore.png com similaridade de 0.6165150659698642\n",
      "Rank 651: caterpie.png com similaridade de 0.6171682408338592\n",
      "Rank 652: absol.png com similaridade de 0.6180510150201137\n",
      "Rank 653: cranidos.png com similaridade de 0.6204464544979654\n",
      "Rank 654: plusle.png com similaridade de 0.6212668782581205\n",
      "Rank 655: wobbuffet.png com similaridade de 0.6235252452027473\n",
      "Rank 656: shelgon.png com similaridade de 0.624389588916799\n",
      "Rank 657: cottonee.png com similaridade de 0.6247017802514198\n",
      "Rank 658: seedot.png com similaridade de 0.6248454337999362\n",
      "Rank 659: mismagius.png com similaridade de 0.6253819994091745\n",
      "Rank 660: glaceon.png com similaridade de 0.6257059051256131\n",
      "Rank 661: diggersby.png com similaridade de 0.6269853188092928\n",
      "Rank 662: hippowdon.png com similaridade de 0.6284140601607866\n",
      "Rank 663: pachirisu.png com similaridade de 0.6319625485225954\n",
      "Rank 664: froslass.png com similaridade de 0.6335865191103377\n",
      "Rank 665: palpitoad.png com similaridade de 0.6345357073649055\n",
      "Rank 666: arceus.png com similaridade de 0.6389218064319466\n",
      "Rank 667: cascoon.png com similaridade de 0.6389795309311554\n",
      "Rank 668: pidgeot.png com similaridade de 0.647301252591147\n",
      "Rank 669: medicham.png com similaridade de 0.6490923571217633\n",
      "Rank 670: arbok.png com similaridade de 0.6501992227856506\n",
      "Rank 671: shellos.png com similaridade de 0.6557463291894292\n",
      "Rank 672: chikorita.png com similaridade de 0.6563347065749103\n",
      "Rank 673: bagon.png com similaridade de 0.6589100276107187\n",
      "Rank 674: scraggy.png com similaridade de 0.6637571865230814\n",
      "Rank 675: ariados.png com similaridade de 0.6645166580120969\n",
      "Rank 676: delphox.png com similaridade de 0.6645493420593471\n",
      "Rank 677: xatu.png com similaridade de 0.6698945541224413\n",
      "Rank 678: victini.png com similaridade de 0.6729943759063852\n",
      "Rank 679: galvantula.png com similaridade de 0.6753992621731966\n",
      "Rank 680: excadrill.png com similaridade de 0.6780281809450193\n",
      "Rank 681: ralts.png com similaridade de 0.6806326457374577\n",
      "Rank 682: blacephalon.png com similaridade de 0.6816312290802689\n",
      "Rank 683: combee.png com similaridade de 0.6828663336110494\n",
      "Rank 684: staraptor.png com similaridade de 0.6830567216357879\n",
      "Rank 685: meloetta-aria.png com similaridade de 0.6855997526244972\n",
      "Rank 686: swellow.png com similaridade de 0.6880127904142865\n",
      "Rank 687: sandile.png com similaridade de 0.6893224587035028\n",
      "Rank 688: hitmonchan.png com similaridade de 0.6895782970566776\n",
      "Rank 689: throh.png com similaridade de 0.6897601437501784\n",
      "Rank 690: oricorio-baile.png com similaridade de 0.6939586750954627\n",
      "Rank 691: shuckle.png com similaridade de 0.694666634564359\n",
      "Rank 692: mew.png com similaridade de 0.6987782459340401\n",
      "Rank 693: alomomola.png com similaridade de 0.7004585893462694\n",
      "Rank 694: reuniclus.png com similaridade de 0.7004626546112676\n",
      "Rank 695: cobalion.png com similaridade de 0.7041463842977094\n",
      "Rank 696: genesect.png com similaridade de 0.7075923992983677\n",
      "Rank 697: krabby.png com similaridade de 0.7080638765443463\n",
      "Rank 698: pidgeotto.png com similaridade de 0.7148590366585037\n",
      "Rank 699: scrafty.png com similaridade de 0.7186296777868804\n",
      "Rank 700: scolipede.png com similaridade de 0.7245581343363022\n",
      "Rank 701: sneasel.png com similaridade de 0.7340160063428013\n",
      "Rank 702: unfezant.png com similaridade de 0.7354214586631871\n",
