Metricas Antigas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introdução\n",
    "\n",
    "Este caderno visa aplicar métricas nas imagens da tabela periódica com vários contrastes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pickle\n",
    "import time\n",
    "\n",
    "from funcoes import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imagens\\recorte_leite_00.jpg\n",
      "Imagens\\recorte_leite_05.jpg\n",
      "Imagens\\recorte_leite_10.jpg\n",
      "Imagens\\recorte_leite_15.jpg\n",
      "Imagens\\recorte_leite_20.jpg\n",
      "Imagens\\recorte_leite_25.jpg\n",
      "Imagens\\recorte_leite_30.jpg\n",
      "Imagens\\recorte_leite_35.jpg\n",
      "Imagens\\recorte_leite_99.jpg\n"
     ]
    }
   ],
   "source": [
    "arquivos_na_pasta =  glob.glob('Imagens\\*.jpg') # modificar caso necessário\n",
    "arquivos_na_pasta.sort()\n",
    "listaImagens = []\n",
    "for file in arquivos_na_pasta:\n",
    "    print(file)\n",
    "    listaImagens.append(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lista_pickle = [listaImagens, testImg, df, df2, df3]\n",
    "# Saving the objects:\n",
    "# with open('objs.pkl', 'wb') as f:  # Python 3: open(..., 'wb')\n",
    "#     pickle.dump(lista_pickle, f)\n",
    "\n",
    "# Getting back the objects:\n",
    "with open('objs.pkl', 'rb') as f:  # Python 3: open(..., 'rb')\n",
    "    listaImagens,testImg, df, df2, df3 = pickle.load(f)\n",
    "with open('df_copia.pkl', 'rb') as f:  # Python 3: open(..., 'rb')\n",
    "    df_copia = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definindo as métricas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Leite"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x, std = media_quadrados(r, iteracoes, tamanho)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HS"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Michelson\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMS - Root Mean Square contrast\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análise de algumas diferentes formas de se ler uma figura"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aplicando métricas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "metricas = [HS,michelson, RMS]\n",
    "lista_HS = []\n",
    "lista_michelson = []\n",
    "lista_RMS = []\n",
    "for file in arquivos_na_pasta:\n",
    "    lista_HS.append(HS(file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Michelson"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\Documents\\GitHub\\Paper-nova-metrica\\funcoes.py:51: RuntimeWarning: overflow encountered in ubyte_scalars\n",
      "  denominador = np.max(imagem) + np.min(imagem)\n"
     ]
    }
   ],
   "source": [
    "for file in arquivos_na_pasta:\n",
    "    lista_michelson.append(michelson(file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for file in arquivos_na_pasta:\n",
    "    lista_RMS.append(RMS(file))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Leite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-4cf2a9a82e0d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfile\u001b[0m \u001b[1;32min\u001b[0m \u001b[0marquivos_na_pasta\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mimagem\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmedia_quadrados\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimagem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miteracoes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtamanho\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mmedias\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mdesvios\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstd\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\GitHub\\Paper-nova-metrica\\funcoes.py\u001b[0m in \u001b[0;36mmedia_quadrados\u001b[1;34m(imagem, N, tamanho)\u001b[0m\n\u001b[0;32m    172\u001b[0m     \u001b[0mlista\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 174\u001b[1;33m         \u001b[0mtemp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpotential_contrast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimagem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtamanho\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    175\u001b[0m         \u001b[0mlista\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    176\u001b[0m     \u001b[1;31m# print('desvio padrao = ', \"%.2f\" % (np.std(lista)))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\GitHub\\Paper-nova-metrica\\funcoes.py\u001b[0m in \u001b[0;36mpotential_contrast\u001b[1;34m(imagem, tamanho)\u001b[0m\n\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m     \u001b[0mhistoBack\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhisto_norm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 159\u001b[1;33m     \u001b[0mhistoFore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhisto_norm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFore\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m     \u001b[0mz\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhistoBack\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mhistoFore\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Documents\\GitHub\\Paper-nova-metrica\\funcoes.py\u001b[0m in \u001b[0;36mhisto_norm\u001b[1;34m(imagem)\u001b[0m\n\u001b[0;32m    136\u001b[0m     \"\"\"\n\u001b[0;32m    137\u001b[0m     \u001b[1;31m# pega o histograma da imagem, equivalente ao imhist do matlab\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 138\u001b[1;33m     \u001b[0mcount\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimagem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m256\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    139\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    140\u001b[0m     \u001b[0mhisto\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcount\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\histograms.py\u001b[0m in \u001b[0;36mhistogram\u001b[1;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[0;32m    823\u001b[0m             \u001b[1;31m# Only include values in the right range\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m             \u001b[0mkeep\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtmp_a\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mfirst_edge\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 825\u001b[1;33m             \u001b[0mkeep\u001b[0m \u001b[1;33m&=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtmp_a\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mlast_edge\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    826\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogical_and\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    827\u001b[0m                 \u001b[0mtmp_a\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtmp_a\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkeep\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "medias = []\n",
