Tensorflow - Complete Tensorflow 2 and Keras Deep Learning Bootcamp - JP/04-CNNs/03-Deep-Learning-Custom-Images-Malaria.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Custom Images\n",
    "\n",
    "So far everything we've worked with has been nicely formatted for us already by Keras.\n",
    "\n",
    "Let's explore what its like to work with a more realistic data set.\n",
    "\n",
    "## The Data\n",
    "\n",
    "-----------\n",
    "\n",
    "ORIGINAL DATA SOURCE:\n",
    "\n",
    "The dataset contains 2 folders - Infected - Uninfected\n",
    "\n",
    "And a total of 27,558 images.\n",
    "\n",
    "Acknowledgements\n",
    "This Dataset is taken from the official NIH Website: https://ceb.nlm.nih.gov/repositories/malaria-datasets/ \n",
    "\n",
    "**Note: We will be dealing with real image files, NOT numpy arrays. Which means a large part of this process will be learning how to work with and deal with large groups of image files. This is too much data to fit in memory as a numpy array, so we'll need to feed it into our model in batches.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ [Data](#data)\n",
    "+ [Visualizing the Data](#Visualizing)\n",
    "    + [Read in Parasitized cell image](#Parasitized)\n",
    "    + [Read in Uninfected cell](#Uninfected)\n",
    "+ [Dimensions Distribution and Finding out Average Dimensions.](#averagediemnsion)\n",
    "    + [Let's find out the average dimensions of these images.](#findout)\n",
    "    + [Visualize the image dimesions distribution](#visualizediemnsion)\n",
    "    + [So why it is important to check the dimensions of the images?](#important)\n",
    "+ [Preparing the Data for the model](#preparingdata)\n",
    "    + [Image Manipulation](#imagemanipulation)\n",
    "        + [Why we want to manipulate the image data?](#whymanipulate)\n",
    "        + [So what is the good value for those random transformation parameters? How much should I rotate, stretch or sequeeze the images?](#howtochoose)\n",
    "       + [Deciding whether we need to rescale the image?](#deciderescale)\n",
    "    + [Image Data Generator](#imagedatagenerator)\n",
    "    + [Image Data Augmentation](#augmentation)\n",
    "    + [Generating many manipulated images from a directory](#manipulateddirectory)\n",
    "+ [Creating the Model](#modelcreation)\n",
    "+ [Training the Model](#training)\n",
    "+ [Evaluating the Model](#evaluation)\n",
    "+ [Predicting on an Imagel](#predictingsingleimage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name='data'>Data</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.image import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'C:\\\\Users\\\\Phone Thiri Yadana\\\\Desktop\\\\Dojo - Data Science and Machine Learning\\\\Complete Tensorflow 2 and Keras Deep Learning Bootcamp - JP\\\\04-CNNs\\\\cell_images\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test', 'train']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = data_dir + '\\\\train\\\\'\n",
    "test_path = data_dir + '\\\\test\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['parasitized', 'uninfected']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check which folders are inside this path\n",
    "os.listdir(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C100P61ThinF_IMG_20150918_144104_cell_162.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_163.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_164.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_165.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_166.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_167.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_168.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_169.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_170.png',\n",
       " 'C100P61ThinF_IMG_20150918_144104_cell_171.png',\n",
       " 'C100P61ThinF_IMG_20150918_144348_cell_138.png',\n",
       " 'C100P61ThinF_IMG_20150918_144348_cell_139.png',\n",
       " 'C100P61ThinF_IMG_20150918_144348_cell_140.png',\n",
       " 'C100P61ThinF_IMG_20150918_144348_cell_141.png',\n",
       " 'C100P61ThinF_IMG_20150918_144348_cell_142.png',\n",
       " 'C100P61ThinF_IMG_20150918_144348_cell_143.png',\n",
       " 'C100P61ThinF_IMG_20150918_144823_cell_157.png',\n",
       " 'C100P61ThinF_IMG_20150918_144823_cell_158.png',\n",
       " 'C100P61ThinF_IMG_20150918_144823_cell_159.png',\n",
       " 'C100P61ThinF_IMG_20150918_144823_cell_160.png',\n",
       " 'C100P61ThinF_IMG_20150918_144823_cell_161.png',\n",
       " 'C100P61ThinF_IMG_20150918_144823_cell_162.png',\n",
       " 'C100P61ThinF_IMG_20150918_145042_cell_162.png',\n",
       " 'C100P61ThinF_IMG_20150918_145042_cell_163.png',\n",
       " 'C100P61ThinF_IMG_20150918_145042_cell_164.png',\n",
       " 'C100P61ThinF_IMG_20150918_145042_cell_165.png',\n",
       " 'C100P61ThinF_IMG_20150918_145042_cell_166.png',\n",
       " 'C100P61ThinF_IMG_20150918_145042_cell_167.png',\n",
       " 'C100P61ThinF_IMG_20150918_145422_cell_163.png',\n",
       " 'C100P61ThinF_IMG_20150918_145422_cell_164.png',\n",
       " 'C100P61ThinF_IMG_20150918_145422_cell_166.png',\n",
       " 'C100P61ThinF_IMG_20150918_145422_cell_167.png',\n",
       " 'C100P61ThinF_IMG_20150918_145422_cell_168.png',\n",
       " 'C100P61ThinF_IMG_20150918_145422_cell_169.png',\n",
       " 'C100P61ThinF_IMG_20150918_145609_cell_144.png',\n",
       " 'C100P61ThinF_IMG_20150918_145609_cell_146.png',\n",
       " 'C100P61ThinF_IMG_20150918_145609_cell_147.png',\n",
       " 'C100P61ThinF_IMG_20150918_145609_cell_148.png',\n",
       " 'C100P61ThinF_IMG_20150918_145609_cell_151.png',\n",
       " 'C100P61ThinF_IMG_20150918_145938_cell_167.png',\n",
       " 'C100P61ThinF_IMG_20150918_145938_cell_168.png',\n",
       " 'C100P61ThinF_IMG_20150918_145938_cell_169.png',\n",
       " 'C100P61ThinF_IMG_20150918_145938_cell_170.png',\n",
       " 'C100P61ThinF_IMG_20150918_145938_cell_171.png',\n",
       " 'C100P61ThinF_IMG_20150918_145938_cell_172.png',\n",
       " 'C100P61ThinF_IMG_20150918_145938_cell_173.png',\n",
       " 'C100P61ThinF_IMG_20150918_145938_cell_174.png',\n",
       " 'C100P61ThinF_IMG_20150918_150041_cell_146.png',\n",
       " 'C100P61ThinF_IMG_20150918_150041_cell_147.png',\n",
       " 'C100P61ThinF_IMG_20150918_150041_cell_149.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_61.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_62.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_63.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_65.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_66.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_67.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_68.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_69.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_70.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_71.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_72.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_73.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_75.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_78.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_79.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_80.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_81.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_82.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_83.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_84.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_85.png',\n",