      "Rank 703: kingdra.png com similaridade de 0.7414379397245623\n",
      "Rank 704: serperior.png com similaridade de 0.7458923501714287\n",
      "Rank 705: gothorita.png com similaridade de 0.7480953530581769\n",
      "Rank 706: spinarak.png com similaridade de 0.7602891592841623\n",
      "Rank 707: noibat.png com similaridade de 0.7688382192515542\n",
      "Rank 708: frillish.png com similaridade de 0.7728826792296203\n",
      "Rank 709: servine.png com similaridade de 0.7826929949071892\n",
      "Rank 710: keldeo-ordinary.png com similaridade de 0.7866284517262467\n",
      "Rank 711: mienshao.png com similaridade de 0.7928427884037652\n",
      "Rank 712: krokorok.png com similaridade de 0.7961036321343472\n",
      "Rank 713: dewott.png com similaridade de 0.7982233464810422\n",
      "Rank 714: steelix.png com similaridade de 0.804245951795207\n",
      "Rank 715: finneon.png com similaridade de 0.8056629351019104\n",
      "Rank 716: necrozma.png com similaridade de 0.8127004242932208\n",
      "Rank 717: eelektrik.png com similaridade de 0.8149635461354994\n",
      "Rank 718: anorith.png com similaridade de 0.8214884555908946\n",
      "Rank 719: togetic.png com similaridade de 0.8312458097093364\n",
      "Rank 720: exeggcute.png com similaridade de 0.8314086121099011\n",
      "Rank 721: goldeen.png com similaridade de 0.8437650234624293\n",
      "Rank 722: lucario.png com similaridade de 0.848371762605054\n",
      "Rank 723: skrelp.png com similaridade de 0.8506863814725574\n",
      "Rank 724: mightyena.png com similaridade de 0.8585772766686504\n",
      "Rank 725: heliolisk.png com similaridade de 0.8590588486060577\n",
      "Rank 726: beheeyem.png com similaridade de 0.8605759685548622\n",
      "Rank 727: goodra.png com similaridade de 0.8616765756187876\n",
      "Rank 728: liepard.png com similaridade de 0.8630761497578063\n",
      "Rank 729: vibrava.png com similaridade de 0.8649352525497904\n",
      "Rank 730: heatran.png com similaridade de 0.8712934815340083\n",
      "Rank 731: braviary.png com similaridade de 0.8750145920401808\n",
      "Rank 732: treecko.png com similaridade de 0.8788116634159389\n",
      "Rank 733: lugia.png com similaridade de 0.8943209370167036\n",
      "Rank 734: masquerain.png com similaridade de 0.895382505137073\n",
      "Rank 735: silvally.png com similaridade de 0.9173217564218654\n",
      "Rank 736: houndoom.png com similaridade de 0.9236950748630619\n",
      "Rank 737: slakoth.png com similaridade de 0.9248735962754246\n",
      "Rank 738: flabebe.png com similaridade de 0.9280476121457439\n",
      "Rank 739: xurkitree.png com similaridade de 0.9415453534699958\n",
      "Rank 740: elgyem.png com similaridade de 0.9494370647281906\n",
      "Rank 741: gorebyss.png com similaridade de 1.0111327241227908\n",
      "Rank 742: articuno.png com similaridade de 1.022056797609855\n",
      "Rank 743: ninjask.png com similaridade de 1.051557610898686\n",
      "Rank 744: deerling.png com similaridade de 1.0534603046862887\n",
      "Rank 745: mantine.png com similaridade de 1.0587280440882874\n",
      "Rank 746: doublade.png com similaridade de 1.0590196148655415\n",
      "Rank 747: claydol.png com similaridade de 1.0800772268420424\n",
      "Rank 748: blaziken.png com similaridade de 1.1116197564642307\n",
      "Rank 749: swanna.png com similaridade de 1.121989601648914\n",
      "Rank 750: aurorus.png com similaridade de 1.1294981058674458\n",
      "Rank 751: bunnelby.png com similaridade de 1.156280172241206\n",
      "Rank 752: hoppip.png com similaridade de 1.1642853825788442\n",
      "Rank 753: ampharos.png com similaridade de 1.1662187591496882\n",
      "Rank 754: noivern.png com similaridade de 1.1816191200143058\n",