    "desvios = []\n",
    "tamanho = 1\n",
    "iteracoes = 20000\n",
    "for file in arquivos_na_pasta:\n",
    "    imagem = cv2.imread(file,0)\n",
    "    x, std = media_quadrados(imagem, iteracoes, tamanho)\n",
    "    medias.append(x)\n",
    "    desvios.append(std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criando DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# dataframe em preto e branco\n",
    "concentracao = [0,5,10,15,20,25,30,35,99]\n",
    "df = pd.DataFrame(\n",
    "{\n",
    "    \"Concentracao\":concentracao,\n",
    "    \"Leite\":medias,\n",
    "    \"HS\":lista_HS,\n",
    "    \"RMS\":lista_RMS,\n",
    "    \"Michelson\":lista_michelson,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Concentracao</th>\n",
       "      <th>Leite</th>\n",
       "      <th>HS</th>\n",
       "      <th>RMS</th>\n",
       "      <th>Michelson</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>252.34800</td>\n",
       "      <td>0.231373</td>\n",
       "      <td>0.227095</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>250.55025</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.082456</td>\n",
       "      <td>2.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>247.86000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.042151</td>\n",
       "      <td>0.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>242.00775</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.024688</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>234.90600</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.015062</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "      <td>233.18475</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.014121</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>229.83150</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.013638</td>\n",
       "      <td>0.235772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>228.62025</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.015040</td>\n",
       "      <td>0.274194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>99</td>\n",
       "      <td>243.58875</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.026970</td>\n",
       "      <td>0.381295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Concentracao      Leite        HS       RMS  Michelson\n",
       "0             0  252.34800  0.231373  0.227095   1.000000\n",
       "1             5  250.55025  0.210526  0.082456   2.444444\n",
       "2            10  247.86000  0.200000  0.042151   0.851852\n",
       "3            15  242.00775  0.200000  0.024688   0.440000\n",
       "4            20  234.90600  0.161290  0.015062   0.272727\n",
       "5            25  233.18475  0.172414  0.014121   0.233333\n",
       "6            30  229.83150  0.172414  0.013638   0.235772\n",
       "7            35  228.62025  0.147059  0.015040   0.274194\n",
       "8            99  243.58875  0.185185  0.026970   0.381295"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAfBUlEQVR4nO3deXRU5f3H8c9kskAakMUJeKgHFUEsgqgFZV9kiYSIxojBJQg/caNC0cMWRBSRzRSsUiq1VFwoNYIQUUGooEJCXZCiKFZ6JIAQkgBhCQlZZp7fH5apKSROEuaZzPB+ncM5uXPnzny/ucmHm+feuY/DGGMEAPC7sEAXAADnCwIXACwhcAHAEgIXACwhcAHAEgIXACwJD3QBtXH4cKE8nupd1da4cbQKCor8VFHg0Fdwoa/g42tvLleDStedd0e44eHOQJfgF/QVXOgr+JyL3s67wAWAQCFwAcASv47hLliwQGvWrJEk9erVSxMmTDhj/YoVK9SwYUNJ0tChQ3XXXXf5syQACBi/BW5WVpY2b96slStXyuFw6L777tP69evVv39/73N27NihefPm6ZprrvFXGQBQZ/gtcF0ulyZNmqTIyEhJUqtWrXTgwIEKz9mxY4cWLVqk/fv3q1OnTpo4caKioqL8VRIABJTfxnBbt26tjh07SpKys7O1Zs0a9erVy7v+5MmTuvLKKzV+/HitXLlSx48f18KFC/1VDgAEnMPft2fctWuXHnjgAT3yyCO69dZbK33eN998o9TUVK1atcqf5QBAwPj1pNnWrVs1ZswYpaamKj4+vsK6AwcOKCsrS0lJSZIkY4zCw6tXTk0++OByNVB+/olqbRMM6Cu40Ffw8bW3gHzwIScnR6NHj1ZaWtoZYStJ9erV07PPPqt9+/bJGKOlS5dWOKEGAKHGb0e4ixcvVklJiWbPnu19LDk5WRs2bNCYMWPUvn17TZ8+XQ899JDKysp07bXXasSIEf4qBwACzu9juP7k7yEFpzNM5ZLcHiNnmEPhktxuT/ULtSBU/5Sjr+ASqn1J52ZIIahvXuNPTmeYCorKNHPJp8orKFZs4/pKvbezGkdH1NnQBVC38dHeSpRL3rCVpLyCYs1c8qnKA1sWgCBG4FbC7THesD0tr6BY7moOYQDAaQRuJZxhDsU2rl/hsdjG9eUMcwSoIgDBjsCtRLik1Hs7e0P39Bgug94Aaor8qITb7VHj6AjNerhbUFylAKDuI3Cr4HZ75NB/vkluI3eA6wEQ3BhSAABLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsMSvgbtgwQLFx8crPj5ec+fOPWP9zp07lZiYqIEDB2rKlCkqLy/3ZzkAEFB+C9ysrCxt3rxZK1eu1KpVq/T1119r/fr1FZ4zfvx4PfHEE3r//fdljFF6erq/ygGAgPNb4LpcLk2aNEmRkZGKiIhQq1atdODAAe/6/fv369SpU+rYsaMkKTExUWvXrvVXOQAQcOH+euHWrVt7v87OztaaNWu0bNky72N5eXlyuVzeZZfLpdzc3Gq9R9OmMTWqzeVqUKPt6jr6Ci70FXxq25vfAve0Xbt26YEHHtCECRN0ySWXeB/3eDxyOBzeZWNMhWVfHD5cKI/HVGsbl6uB8vNPVGubYEBfwYW+go+vvVUVyn49abZ161bde++9euyxx3TrrbdWWNe8eXPl5+d7lw8dOqTY2Fh/lgMAAeW3wM3JydHo0aOVlpam+Pj4M9a3aNFCUVFR2rp1qyQpIyNDPXv29Fc5ABBwfhtSWLx4sUpKSjR79mzvY8nJydqwYYPGjBmj9u3bKy0tTY8//rgKCwvVrl07paSk+KscAAg4hzGmeoOgdQhjuP9FX8GFvoJPnR/DBQD8F4ELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJYQuABgCYELAJaE+/rE0tJSFRcXyxjjfaxRo0Z+KQoAQpFPgbts2TLNmjVLZWVlkiRjjBwOh3bu3OnX4gAglPgUuIsXL9ayZcvUrl27ar9BYWGhkpOT9eKLL+qXv/xlhXULFizQihUr1LBhQ0nS0KFDddddd1X7PQAgGPgUuBdeeGGNwnb79u16/PHHlZ2dfdb1O3bs0Lx583TNNddU+7UBINj4dNKse/fu+utf/6rc3FwdPXrU++/npKena9q0aYqNjT3r+h07dmjRokVKSEjQ9OnTVVJSUr3qASCIOMxPz4JVokOHDiotLa24YTXGcPv27atXX321wpDCyZMn9dvf/laTJk1Sy5YtNWnSJLVo0ULjxo2rZgsAEBx8CtzaOlvg/q9vvvlGqampWrVqlc+ve/hwoTye6pXvcjVQfv6Jam0TDOgruNBX8PG1N5erQaXrfBrD9Xg8Wrx4sT7++GOVl5erW7duevDBBxUe7vNVZWc4cOCAsrKylJSUJOnHKx9q83oAUNf5NIb7u9/9Tv/4xz80fPhwjRgxQtu2bdOcOXNq9cb16tXTs88+q3379skYo6VLl6p///61ek0AqMt8OqTctGmTVqxYoYiICElS7969dfPNN9foDUeNGqUxY8aoffv2mj59uh566CGVlZXp2muv1YgRI2r0mgAQDHwKXGOMN2wlKTIyssLyz9mwYYP365deesn79cCBAzVw4ECfXwcAgplPQwpt27bVzJkztXfvXu3bt0+zZs1SmzZt/F0bAIQUnwJ32rRpOnbsmJKTk3X77bfryJEjmjp1qr9rA4CQ4tOQQkxMjPckWWlpqQ4dOsSNawCgmnw6wl2/fr2efvppFRYWKi4uTkOGDNErr7zi79oAIKT4FLiLFi3S0KFDtW7dOnXs2FEbN25URkaGv2sDgJDiU+AaY3TFFVcoKytLPXv2VExMjCx8QA0AQopPgRsWFqb33ntPmzdvVrdu3fTRRx/J4XD4uzYACCk+Be7EiROVnp6ucePGyeVy6Y9//KOmTJni79oAIKT4dJXCr3/9ay1ZssS7/Le//c1f9QBAyKoycBMSEqrcePXq1ee0GAAIZVUGLh9uAIBzp8ox3M6dO3v/1atXT99//706duyoiIgIde7c2VaNABASfDpp9tZbb2ny5Mn685//rBMnTujhhx9Wenq6v2sDgJDiU+C+9tpreuONNxQTE6OmTZvqrbfe4pNmAFBNPl+HGxMT412+6KKL5HQ6/VYUAIQinwK3UaNG2rlzp/fDDm+//bYuuOACvxYGAKHGp+twU1NTNXbsWO3du1fdu3dXVFSUFi5c6O/aACCk+BS4rVq1UkZGhrKzs+V2u3XppZdWa8YHAICPgXvq1CmtW7dOR44ckTFGmZmZksQcZABQDT4F7qOPPqqDBw+qTZs23LQGAGrIp8DdtWuX3n//fYWF+XSODQBwFj4laNOmTVVeXu7vWgAgpFV5hPvyyy9Lklwul+655x7deOONFU6WMYYLAL6rMnC/++47ST9OIhkTE6Pdu3dbKQoAQlGVgTtr1izv15999pk6deqko0eP6vPPP1e/fv38XhwAhBKfxnDnz5+v559/XtKPl4j96U9/4oMPAFBNPgXuBx98oL/85S+SpObNm+v111/Xe++959fCACDU+BS4ZWVlFU6WRUREcD0uAFSTT9fhXnvttXrssceUlJQkh8OhVatW6eqrr/Z3bQAQUnw6wp06daouvPBCzZo1S3PnzlXTpk2ZtRcAqsmnI9zo6GhNnjzZ37UAQEirMnDHjh2r3//+95XO3susvQDguyoD9/7779fRo0f1m9/8psJJMmMMJ80AoJqqDNzbbrvNG6zGmArrHA6Hdu7c6b/KACDEVBm4t9xyi7Zt26a+ffvqtttu0+WXX26rLgAIOVUG7uzZs1VcXKx169bpmWeeUVFRkW6++WYlJCSoYcOGtmoEgJDws1cp1K9fX0OGDNGQIUN08OBBZWRkKCUlRZdccomee+45GzUCQEio1h3Fjxw5oiNHjqigoEAnTpzwV00AEJJ+9gg3JydHb7/9tjIyMuR0OnXzzTcrPT1dzZo1s1EfAISMKgP3nnvu0e7duzVo0CClpaXpV7/6la26ACDkVBm4n332maKiovTmm29q+fLl3sdPX4f7xRdf+L1AAAgVVQbuBx98YKsOAAh5VQZuixYtbNUBACGPec8BwBICFwAsIXABwBICFwAsIXABwBICFwAsIXABwBICFwAsIXABwBK/Bm5hYaEGDx6sH3744Yx1O3fuVGJiogYOHKgpU6aovLzcn6XI6QyTcYYpr6BIxhkmp5P/awDY5bfU2b59u4YNG6bs7Oyzrh8/fryeeOIJvf/++zLGKD093V+lyOkMU0FRmSYvzNT/zVivyQszVVBURugCsMpviZOenq5p06YpNjb2jHX79+/XqVOn1LFjR0lSYmKi1q5d669SVC5p5pJPlVdQLEnKKyjWzCWfyr/H1ABQ0c/egLymnnnmmUrX5eXlyeVyeZddLpdyc3Or/R5Nm8b49Ly8giJv2P73sWLJ4ZDL1aDa71tXhVIvP0VfwSVU+5Jq35vfArcqHo/HO/269N/761bX4cOF8njMzz/RGabYxvUrhG5s4/qSMcrPD42pglyuBiHTy0/RV3AJ1b4k33urKpQDMojZvHlz5efne5cPHTp01qGHcyVcUuq9nX8MWf0Ytqn3dg7M/zYAzlsByZwWLVooKipKW7du1XXXXaeMjAz17NnTb+/ndnvUODpCsx7uJjkckjEK/8/jAGCL1SPcUaNG6auvvpIkpaWladasWYqLi1NRUZFSUlL8+t5ut0cOt0exjaPlcHsIWwDWOYwxPgyC1k0+j+H+RKiOMdFXcKGv4BO0Y7gAcD4icAHAEgIXACwhcAHAEgIXACwhcAHAEgIXACwhcAHAEgIXACwhcAHAEgLXD05P51PucDCdDwAv7lB4jp2ezuf0DBOnbwXZODqCG+YA5zkOvc4xpvMBUBkC9xxze8xZp/NxV/OuZgBCD4F7jjnDHN6ZJU6LbVxfzrDqTyEEILQQuOcY0/kAqAw5cI79dDoft8fIGeZgOh8Akghcv3C7PXLoP99ct5E7wPUAqBsYUgAASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALDEr4G7evVqDRo0SAMGDNDSpUvPWL9gwQL16dNHQ4YM0ZAhQ876HAAIFeH+euHc3FzNnz9fb731liIjI5WcnKzrr79el19+ufc5O3bs0Lx583TNNdf4qwwAqDP8doSblZWlG264QY0aNVJ0dLQGDhyotWvXVnjOjh07tGjRIiUkJGj69OkqKSnxVzkAEHB+C9y8vDy5XC7vcmxsrHJzc73LJ0+e1JVXXqnx48dr5cqVOn78uBYuXOivcgAg4Pw2pODxeORwOLzLxpgKy7/4xS/00ksveZdHjhyp1NRUjRs3zuf3aNo0pka1uVwNarRdXUdfwYW+gk9te/Nb4DZv3lyff/65dzk/P1+xsbHe5QMHDigrK0tJSUmSfgzk8PDqlXP4cKE8HlOtbVyuBsrPP1GtbYIBfQUX+go+vvZWVSj7bUiha9eu2rJli44cOaLi4mKtW7dOPXv29K6vV6+enn32We3bt0/GGC1dulT9+/f3VzkAEHB+C9xmzZpp3LhxSklJ0S233KLBgwerQ4cOGjVqlL766is1adJE06dP10MPPaS4uDgZYzRixAh/lQMAAecwxlTvb/I6JJSGFJzOMJVLcnuMnGEOhUtyuz0+b19X+6ot+gouodqXdG6GFPw2hgvfOZ1hKigq08wlnyqvoFixjesr9d7OahwdUa3QBVC38dHeOqBc8oatJOUVFGvmkk9VHtiyAJxjBG4d4PYYb9ielldQLHc1h0sA1G0Ebh3gDHMotnH9Co/FNq4vZ5ijki0ABCMCtw4Il5R6b2dv6J4ew2WAHQgt/E7XAW63R42jIzTr4W41vkoBQN1H4NYRbrdHDv1nh7iN3AGuBzifne0yzXOBwAWAn6jsMs1GjWp/EpsxXAD4icou0zx2sva3jyVwAeAnKrtMs6y89udUCFwA+InKLtOMCK99XBK4APATlV2mecEvos7JawMA/qOyyzTDzsEHkQhcAPgf/rpMkyEFALCEwAUASwhcALCEwAUASwhcALCEwIVfOZ1hMs4wlTscMs4wOZ38yOH8xWVhQe70XY3yCookZ1iduq0jc7UBFXG4EcROB9rkhZn6vxnrNXlhpgqKyurMUSRztQEV1Y3fTNRIXQ805moDKiJwg1hdDzTmagMqInCDWE0DraYnsqq7HXO1ARXxsx/ETgfa/56UCpcq/ex3TU9k1WQ75moDKnIYY+rG3581cPhwoTzV/PPZ5Wqg/PwTfqrIvtNXKcjhkIz52UAzzjBNXphZYSgitnF9zXq4mxx+2K62Qm1/nUZfwcfX3lyuBpWuY0ghyLndHjncHsU2jpbD7fnZo8eajvvW9fFiIBgQuOeZGo/7cgIMqDUC9zxT0xNZnAADao/fl/NMTU9kcQIMqD0C9zxU07vZ++su+MD5giEFALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALCEwAUASwhcALAkqD9pFlbDG6fUdLu6jr6CC30Fn9r2FtT3wwWAYMKQAgBYQuACgCUELgBYQuACgCUELgBYQuACgCUELgBYQuACgCUELgBYct4E7urVqzVo0CANGDBAS5cuDXQ5tbJgwQLFx8crPj5ec+fOlSRlZWUpISFBAwYM0Pz58wNcYe3MmTNHkyZNkiTt3LlTiYmJGjhwoKZMmaLy8vIAV1d9GzZsUGJiom666SbNmDFDUujsr4yMDO/P4pw5cyQF9z4rLCzU4MGD9cMPP0iqfD/VuEdzHjh48KDp06ePKSgoMCdPnjQJCQlm165dgS6rRjIzM80dd9xhSkpKTGlpqUlJSTGrV682vXr1Mnv37jVlZWVm5MiR5sMPPwx0qTWSlZVlrr/+ejNx4kRjjDHx8fFm27ZtxhhjJk+ebJYuXRrI8qpt7969pnv37iYnJ8eUlpaaYcOGmQ8//DAk9ldRUZHp1KmTOXz4sCkrKzNJSUkmMzMzaPfZP//5TzN48GDTrl07s2/fPlNcXFzpfqppj+fFEW5WVpZuuOEGNWrUSNHR0Ro4cKDWrl0b6LJqxOVyadKkSYqMjFRERIRatWql7OxstWzZUhdffLHCw8OVkJAQlP0dPXpU8+fP14MPPihJ2r9/v06dOqWOHTtKkhITE4Our/Xr12vQoEFq3ry5IiIiNH/+fNWvXz8k9pfb7ZbH41FxcbHKy8tVXl6u8PDwoN1n6enpmjZtmmJjYyVJX3755Vn3U21+LoP6bmG+ysvLk8vl8i7Hxsbqyy+/DGBFNde6dWvv19nZ2VqzZo3uvvvuM/rLzc0NRHm18sQTT2jcuHHKycmRdOZ+c7lcQdfXnj17FBERoQcffFA5OTnq3bu3WrduHRL7KyYmRmPHjtVNN92k+vXrq1OnToqIiAjaffbMM89UWD5bbuTm5tbq5/K8OML1eDxyOP57WzVjTIXlYLRr1y6NHDlSEyZM0MUXXxz0/b355pu66KKL1KVLF+9jobDf3G63tmzZopkzZ+qNN97Ql19+qX379gV9X5L07bffasWKFdq4caM2bdqksLAwZWZmhkRvUuU/f7X5uTwvjnCbN2+uzz//3Lucn5/v/bMhGG3dulVjxoxRamqq4uPj9emnnyo/P9+7Phj7e++995Sfn68hQ4bo2LFjKioqksPhqNDXoUOHgq6vCy+8UF26dFGTJk0kSf369dPatWvldDq9zwnG/SVJmzdvVpcuXdS0aVNJP/5pvXjx4qDfZ6c1b978rL9X//t4dXo8L45wu3btqi1btujIkSMqLi7WunXr1LNnz0CXVSM5OTkaPXq00tLSFB8fL0m6+uqrtXv3bu3Zs0dut1vvvPNO0PX38ssv65133lFGRobGjBmjvn37atasWYqKitLWrVsl/XhGPNj66tOnjzZv3qzjx4/L7XZr06ZNiouLC/r9JUlt27ZVVlaWioqKZIzRhg0b1Llz56DfZ6dV9nvVokWLGvd4XhzhNmvWTOPGjVNKSorKysqUlJSkDh06BLqsGlm8eLFKSko0e/Zs72PJycmaPXu2HnnkEZWUlKhXr16Ki4sLYJXnTlpamh5//HEVFhaqXbt2SklJCXRJ1XL11Vfrvvvu05133qmysjJ169ZNw4YN02WXXRb0+6t79+765ptvlJiYqIiICLVv317333+/+vfvH9T77LSoqKhKf69q+nPJjA8AYMl5MaQAAHUBgQsAlhC4AGAJgQsAlhC4AGDJeXFZGOoet9utV199VatXr5bb7VZZWZn69OmjsWPHKjIyMtDlacGCBWrbtq369esX6FIQQjjCRUA8+eST2rZtm1555RVlZGRo+fLl2r17t6ZMmRLo0iRJn3zySVDdVhDBgSNcWPfDDz9o9erV2rx5s2JiYiRJ0dHReuqpp/TFF1/oxIkTeuqpp/Ttt9/K4XCoR48eevTRRxUeHu69uD4zM1N5eXneDxVI0qJFi7Ry5UqFh4erZcuWmj17tho0aKA333xTy5Ytk8fjUaNGjTR16lS1atVKkyZNUkxMjP71r3/p4MGDuuKKKzRnzhytWrVKO3bs0Ny5c+V0OvXBBx/o6NGj2rdvn3r37q2kpCRNnz5dJ0+eVH5+vtq2bavnnntOUVFR2r59u2bMmKHi4mJFRERowoQJ6tKli5YvX6433nhDZWVlOnbsmEaNGuWt+w9/+IPeffddOZ1OXXrppZo6dWqFm6MghJzjW0oCP2vt2rXmtttuq3T9hAkTzNNPP208Ho8pKSkxI0eONIsWLTLGGNOmTRvz2muvGWOM+eqrr8xVV11lTp06Zf7+97+bAQMGmKNHjxpjjJk5c6ZZuHCh+eSTT8ydd95pioqKjDHGbNq0ycTFxRljjJk4cWKFewvfcsstZvny5cYYY+6++26zZs0a7/OGDx/urW/27Nlm1apVxhhjSktLzeDBg83atWtNaWmp6datm9m4caO3vsGDB5sTJ06YoUOHmiNHjhhjjNm2bZvp2LGjMcaY5cuXmzvuuMOcPHnSGGPM888/b0aOHFm7bzDqLI5wYV1YWJg8Hk+l6z/++GMtW7ZMDodDkZGRSk5O1iuvvKL7779fknTjjTdKktq1a6fS0lIVFRVpy5YtiouL0wUXXCBJmjx5siRp7ty52rNnj5KTk72vf/z4cR09elSS1KNHD++YcZs2bXTs2LGz1nTdddd5vx4/frwyMzP10ksvKTs7W3l5eSoqKtJ3332nsLAw9e7dW5J01VVXafXq1ZKkF198UR999JGys7P17bffqqioyNtrYmKioqOjJUkpKSl68cUXVVpaWifGsnFuEbiwrkOHDvr+++9VWFjoHVKQpNzcXE2dOvWM2995PJ4K46lRUVGS5H2OMUZOp7PCNsePH9fx48fl8Xg0ZMgQjR8/3vtaeXl53mCuV6+edxuHwyFTySfdTweiJD366KNyu9266aab1Lt3b+Xk5Jy1Bkn67rvv1LBhQ91xxx0aOnSorrvuOsXFxWnjxo3eeqrqFaGFk2awrlmzZkpISFBqaqoKCwsl/TiX1JNPPqlGjRqpe/fuev3112WMUWlpqdLT09W1a9cqX7Nr165av3699/VeeOEFLVmyRN27d9e7776rvLw8SdKyZcs0fPjwn63R6XRWGnybN2/W6NGjNWjQIEnS9u3b5Xa7ddlll8nhcCgzM1OS9PXXX2v48OH64osv1KRJEz388MPq3r27N2zdbrd69OihFStWeI94X3vtNXXq1Imj2xDFES4CYtq0aVq4cKGSk5PldDpVWlqqfv366ZFHHtHJkyc1Y8YMJSQkqKysTD169PBOu1OZXr166d///reGDRsmSbr88sv19NNPKyYmRqNGjdLIkSPlcDgUExOjBQsW/OwNo/v27at58+aprKzsjHXjxo3T6NGjFR0drZiYGHXq1El79+5VZGSkXnjhBc2cOVNz585VRESEXnjhBbVr105vv/224uLi5HA41LlzZzVp0kR79uxRUlKScnJydPvtt8vj8ahly5ZKS0ur+TcWdRp3CwMASxhSAABLCFwAsITABQBLCFwAsITABQBLCFwAsITABQBLCFwAsOT/AZYQ857WTZuBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for coluna in df.columns[1:]:\n",