       " 'C101P62ThinF_IMG_20150918_151006_cell_86.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_68.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_69.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_70.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_71.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_72.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_73.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_75.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_78.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_79.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_80.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_81.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_82.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_83.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_84.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_85.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_86.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_87.png',\n",
       " 'C101P62ThinF_IMG_20150918_151149_cell_88.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_100.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_75.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_78.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_79.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_81.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_82.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_83.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_84.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_85.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_86.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_87.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_89.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_90.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_91.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_92.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_93.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_94.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_95.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_96.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_97.png',\n",
       " 'C101P62ThinF_IMG_20150918_151239_cell_98.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_51.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_52.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_53.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_54.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_55.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_56.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_57.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_59.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_60.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_61.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_62.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_63.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_64.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_65.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_67.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_68.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_69.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_70.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_71.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_72.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_73.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_75.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150918_151335_cell_78.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_49.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_50.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_51.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_52.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_53.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_54.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_55.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_56.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_57.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_58.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_59.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_61.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_62.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_64.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_66.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_67.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_68.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_69.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_71.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_72.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150918_151507_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_58.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_59.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_60.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_61.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_62.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_64.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_66.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_67.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_68.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_69.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_71.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_72.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_73.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_75.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_78.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_79.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_80.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_81.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_82.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_83.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_84.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_85.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_88.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_89.png',\n",
       " 'C101P62ThinF_IMG_20150918_151942_cell_90.png',\n",
       " 'C101P62ThinF_IMG_20150918_155026_cell_1.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_61.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_63.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_64.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_65.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_68.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_69.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_70.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_71.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_73.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_75.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_78.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_79.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_80.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_81.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_82.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_83.png',\n",