      "Rank 755: dratini.png com similaridade de 1.1862319512909274\n",
      "Rank 756: carnivine.png com similaridade de 1.1925960005210066\n",
      "Rank 757: swablu.png com similaridade de 1.2204233696907296\n",
      "Rank 758: gardevoir.png com similaridade de 1.2375391648138399\n",
      "Rank 759: rotom.png com similaridade de 1.2379670604143136\n",
      "Rank 760: metagross.png com similaridade de 1.2657633150062504\n",
      "Rank 761: sliggoo.png com similaridade de 1.3020034117007435\n",
      "Rank 762: watchog.png com similaridade de 1.3036330908324474\n",
      "Rank 763: staravia.png com similaridade de 1.323777531643124\n",
      "Rank 764: mothim.png com similaridade de 1.3268940859130387\n",
      "Rank 765: talonflame.png com similaridade de 1.3279925751337327\n",
      "Rank 766: sawsbuck.png com similaridade de 1.3706332703246167\n",
      "Rank 767: salazzle.png com similaridade de 1.3722015653646717\n",
      "Rank 768: stantler.png com similaridade de 1.3831094252262404\n",
      "Rank 769: darkrai.png com similaridade de 1.3900012359184952\n",
      "Rank 770: crobat.png com similaridade de 1.4784225537288083\n",
      "Rank 771: empoleon.png com similaridade de 1.4817116757955773\n",
      "Rank 772: golbat.png com similaridade de 1.4962170659370824\n",
      "Rank 773: girafarig.png com similaridade de 1.5113029399847857\n",
      "Rank 774: stoutland.png com similaridade de 1.548708896773328\n",
      "Rank 775: skarmory.png com similaridade de 1.5565352783135746\n",
      "Rank 776: tirtouga.png com similaridade de 1.5767684828771773\n",
      "Rank 777: klefki.png com similaridade de 1.604692173950907\n",
      "Rank 778: fletchinder.png com similaridade de 1.6335157981226418\n",
      "Rank 779: aerodactyl.png com similaridade de 1.6644614560957258\n",
      "Rank 780: xerneas.png com similaridade de 1.6671750370197853\n",
      "Rank 781: zebstrika.png com similaridade de 1.7429992010609598\n",
      "Rank 782: surskit.png com similaridade de 1.8844017899070697\n",
      "Rank 783: stunky.png com similaridade de 1.9065268490706049\n",
      "Rank 784: tentacool.png com similaridade de 1.9201761444078027\n",
      "Rank 785: doduo.png com similaridade de 2.040923782288309\n",
      "Rank 786: shellder.png com similaridade de 2.1347470865777183\n",
      "Rank 787: sawk.png com similaridade de 2.1742739554914126\n",
      "Rank 788: amaura.png com similaridade de 2.2976756070617346\n",
      "Rank 789: onix.png com similaridade de 2.3173951103181247\n",
      "Rank 790: sigilyph.png com similaridade de 2.320464182467126\n",
      "Rank 791: yanma.png com similaridade de 2.3912271394410234\n",
      "Rank 792: moltres.png com similaridade de 2.3981212683986324\n",
      "Rank 793: geodude.png com similaridade de 2.44009678357868\n",
      "Rank 794: leavanny.png com similaridade de 2.511083631545489\n",
      "Rank 795: dragalge.png com similaridade de 2.5827286929620206\n",
      "Rank 796: skuntank.png com similaridade de 2.653353557008435\n",
      "Rank 797: chimecho.png com similaridade de 2.66122649407031\n",
      "Rank 798: metang.png com similaridade de 2.7436960436979345\n",
      "Rank 799: gallade.png com similaridade de 2.860873144080493\n",
      "Rank 800: chandelure.png com similaridade de 3.1428025210469004\n",
      "Rank 801: gothitelle.png com similaridade de 3.338709194852451\n",
      "Rank 802: honchkrow.png com similaridade de 3.4687781056887435\n",
      "Rank 803: eelektross.png com similaridade de 3.513531958605721\n",
      "Rank 804: yveltal.png com similaridade de 3.6685305295592454\n",
      "Rank 805: dragonair.png com similaridade de 3.8100864980569593\n",
      "Rank 806: yanmega.png com similaridade de 5.284049267415162\n",
      "Rank 807: lumineon.png com similaridade de 6.317743197819195\n",