    "    plt.figure()\n",
    "    sns.relplot(x=\"Concentracao\", y=coluna, data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tarefa, aplicar HS em vários tamanhos diferentes e descobrir qual a diferença entre os valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "tamanhos = [5,10,50,100,200,500,1000]\n",
    "iteracoes = [1,10,100,1000]\n",
    "# tamanhos = [1,5,10]\n",
    "# iteracoes = [1,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 1 \n",
      "Tempo de execução:  0.0019989013671875\n",
      "5 10 \n",
      "Tempo de execução:  0.015991687774658203\n",
      "5 100 \n",
      "Tempo de execução:  0.10793876647949219\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:32: RuntimeWarning: divide by zero encountered in longlong_scalars\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 1000 \n",
      "Tempo de execução:  0.8834946155548096\n",
      "10 1 \n",
      "Tempo de execução:  0.0019974708557128906\n",
      "10 10 \n",
      "Tempo de execução:  0.018988847732543945\n",
      "10 100 \n",
      "Tempo de execução:  0.09494519233703613\n",
      "10 1000 \n",
      "Tempo de execução:  0.8804931640625\n",
      "50 1 \n",
      "Tempo de execução:  0.0009949207305908203\n",
      "50 10 \n",
      "Tempo de execução:  0.009987831115722656\n",
      "50 100 \n",
      "Tempo de execução:  0.09394669532775879\n",
      "50 1000 \n",
      "Tempo de execução:  1.0503954887390137\n",
      "100 1 \n",
      "Tempo de execução:  0.0009982585906982422\n",
      "100 10 \n",
      "Tempo de execução:  0.009996414184570312\n",
      "100 100 \n",
      "Tempo de execução:  0.09896159172058105\n",
      "100 1000 \n",
      "Tempo de execução:  1.1043646335601807\n",
      "200 1 \n",
      "Tempo de execução:  0.001998424530029297\n",
      "200 10 \n",
      "Tempo de execução:  0.016989946365356445\n",
      "200 100 \n",
      "Tempo de execução:  0.24186182022094727\n",
      "200 1000 \n",
      "Tempo de execução:  1.9059083461761475\n",
      "500 1 \n",
      "Tempo de execução:  0.009012937545776367\n",
      "500 10 \n",
      "Tempo de execução:  0.09094691276550293\n",
      "500 100 \n",
      "Tempo de execução:  0.8605072498321533\n",
      "500 1000 \n",
      "Tempo de execução:  8.442152500152588\n",
      "1000 1 \n",
      "Tempo de execução:  0.034981489181518555\n",
      "1000 10 \n",
      "Tempo de execução:  0.431751012802124\n",
      "1000 100 \n",
      "Tempo de execução:  3.365070104598999\n",
      "1000 1000 \n",
      "Tempo de execução:  30.813306093215942\n",
      "[[5, 1, 0.2857142857142857, 0.0], [5, 10, 0.3252769030998685, 0.13227918032881605], [5, 100, 2.458480182487538, 20.35681976826886], [5, 1000, inf, nan], [10, 1, 0.16666666666666666, 0.0], [10, 10, 0.23161269776691604, 0.10980431311199419], [10, 100, 0.278954454912882, 0.13382266158740277], [10, 1000, 0.2824970095358076, 0.1419980936636857], [50, 1, 0.4819277108433735, 0.0], [50, 10, 0.29847444132408274, 0.18515008195215804], [50, 100, 0.22937029879199247, 0.19207456192796996], [50, 1000, 0.23467599493401667, 0.18511460752112593], [100, 1, 0.028925619834710745, 0.0], [100, 10, 0.24397506379932543, 0.16235804746501736], [100, 100, 0.18696652189283605, 0.17324985265808598], [100, 1000, 0.18749607939558433, 0.1752053288046912], [200, 1, 0.02880658436213992, 0.0], [200, 10, 0.12837865127440776, 0.11204068860283531], [200, 100, 0.15645548243906585, 0.10881864956431442], [200, 1000, 0.17215635119990624, 0.13079638865492288], [500, 1, 0.21176470588235294, 0.0], [500, 10, 0.2015686274509804, 0.07186642314638625], [500, 100, 0.17961479079820905, 0.0662723198870702], [500, 1000, 0.19408950131233596, 0.06286915678059328], [1000, 1, 0.21568627450980393, 0.0], [1000, 10, 0.21607843137254906, 0.011154088355551293], [1000, 100, 0.21713725490196079, 0.021965370669545257], [1000, 1000, 0.21258431372549022, 0.01799966636454436]]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "temp = []\n",
    "primeira_imagem = cv2.imread(arquivos_na_pasta[0],0)\n",
    "for tamanho in tamanhos:\n",
    "    for N in iteracoes:\n",
    "        tic = time.time()\n",
    "        valor, std = quadrados_media_std(primeira_imagem,N,tamanho,HS)\n",
    "        temp.append([tamanho,N,valor,std])\n",
    "        toc = time.time()\n",
    "        print(tamanho,N,\"\\nTempo de execução: \",toc-tic)\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tamanho</th>\n",
       "      <th>Iteracoes</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Std</th>\n",
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       "      <td>0.162358</td>\n",
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       "      <td>0.018000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tamanho  Iteracoes     Valor        Std\n",
       "0         5          1  0.285714   0.000000\n",
       "1         5         10  0.325277   0.132279\n",
       "2         5        100  2.458480  20.356820\n",
       "3         5       1000       inf        NaN\n",
       "4        10          1  0.166667   0.000000\n",
       "5        10         10  0.231613   0.109804\n",
       "6        10        100  0.278954   0.133823\n",
       "7        10       1000  0.282497   0.141998\n",
       "8        50          1  0.481928   0.000000\n",
       "9        50         10  0.298474   0.185150\n",
       "10       50        100  0.229370   0.192075\n",
       "11       50       1000  0.234676   0.185115\n",
       "12      100          1  0.028926   0.000000\n",
       "13      100         10  0.243975   0.162358\n",
       "14      100        100  0.186967   0.173250\n",
       "15      100       1000  0.187496   0.175205\n",
       "16      200          1  0.028807   0.000000\n",
       "17      200         10  0.128379   0.112041\n",
       "18      200        100  0.156455   0.108819\n",
       "19      200       1000  0.172156   0.130796\n",
       "20      500          1  0.211765   0.000000\n",
       "21      500         10  0.201569   0.071866\n",
       "22      500        100  0.179615   0.066272\n",
       "23      500       1000  0.194090   0.062869\n",
       "24     1000          1  0.215686   0.000000\n",
       "25     1000         10  0.216078   0.011154\n",
       "26     1000        100  0.217137   0.021965\n",
       "27     1000       1000  0.212584   0.018000"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame(data=temp,columns=['Tamanho',\"Iteracoes\", \"Valor\", \"Std\"])\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23137254901960785"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HS(primeira_imagem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVwAAAFcCAYAAACEFgYsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAfJUlEQVR4nO3dbXRU1d338d/MJCEJUJ6ciEXBVlmttVBoBRVpEBYQIORGAq1YWgooAlJB2oWEQKFyo1GJUO9Ftcjy0oVGL6NIUqggKNIC4SqaZSFUXKUuw0OIIUAQ8pzMnPsFZi4iGfNAzp6cyffzKmfO4cx/z0l+7Nlnzx6XZVmWAAC2c4e6AABoLwhcADCEwAUAQwhcADCEwAUAQwhcADAkItQFXI2zZ0vl9zdtVlu3brEqKSm3uSLzaJez0C5naUm7vN7OQfe1mx5uRIQn1CXYgnY5C+1yltZuV7sJXAAINQIXAAwhcAHAEAIXAAyxdZbCunXrtG3bNknSsGHD9Oijj16xf9OmTfrWt74lSfr5z3+uqVOn2lkSAISMbYGbk5OjvXv3avPmzXK5XHrggQe0c+dOjRo1KnDM4cOHtWbNGg0cONCuMgCgzbAtcL1er1JSUhQVFSVJuummm3Tq1Kl6xxw+fFjr169XQUGBBg0apMWLF6tDhw52lQQAIWXbGG7fvn01YMAASVJ+fr62bdumYcOGBfaXlZXplltu0aJFi7R582ZduHBBzz33nF3lAEDIuexegPzo0aOaPXu2Hn74YU2cODHocZ988olSU1OVlZVlZzkAEDK23jTLzc3V/PnzlZqaqsTExHr7Tp06pZycHE2ePFmSZFmWIiKaV05zPtrr9XZWcfHFZp3fCWiXs9AuZ2lJu0Ly0d7CwkLNmzdP6enpV4StJEVHR2v16tU6ceKELMtSRkZGvRtqrcnjcavkYqVqXS5ZHrc8HmbDATDPth7uiy++qKqqKj355JOBx6ZMmaJdu3Zp/vz56tevn1auXKm5c+eqpqZGP/7xjzVjxoxWr8PjcaukvEZPvLxPp0sqFNctRqnTB6tbbKR8Pn+rPx8ABGP7GK6dmjKkYHncWvLcpbCtE9ctRmkP3SVXGAQub+WchXY5i2OGFNoKn9+qF7aSdLqkQr4mjv0CQGsJ+8D1uF2K6xZT77G4bjHyuF0hqghAexX2gRshKXX64EDo1o3hOnrldQCOFPa54/P51S02UukL4lVZVSuP26WIrx4HAJPCvocrfRW6naMVYVly+fyELYCQaBeBCwBtAYELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIYQuABgCIELAIbYGrjr1q1TYmKiEhMT9fTTT1+x/8iRI0pOTlZCQoKWLl2q2tpaO8sBgJCyLXBzcnK0d+9ebd68WVlZWfrXv/6lnTt31jtm0aJFWr58ud59911ZlqXMzEy7ygGAkLMtcL1er1JSUhQVFaXIyEjddNNNOnXqVGB/QUGBKisrNWDAAElScnKytm/fblc5ABByEXaduG/fvoGf8/PztW3bNr3++uuBx06fPi2v1xvY9nq9KioqsqscAAg52wK3ztGjRzV79mw9+uijuvHGGwOP+/1+uVyuwLZlWfW2m6JHj07NOt7r7dys452CdjkL7XKW1myXrYGbm5ur+fPnKzU1VYmJifX29ezZU8XFxYHtM2fOKC4urlnnP3u2VH6/1aRjvd7OKi6+2KzzOwHtchba5Swtadc3BbRtY7iFhYWaN2+e0tPTrwhbSerVq5c6dOig3NxcSVJ2drbi4+PtKgcAQs62Hu6LL76oqqoqPfnkk4HHpkyZol27dmn+/Pnq16+f0tPTtWzZMpWWlurWW2/VtGnT7CoHAELOZVlW096Tt0EMKdAup6FdzuKYIQUAQH0ELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCG2B25paanGjx+vkydPXrFv3bp1Gj58uCZMmKAJEyYoIyPD7nIAIGQi7Dz5wYMHtWzZMuXn5ze4//Dhw1qzZo0GDhxoZxkA0CbY2sPNzMzUihUrFBcX1+D+w4cPa/369UpKStLKlStVVVVlZzkAEFIuy7Isu59kxIgR2rhxo66//vrAY2VlZXrkkUeUkpKiPn36KCUlRb169dLChQvtLgcAQiJkgft1n3zyiVJTU5WVldXk8549Wyq/v2nle72dVVx8scnndgra5Sy0y1la0i6vt3PQfSGbpXDq1Cm99dZbgW3LshQRYeuQMgCEVMgCNzo6WqtXr9aJEydkWZYyMjI0atSoUJUDALYzHrizZs1SXl6eunfvrpUrV2ru3LkaM2aMLMvSjBkzTJcDAMYYGcO1C2O4tMtpaJezhM0YLgC0NwQuABhC4AKAIQQuABhC4AKAIQQuABhC4AKAIQQuABhC4AKAIQQuABhC4AKAIY0G7q9//WsTdQBA2Gs0cC9evKjy8nITtQBAWGt0xe+YmBgNHz5c3/ve9xQbGxt4/M9//rOthQFAuGk0cCdPnmyiDgAIe40G7sSJE1VQUKADBw6otrZWgwcPVp8+fUzUBgBhpdEx3D179mjSpEl677339P7772vy5Ml67733TNQGAGGl0R7us88+q1dffVU333yzJOno0aNatGiRRo4caXtxABBOGu3h1tTUBMJWkvr27Sufz2drUQAQjhoN3OjoaOXl5QW28/LyFBMTY2tRABCOGh1SWLRokebMmRO4Ufb555/r2Weftb0wAAg3jQbubbfdpr/+9a86ePCg/H6/BgwYoG7dupmoDQDCStDAfemllxp8PD8/X5I0Y8YMWwoCgHAVNHD//e9/m6wDAMJe0MBNS0szWQcAhL1Gx3A//vhjvfDCCyovL5dlWfL7/Tp58qR2795toDwACB+NTgtbtmyZBg4cqNLSUiUlJalTp04aPXq0idoAIKw02sN1uVx68MEHVVJSou9+97tKSkrSpEmTTNQGAGGl0R5ux44dJUm9e/fW0aNHFR0dLbebL4oAgOZqtIfbr18/PfLII1qwYIFmz56t/Px8RUQ0+s8AAF8TtKs6b9487d+/X0uXLtX06dP1ne98R6mpqfL7/XrmmWdM1ggAYSFoV/UnP/mJVq5cKUn6xS9+oZtvvll333237r77blO1AUBYCdrDnTlzprZt26bHHntMBw8e1KhRo/SHP/xBR48eNVkfAISNRgdjBw8erMGDB+v8+fPKzs7W4sWL1alTJ23cuNFEfQAQNpo83SAqKkqxsbHq2LGjSkpK7KwJAMJSoz3c3NxcvfXWW3r//fc1ZMgQPfzwwxo8eLCJ2gAgrAQN3A0bNmjTpk2qqKjQz372M23dulVxcXEmawOAsBI0cPfs2aNHHnlEo0aNksfjMVkTAISloIHLTTEAaF18RhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQWwO3tLRU48eP18mTJ6/Yd+TIESUnJyshIUFLly5VbW2tnaUAQMjZFrgHDx7Ufffdp/z8/Ab3L1q0SMuXL9e7774ry7KUmZlpVykA0CbYFriZmZlasWJFgwveFBQUqLKyUgMGDJAkJScna/v27XaVAgBtgm3fBvn4448H3Xf69Gl5vd7AttfrVVFRUbOfo0ePTs063uvt3OzncALa5Sy0y1las10h+fpdv98vl8sV2LYsq952U509Wyq/32rSsV5vZxUXX2z2c7R1tMtZaJeztKRd3xTQIZml0LNnTxUXFwe2z5w5w1q7AMJeSAK3V69e6tChg3JzcyVJ2dnZio+PD0UpAGCM0cCdNWuW8vLyJEnp6elKS0vTmDFjVF5ermnTppksBQCMc1mW1bRB0DaIMVza5TS0y1nCYgwXANojAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADCFwAcAQAhcADLE1cLds2aJx48Zp9OjRysjIuGL/unXrNHz4cE2YMEETJkxo8BgACBcRdp24qKhIa9eu1dtvv62oqChNmTJFt99+u26++ebAMYcPH9aaNWs0cOBAu8oAgDbDth5uTk6O7rjjDnXt2lWxsbFKSEjQ9u3b6x1z+PBhrV+/XklJSVq5cqWqqqrsKgcAQs62wD19+rS8Xm9gOy4uTkVFRYHtsrIy3XLLLVq0aJE2b96sCxcu6LnnnrOrnGbzeNyyPG7VulyyPG55PAx3A7g6tg0p+P1+uVyuwLZlWfW2O3bsqA0bNgS2Z86cqdTUVC1cuLDJz9GjR6dm1eT1dm7ScX6/pWNfXNCq//qHTpdUKK5bjJbNvF19en5Lbrer8RMY1tR2OQ3tchba1TjbArdnz5766KOPAtvFxcWKi4sLbJ86dUo5OTmaPHmypEuBHBHRvHLOni2V32816Vivt7OKiy826VjL4w6ErSSdLqnQqv/6h9Ieuksun79ZNdqtOe1yEtrlLLSr/r8Jxrb3yUOGDNH+/ft17tw5VVRUaMeOHYqPjw/sj46O1urVq3XixAlZlqWMjAyNGjXKrnKaxee3AmFb53RJhXxNDHcAaIhtgXvttddq4cKFmjZtmu655x6NHz9e/fv316xZs5SXl6fu3btr5cqVmjt3rsaMGSPLsjRjxgy7ymkWj9uluG4x9R6L6xYjTxscTgDgHC7LshzbbbNrSMHjcaukvEZPvHwgMIabOn2wusVGyseQghG0y1loV/1/E4xtY7hO5vP51S02UmkP3SWf35LH7VLEV48DQEsRuEH4fH659NUL5LPkC3E9AJyPyaUAYAiBCwCGELgAYAiBCwCGELgAYAiBCwCGELgAYAiBCwCGELgAYEi7CFyPx62Si5UsJg4gpML+o73/uxDNvja/EA2A8Bb2Xb1aKbDql3RpXdsnXj6g2tCWBaAdCvvAbeli4nynGYDWFv5DCl8tJn556AYWE/c1HLpOWg8XgHOEfbctQtLS6YMV1y1G3+vdTSseuF3/d/YQueQK2mtlGAKAHcK+hytJkZFu/W7qjxUVGaG0y3qtS6cPVtcGeq3fNAzRLl4wALYI+x5uraQ/bPgfVdf4A2ErXQrQx18+IJ+u/J4yvtMMgB3CPnDreqs9usQ02GutbeDmWYSk1K+GISQFxnDp3QK4GmGfIXW9VXeQm2dut/T1789p6XeaeTxu1Up8DxqABoV9D7eut/plaaUWTBlYr9e6YMpARXgaHibw+fxy+fyKsCy5fP4mhW1JeY2WPLdPD6a9ryXP7VNJeQ3TyQAEhH0Pt6636omK1oXyas2d1F/RURGqrK5Vt84d5AoyNSwyyqNqn+Tz++VxuxXlkWqqg3+VZLCZDWkP3dXAKDGA9ijsA1e6FLpdY2N1oay6ScdHRnl05mJ1vRkNS6YP1jWdo4KGLjMbgPBRNzx4uqRc8rhbbXiw3WTBl2VV+sOG/7liDLehHmi1T1fMaEh7+YDSHhoatLfakg9YILQYc0dDPB63ymp9KjpbHng3fG2PWHWM8Fz170e7GWCsqfU3+SO+Pn+wY4O/2MxscBaPx62yGp+OF13U2S8rdbzoospqfIy5Q/K4VXKhSs9vOqQlz+3T85sOqeRCldQKvxvtJg8iI9xN7oF63MGOdUu+IEMKLZzZgNCwPC5VlNX/7GBFda1ioyOumLWC9qXGZ+nZ//643jvcZ//7Yz3x0NCr7qG2m//Ou3Ts0OQeaJRHWvK1Y5dMH6wozzc/h9vjkgKDDq6vttEWWZIqq3z1ejGVVT4x+AN/kHe4/kYWvGqKdtPDdbtdTe6B1lT7dE3nKKU9NLTJsxRacqPtatg1qN9e+GzsxcDZIjwNv8ON8Lh0tQuq8LsVRE21Ty6f76t5uL5GQzPYjTYbsrbenN/7V+1kzm8LWJYa7MVYFn3c9i4qwtXgu+GoiKt/x9puerh+v2XrkovfdKOttV9k5vxePbcryCcPXbyC7V11Za2