      "Rank 808: wingull.png com similaridade de 21.20512183469347\n",
      "Rank 809: lunala.png com similaridade de 22.31976204983932\n"
     ]
    }
   ],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# Fun&ccedil;&atilde;o para processar a imagem e obter o contorno principal\n",
    "def get_main_contour(image_path):\n",
    "    # Carregar a imagem\n",
    "    image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)\n",
    "    # Aplicar a binariza&ccedil;&atilde;o usando Otsu\n",
    "    _, threshold = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
    "    # Detectar contornos\n",
    "    contours, _ = cv2.findContours(threshold, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n",
    "\n",
    "    # Verificar se h&aacute; contornos detectados\n",
    "    if contours:\n",
    "        # Obter o maior contorno (assumindo que &eacute; o contorno do objeto)\n",
    "        main_contour = max(contours, key=cv2.contourArea)\n",
    "    else:\n",
    "        main_contour = None\n",
    "    return main_contour\n",
    "    \n",
    "# Fun&ccedil;&atilde;o para comparar dois contornos\n",
    "def compare_contours(contour1, contour2):\n",
    "    # Aproximar os contornos\n",
    "    epsilon1 = 0.01 * cv2.arcLength(contour1, True)\n",
    "    epsilon2 = 0.01 * cv2.arcLength(contour2, True)\n",
    "    approx1 = cv2.approxPolyDP(contour1, epsilon1, True)\n",
    "    approx2 = cv2.approxPolyDP(contour2, epsilon2, True)\n",
    "    # Comparar os contornos\n",
    "    similarity = cv2.matchShapes(approx1, approx2, cv2.CONTOURS_MATCH_I1, 0.0)\n",
    "    return similarity\n",
    "\n",
    "# Caminho da imagem de entrada\n",
    "input_image_path = 'abra.png'\n",
    "\n",
    "# Pasta contendo as imagens do dataset\n",
    "dataset_folder = 'pokemon_dataset/images'\n",
    "\n",
    "# Obter o contorno principal da imagem de entrada\n",
    "input_contour = get_main_contour(input_image_path)\n",
    "\n",
    "# Dicion&aacute;rio para armazenar as similaridades\n",
    "similarity_dict = {}\n",
    "\n",
    "# Lista de arquivos de imagem na pasta do dataset\n",
    "image_files = [f for f in os.listdir(dataset_folder) if f.endswith(('.png', '.jpg', '.jpeg'))]\n",
    "\n",
    "# Itera&ccedil;&atilde;o sobre as imagens do dataset\n",
    "for filename in image_files:\n",
    "    dataset_image_path = os.path.join(dataset_folder, filename)\n",
    "    try:\n",
    "        # Obter o contorno principal da imagem do dataset\n",
    "        dataset_contour = get_main_contour(dataset_image_path)\n",
    "        if dataset_contour is not None:\n",
    "            # Comparar os contornos\n",
    "            similarity = compare_contours(input_contour, dataset_contour)\n",
    "            # Armazenar a similaridade no dicion&aacute;rio\n",
    "            similarity_dict[filename] = similarity\n",
    "    except Exception as e:\n",
    "        print(f'Erro ao processar {filename}: {e}')\n",
    "\n",
    "# Ordenar o dicion&aacute;rio por similaridade\n",
    "ranked_similarity = {k: v for k, v in sorted(similarity_dict.items(), key=lambda item: item[1])}\n",
    "\n",
    "# Exibir a imagem mais semelhante\n",
    "best_match = next(iter(ranked_similarity))\n",
    "best_similarity = ranked_similarity[best_match]\n",
    "\n",
    "print(f'A imagem mais semelhante &eacute;: {best_match} com similaridade de {best_similarity}\\n')\n",
    "\n",
    "# Exibir o resultado\n",
    "for rank, (filename, similarity) in enumerate(ranked_similarity.items(), start=1):\n",
    "    print(f'Rank {rank}: {filename} com similaridade de {similarity}')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ep_15",
   "language": "python",
   "name": "ep_15"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}