u6dLhq3e4l94Nd4h0qbry6tcLbDddoi/LqmxdcrHuRtvlAjfaWpk/yJzfb5hEga9zWZp/b/1PHs6/d6DkooeLS6Hr8vl03TUd5fL5WiVspXYUuM2ZFtYSLb3R1hLuIOHuZjWzprOkLXs+0wMT+intobv0wIR+2rLnM3HXDHZqN0MKzZkW1hItudHWYl/1zv7fGx8HhkfonTVPhKT7Rn//iiGmCDErDPZpN4FbNy3Mzj+wmupLq+tGSJLPpxqb/nJdlivQO+scG6mL5TXasuczzb6nvz1PGIaYN41QaDeB25xpYW2d2yP9n/ibAtOa6lY+c3skP92zJvP5/Jf9B2nRs4Xt2k3gSuHzB1Zd49fGvx6p18Pd+Ncj+t3UH7evCwo4DH+fDuRxu1RysVJPvHwg8BgL5QBtX7uZpRBOWCgHcCb+Rh3o8hs+crkky3LseDTQntDDdai6rwCK6xbbpK8AAhB6BC4AGELgAoAhBC4AGELgtiKPxy3L41atyyXL47Z1ucS65zpdUm77cwFoHcxSaCV1a9TatfxjqJ4LQOuhW9RKgq1R21rLP4bquQC0HlsDd8uWLRo3bpxGjx6tjIyMK/YfOXJEycnJSkhI0NKlS1Vb69zI8AVZo7a1ln8M1XMBaD22BW5RUZHWrl2r1157TVlZWXrjjTf0n//8p94xixYt0vLly/Xuu+/KsixlZmbaVY7tPG5XkAXIW3+NWpPPBaD12Ba4OTk5uuOOO9S1a1fFxsYqISFB27dvD+wvKChQZWWlBgwYIElKTk6ut99pTH7clo/2As5k29/o6dOn5fV6A9txcXE6dOhQ0P1er1dFRUV2lWM7k+ur8tFewJlsC1y/3y/XZV/IZ1lWve3G9jdFjx6dmnW819u5WccjtML1etEuZ2nNdtkWuD179tRHH30U2C4uLlZcXFy9/cXFxYHtM2fO1NvfFGfPlsrfxBtFXm9nFRdfbNb5nYB2OQvtcpaWtOubAtq2MdwhQ4Zo//79OnfunCoqKrRjxw7Fx8cH9vfq1UsdOnRQbm6uJCk7O7vefgAIN7YF7rXXXquFCxdq2rRpuueeezR+/Hj1799fs2bNUl5eniQpPT1daWlpGjNmjMrLyzVt2jS7ygGAkHNZluXYyZsMKdAup6FdzuKYIQUAQH0ELgAYQuACgCEELgAYQuACgCGO/vi9u5mLtTT3eKegXc5Cu5ylNdvl6GlhAOAkDCkAgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCEELgAYQuACgCHtInC3bNmicePGafTo0crIyAh1Oc2ybt06JSYmKjExUU8//bSkS19Bn5SUpNGjR2vt2rWBY48cOaLk5GQlJCRo6dKlqq2tDVXZTfbUU08pJSVFUvD6T506palTp2rMmDGaO3euysrKQlnyN9q1a5eSk5M1duxYrVq1SlJ4XK/s7OzA7+FTTz0lydnXq7S0VOPHj9fJkyclNf8atbiNVpj74osvrOHDh1slJSVWWVmZlZSUZB09ejTUZTXJvn37rHvvvdeqqqqyqqurrWnTpllbtmyxhg0bZh0/ftyqqamxZs6cae3evduyLMtKTEy0Pv74Y8uyLGvJkiVWRkZGKMtvVE5OjnX77bdbixcvtiwreP0PPvigtXXrVsuyLGvdunXW008/HZqCG3H8+HFr6NChVmFhoVVdXW3dd9991u7dux1/vcrLy61BgwZZZ8+etWpqaqzJkydb+/btc+z1+uc//2mNHz/euvXWW60TJ05YFRUVzb5GLW1j2Pdwc3JydMcdd6hr166KjY1VQkKCtm/fHuqymsTr9SolJUVRUVGKjIzUTTfdpPz8fPXp00c33HCDIiIilJSUpO3bt6ugoECVlZUaMGCAJCk5OblNt/P8+fNau3at5syZI0lB66+pqdGHH36ohISEeo+3RTt37tS4cePUs2dPRUZGau3atYqJiXH89fL5fPL7/aqoqFBtba1qa2sVERHh2OuVmZmpFStWBL4l/NChQ826RlfTRkevFtYUp0+fltfrDWzHxcXp0KFDIayo6fr27Rv4OT8/X9u2bdMvf/nLK9pTVFR0RTu9Xq+KioqM1tscy5cv18KFC1VYWCjpyutUV39JSYk6deqkiIiIeo+3RceOHVNkZKTmzJmjwsJC3X333erbt6/jr1enTp20YMECjR07VjExMRo0aJAiIyMde70ef/zxetsNZcQ3XaOraWPY93D9fr9crv9dXs2yrHrbTnD06FHNnDlTjz76qG644YYG2+Okdr755pu67rrrdOeddwYeC1Z/Q+1oq+3y+Xzav3+/nnjiCb3xxhs6dOiQTpw44fjr9emnn2rTpk364IMPtGfPHrndbu3bt8/x16tOsGthx+9k2Pdwe/bsqY8++iiwXVxcHHgr4QS5ubmaP3++UlNTlZiYqAMHDqi4uDiwv649PXv2rPf4mTNn2mw733nnHRUXF2vChAn68ssvVV5eLpfL1WD93bt318WLF+Xz+eTxeNr09bvmmmt05513qnv37pKkkSNHavv27fJ4PIFjnHi99u7dqzvvvFM9evSQdOkt9Isvvuj461Xn69eisWt0NW0M+x7ukCFDtH//fp07d04VFRXasWOH4uPjQ11WkxQWFmrevHlKT09XYmKiJOlHP/qRPv/8cx07dkw+n09bt25VfHy8evXqpQ4dOig3N1fSpbvKbbWdL730krZu3ars7GzNnz9fI0aMUFpaWoP1R0ZG6rbbbtM777wjScrKymqz7Ro+fLj27t2rCxcuyOfzac+ePRozZozjr9f3v/995eTkqLy8XJZladeuXRo8eLDjr1ed5v5NXU0b28UC5Fu2bNH69etVU1OjyZMna9asWaEuqUlWrVqlTZs2qXfv3oHHpkyZohtvvFFpaWmqqqrSsGHDtGTJErlcLn366adatmyZSktLdeuttyotLU1RUVEhbEHj3n77bR04cEBPPvlk0PoLCgqUkpKis2fP6rrrrtOaNWvUpUuXUJfeoLfeeksvv/yyampqdNddd2nZsmX6xz/+4fjr9cILL+jtt99WZGSk+vXrpxUrVujzzz939PUaMWKENm7cqOuvv1779+9v1jVqaRvbReACQFsQ9kMKANBWELgAYAiBCwCGELgAYAiBCwCGhP0HH+B8q1at0ocffihJ+uyzz9SrVy9FR0dLkt54443Az6YcO3ZMkyZNqveBGqApCFy0ecuWLQv8PGLECKWnp6tfv34hrAhoGQIXjpaZmak333xTNTU1+vLLLzVnzhzde++9evPNN7Vr1y7V1taqsLBQ3/72tzVx4kS99tprys/P1/3336/p06errKxMK1as0PHjx3X+/Hl16tRJa9euVZ8+fXTfffdp0KBBys3NVWFhoYYOHarHHntM0qV1E37/+98rLy9PpaWlSklJ0ciRI1VdXa20tDQdOHBAbrdbAwYMUEpKijp27BjiVwptAWO4cKzS0lJt2rRJGzZsUFZWllavXq309PTA/tzcXK1atUp/+ctfdPz4ce3cuVMbN27U888/rz/+8Y+yLEt/+9vf1L17d2VmZmrHjh36wQ9+UG+R+oKCAr3yyivKzs7W+++/H/iYZ3l5uYYNG6asrCz97ne/0+rVqyVJf/rTn1RSUqLs7GxlZWWpurpazzzzjNkXBm0WPVw4VqdOnfT888/rgw8+UH5+vo4cOaLy8vLA/v79++vaa6+VJPXq1Ut33XWXXC6XevfurYqKClVXV2vcuHHq3bu3Nm7cqOPHj+vDDz/UoEGDAucYPny43G63OnfurBtuuEHnz5+X1+tVTEyMRo4cKUm65ZZbdO7cOUnS3//+dy1evDiwdN/UqVP129/+1tRLgjaOHi4cq6CgQBMnTtQXX3yh2267TQsWLNDln1T/+roEdSF4uVdeeUXLly9XbGyskpKSNHbs2HrnuPyG3OVL8F1+rrol+6RLQw1fX9KvLX91DswicOFYeXl58nq9mjNnjoYOHaoPPvhAfr+/WefYu3evJk2apMmTJ6tPnz7avXt3s89xuZ/+9Kd6/fXXVVtbK7/fr4yMDA0ZMqTF50N4IXDhWPHx8erevbvGjBmjsWPHqri4WF26dNHx48ebfI77779fr776qpKSkvSrX/1KP/zhD3Xs2LEW1/Sb3/xGXbp00YQJEzR27Fi5XC4tWbKkxedDeGG1MAAwhB4uABhC4AKAIQQuABhC4AKAIQQuABhC4AKAIQQuABhC4AKAIf8fZDEAN4bGmU4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"Tamanho\", y=\"Valor\", data=df2,palette='inferno');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'HS com vários tamanhos de quadrados e de iterações')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 424.1x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Tamanho\", y=\"Valor\",hue='Iteracoes' ,data=df2.loc[df2.Valor < 1],\n",
    "            s=10,\n",
    "            alpha=0.7);\n",
    "plt.title('HS com vários tamanhos de quadrados e de iterações')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d49639de10>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Tamanho\", y=\"Valor\",col='Iteracoes' ,data=df2.loc[df2.Valor < 1],\n",
    "           col_wrap=2,\n",
    "           s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Usuario\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:32: RuntimeWarning: divide by zero encountered in longlong_scalars\n",
      "C:\\Users\\Usuario\\Anaconda3\\lib\\site-packages\\numpy\\core\\_methods.py:117: RuntimeWarning: invalid value encountered in subtract\n",
      "  x = asanyarray(arr - arrmean)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 10000 \n",
      "Tempo de execução:  9.490550518035889\n",
      "5 50000 \n",
      "Tempo de execução:  48.67206335067749\n",
      "10 10000 \n",
      "Tempo de execução:  8.886896848678589\n",
      "10 50000 \n",
      "Tempo de execução:  53.00556540489197\n",
      "50 10000 \n",
      "Tempo de execução:  13.142455101013184\n",
      "50 50000 \n",
      "Tempo de execução:  61.77304172515869\n",
      "100 10000 \n",
      "Tempo de execução:  14.492841720581055\n",
      "100 50000 \n",
      "Tempo de execução:  60.66216969490051\n",
      "200 10000 \n",
      "Tempo de execução:  22.580036401748657\n",
      "200 50000 \n",
      "Tempo de execução:  109.14533185958862\n",
      "500 10000 \n",
      "Tempo de execução:  102.97287654876709\n",
      "500 50000 \n",
      "Tempo de execução:  445.6491286754608\n",
      "1000 10000 \n",
      "Tempo de execução:  309.47632670402527\n",
      "1000 50000 \n",
      "Tempo de execução:  1747.792760848999\n",
      "[[5, 10000, inf, nan], [5, 50000, inf, nan], [10, 10000, 0.3780309171941133, 2.979373256337895], [10, 50000, inf, nan], [50, 10000, 0.23893017298719818, 0.18693383239868747], [50, 50000, 0.23556585305777303, 0.18654741363289085], [100, 10000, 0.19041901402012182, 0.17654730340531913], [100, 50000, 0.19171756336920634, 0.1767354207423829], [200, 10000, 0.1710516673010169, 0.12852040931839526], [200, 50000, 0.16901054459982312, 0.12796186406651375], [500, 10000, 0.19195491122433225, 0.062381839979401124], [500, 50000, 0.19007498131851167, 0.06184644512824433], [1000, 10000, 0.21331647058823533, 0.01892168912285112], [1000, 50000, 0.21302549019607847, 0.01849117446936348]]\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "temp = []\n",
    "primeira_imagem = cv2.imread(arquivos_na_pasta[0],0)\n",
    "for tamanho in tamanhos:\n",
    "    for N in [10000,50000]:\n",
    "        tic = time.time()\n",
    "        valor, std = quadrados_media_std(primeira_imagem,N,tamanho,HS)\n",
    "        temp.append([tamanho,N,valor,std])\n",
    "        toc = time.time()\n",
    "        print(tamanho,N,\"\\nTempo de execução: \",toc-tic)\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tamanho</th>\n",
       "      <th>Iteracoes</th>\n",
       "      <th>Valor</th>\n",
       "      <th>Std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>10000</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>50000</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.378031</td>\n",
       "      <td>2.979373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>50000</td>\n",
       "      <td>inf</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.238930</td>\n",
       "      <td>0.186934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.235566</td>\n",
       "      <td>0.186547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.190419</td>\n",
       "      <td>0.176547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.191718</td>\n",
       "      <td>0.176735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>200</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.171052</td>\n",
       "      <td>0.128520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>200</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.169011</td>\n",
       "      <td>0.127962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>500</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.191955</td>\n",
       "      <td>0.062382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>500</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.190075</td>\n",
       "      <td>0.061846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1000</td>\n",
       "      <td>10000</td>\n",
       "      <td>0.213316</td>\n",
       "      <td>0.018922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1000</td>\n",
       "      <td>50000</td>\n",
       "      <td>0.213025</td>\n",
       "      <td>0.018491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Tamanho  Iteracoes     Valor       Std\n",
       "0         5      10000       inf       NaN\n",
       "1         5      50000       inf       NaN\n",
       "2        10      10000  0.378031  2.979373\n",
       "3        10      50000       inf       NaN\n",
       "4        50      10000  0.238930  0.186934\n",
       "5        50      50000  0.235566  0.186547\n",
       "6       100      10000  0.190419  0.176547\n",
       "7       100      50000  0.191718  0.176735\n",
       "8       200      10000  0.171052  0.128520\n",
       "9       200      50000  0.169011  0.127962\n",
       "10      500      10000  0.191955  0.062382\n",
       "11      500      50000  0.190075  0.061846\n",
       "12     1000      10000  0.213316  0.018922\n",
       "13     1000      50000  0.213025  0.018491"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame(data=temp,columns=['Tamanho',\"Iteracoes\", \"Valor\", \"Std\"])\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1, 'HS com vários tamanhos de quadrados e de iterações')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.225x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Tamanho\", y=\"Valor\",hue='Iteracoes' ,data=df3.loc[df3.Valor < 1],\n",
    "            s=10,\n",
    "            alpha=0.7);\n",
    "\n",
    "plt.title('HS com vários tamanhos de quadrados e de iterações')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Concentracao</th>\n",
       "      <th>Leite</th>\n",
       "      <th>HS</th>\n",
       "      <th>RMS</th>\n",
       "      <th>Michelson</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>252.34800</td>\n",
       "      <td>0.231373</td>\n",
       "      <td>0.227095</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>250.55025</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.082456</td>\n",
       "      <td>2.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>247.86000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.042151</td>\n",
       "      <td>0.851852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>242.00775</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.024688</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>234.90600</td>\n",
       "      <td>0.161290</td>\n",
       "      <td>0.015062</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25</td>\n",
       "      <td>233.18475</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.014121</td>\n",
       "      <td>0.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>229.83150</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.013638</td>\n",
       "      <td>0.235772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35</td>\n",
       "      <td>228.62025</td>\n",
       "      <td>0.147059</td>\n",
       "      <td>0.015040</td>\n",
       "      <td>0.274194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>99</td>\n",
       "      <td>243.58875</td>\n",
       "      <td>0.185185</td>\n",
       "      <td>0.026970</td>\n",
       "      <td>0.381295</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Concentracao      Leite        HS       RMS  Michelson\n",
       "0             0  252.34800  0.231373  0.227095   1.000000\n",
       "1             5  250.55025  0.210526  0.082456   2.444444\n",
       "2            10  247.86000  0.200000  0.042151   0.851852\n",
       "3            15  242.00775  0.200000  0.024688   0.440000\n",
       "4            20  234.90600  0.161290  0.015062   0.272727\n",
       "5            25  233.18475  0.172414  0.014121   0.233333\n",
       "6            30  229.83150  0.172414  0.013638   0.235772\n",
       "7            35  228.62025  0.147059  0.015040   0.274194\n",
       "8            99  243.58875  0.185185  0.026970   0.381295"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cálculo dos desvios padrão / erros padrão para as métricas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Passos:\n",
    "* Rodar novamente as métricas, utilizando um valor de N = 10.000 iterações.\n",
    "* Salvar dados no dataframe.\n",
    "* Calcular os erros padrão, coloque também no dataframe.\n",
    "* Plotar os gráficos com os erros padrão. \n",
    "* Salvar df no pickle, excel.\n",
    "* Limpar código, salvar em py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_copia = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 1000 \n",
      "Tempo de execução:  2.62630295753479\n",
      "[]\n"
     ]
    }
   ],
   "source": [
    "# 1. Rodar novamente as métricas, utilizando um valor de N = 10.000 iterações.\n",
    "import time\n",
    "tamanho = 100\n",
    "iteracoes = 1000\n",
    "N = iteracoes\n",
    "temp = []\n",
    "\n",
    "primeira_imagem = cv2.imread(arquivos_na_pasta[0],0)\n",
    "\n",
    "for metrica in [HS, michelson, RMS]:\n",
    "    for file in arquivos_na_pasta:\n",
    "        tic = time.time()\n",
    "        valor, std  = quadrados_media_std(file,N,tamanho,metrica)\n",
    "        toc = time.time()\n",
    "\n",
    "    # 2. Salvar dados no dataframe.\n",
    "    df_temp[metrica.__name__ + '_valor'] = valor\n",
    "    df_temp[metrica.__name__ + '_std']   = std\n",
    "\n",
    "    # 3. Calcular os erros padrão, coloque também no dataframe.\n",
    "    df_temp[metrica.__name__ + '_erro']  = std/100 #std / sqrt(10.000)\n",
    "\n",
    "\n",
    "\n",
    "print(tamanho,N,\"\\nTempo de execução: \",toc-tic)\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp[HS.__name__ + '_valor'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "str()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A partir de https://en.wikipedia.org/wiki/Contrast_(vision)#Weber_contrast, colocar também o contraste de Weber, definido como:\n",
    "\n",
    "$$\\frac{I-I_b}{I_b},$$\n",
    "\n",
    "onde:\n",
    "\n",
    "$I$ representa a luminancia dos atributos e $I_b$ representa a luminância do background.\n",
    "> Weber contrast is commonly used in cases where small features are present on a large uniform background, i.e., where the average luminance is approximately equal to the background luminance.\n",
    "\n",
    "De acordo com https://www.schorsch.com/en/kbase/glossary/contrast.html:\n",
    "> Mathematically, the difference between the two luminances divided by the lower luminance . This definition is also called Weber Contrast, and is the most commonly useful one in the context of lighting.\n",
    "\n",
    "$$C = (L_{max} - L_{min}) / L_{min}.$$\n",
    "\n",
    "Na pergunta do Stack Overflow, https://stackoverflow.com/questions/5840665/how-to-implement-weber-contrast-of-an-image-in-matlab-octave, um usuário cria uma função específica (pra pergunta) para calcular o contraste Weber, mas creio que considera que temos uma imagem já separada em back e foreground.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Eu defini como sendo Foreground e Background, como na Monotonic Haziness e peguei o máximo e o mínimo deles, isto é:\n",
    "\n",
    "$$(Fore_{max} - Back_{min})/ Back_{min}.$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def weber(imagem,tamanho):\n",
    "    if type(imagem) == str:\n",
    "        imagem = cv2.imread(imagem,0) \n",
    "        \n",
    "    Back = recortar_quadrado(imagem, tamanho)\n",
    "    Fore = recortar_quadrado(imagem, tamanho)\n",
    "    maximo = np.max(Fore)\n",
    "    minimo = np.min(Back)\n",
    "    if minimo ==0:\n",
    "        minimo = 1\n",
    "#     print(maximo,minimo,abs(maximo-minimo)/minimo)\n",
    "    return abs(maximo-minimo)/minimo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Tempo de execução:  39.10515594482422\n",
      "media 254.0 e desvio padrão 0.0\n",
      "imagem:  Imagens\\recorte_leite_00.jpg\n",
      "\n",
      "Tempo de execução:  36.4482307434082\n",
      "media 127.56264687412977 e desvio padrão 126.43738485739853\n",
      "imagem:  Imagens\\recorte_leite_05.jpg\n",
      "\n",
      "Tempo de execução:  38.4020836353302\n",
      "media 85.16095217179657 e desvio padrão 119.38767372397346\n",
      "imagem:  Imagens\\recorte_leite_10.jpg\n",
      "\n",
      "Tempo de execução:  33.87049078941345\n",
      "media 63.92186055124961 e desvio padrão 109.74224214336664\n",
      "imagem:  Imagens\\recorte_leite_15.jpg\n",
      "\n",
      "Tempo de execução:  32.06375980377197\n",
      "media 51.16683460439801 e desvio padrão 101.41721026552376\n",
      "imagem:  Imagens\\recorte_leite_20.jpg\n",
      "\n",
      "Tempo de execução:  35.86782431602478\n",
      "media 42.65998569456575 e desvio padrão 94.51476714616129\n",
      "imagem:  Imagens\\recorte_leite_25.jpg\n",
      "\n",
      "Tempo de execução:  32.98757863044739\n",
      "media 36.58306796123524 e desvio padrão 88.7607237530539\n",
      "imagem:  Imagens\\recorte_leite_30.jpg\n",
      "\n",
      "Tempo de execução:  36.77192759513855\n",
      "media 32.027932243865074 e desvio padrão 83.89816863056362\n",
      "imagem:  Imagens\\recorte_leite_35.jpg\n",
      "\n",
      "Tempo de execução:  30.599260568618774\n",
      "media 28.49245367722023 e desvio padrão 79.72953881784811\n",
      "imagem:  Imagens\\recorte_leite_99.jpg\n"
     ]
    }
   ],
   "source": [
    "tamanho = 1000\n",
    "iteracoes = 10000\n",
    "N = iteracoes\n",
    "temp = []\n",
    "valor=[]\n",
    "valor_lista = []\n",
    "std_lista=[]\n",
    "\n",
    "for file in arquivos_na_pasta:\n",
    "    tic = time.time()\n",
    "    imagem = cv2.imread(file,0) \n",
    "    for _ in range(N):\n",
    "        valor.append(weber(imagem,tamanho))\n",
    "    media = np.mean(valor)\n",
    "    std = np.std(valor)\n",
    "    toc = time.time()\n",
    "    print(\"\\nTempo de execução: \",toc-tic)\n",
    "    print(f'media {media} e desvio padrão {std}')\n",
    "    print(\"imagem: \", file)\n",
    "    valor_lista.append(media)\n",
    "    std_lista.append(std)\n",
    "    \n",
    "df_copia['weber_valor'] = valor_lista\n",
    "df_copia['weber_std'] = std_lista\n",
    "df_copia['weber_erro'] = df_copia['weber_std']/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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