       " 'C101P62ThinF_IMG_20150918_155731_cell_84.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_1.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_10.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_11.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_12.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_13.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_14.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_15.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_16.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_17.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_19.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_20.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_21.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_22.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_23.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_24.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_25.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_26.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_27.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_28.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_29.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_3.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_30.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_32.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_33.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_34.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_35.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_37.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_38.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_39.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_4.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_40.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_41.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_5.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_6.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_7.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_8.png',\n",
       " 'C101P62ThinF_IMG_20150923_165215_cell_9.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_1.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_10.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_11.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_12.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_14.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_15.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_16.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_17.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_18.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_19.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_2.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_20.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_21.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_22.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_23.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_24.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_3.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_4.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_5.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_6.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_7.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_8.png',\n",
       " 'C101P62ThinF_IMG_20150923_170110_cell_9.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_62.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_63.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_64.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_65.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_66.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_67.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_68.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_70.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_71.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_72.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_75.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_78.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_79.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_80.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_81.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_82.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_83.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_84.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_85.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_87.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_88.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_89.png',\n",
       " 'C101P62ThinF_IMG_20150923_170246_cell_90.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_100.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_71.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_72.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_73.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_74.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_75.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_76.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_77.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_78.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_79.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_80.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_81.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_82.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_84.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_86.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_87.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_88.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_89.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_90.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_91.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_92.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_93.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_94.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_95.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_97.png',\n",
       " 'C101P62ThinF_IMG_20150923_170344_cell_98.png',\n",
       " 'C102P63ThinF_IMG_20150918_161508_cell_190.png',\n",
       " 'C102P63ThinF_IMG_20150918_161508_cell_191.png',\n",
       " 'C102P63ThinF_IMG_20150918_161508_cell_192.png',\n",
       " 'C102P63ThinF_IMG_20150918_161508_cell_193.png',\n",
       " 'C102P63ThinF_IMG_20150918_161508_cell_194.png',\n",
       " 'C102P63ThinF_IMG_20150918_161508_cell_195.png',\n",
       " 'C102P63ThinF_IMG_20150918_161826_cell_170.png',\n",
       " 'C102P63ThinF_IMG_20150918_161826_cell_171.png',\n",
       " 'C102P63ThinF_IMG_20150918_161826_cell_172.png',\n",
       " 'C102P63ThinF_IMG_20150918_162051_cell_141.png',\n",
       " 'C102P63ThinF_IMG_20150918_162051_cell_142.png',\n",
       " 'C102P63ThinF_IMG_20150918_162051_cell_143.png',\n",
       " 'C102P63ThinF_IMG_20150918_162051_cell_144.png',\n",
       " 'C102P63ThinF_IMG_20150918_162051_cell_145.png',\n",
       " 'C102P63ThinF_IMG_20150918_162051_cell_146.png',\n",
       " 'C102P63ThinF_IMG_20150918_162051_cell_147.png',\n",
       " 'C102P63ThinF_IMG_20150918_162155_cell_132.png',\n",
       " 'C102P63ThinF_IMG_20150918_162155_cell_133.png',\n",
       " 'C102P63ThinF_IMG_20150918_162300_cell_180.png',\n",
       " 'C102P63ThinF_IMG_20150918_162300_cell_181.png',\n",
       " 'C102P63ThinF_IMG_20150918_162300_cell_182.png',\n",
       " 'C102P63ThinF_IMG_20150918_162511_cell_169.png',\n",
       " 'C102P63ThinF_IMG_20150918_162511_cell_170.png',\n",
       " 'C102P63ThinF_IMG_20150918_162511_cell_171.png',\n",
       " 'C102P63ThinF_IMG_20150918_162511_cell_172.png',\n",
       " 'C102P63ThinF_IMG_20150918_162511_cell_173.png',\n",
       " 'C102P63ThinF_IMG_20150918_162927_cell_1.png',\n",
       " 'C102P63ThinF_IMG_20150918_162927_cell_2.png',\n",
       " 'C102P63ThinF_IMG_20150918_162927_cell_3.png',\n",
       " 'C102P63ThinF_IMG_20150918_162927_cell_4.png',\n",
       " 'C102P63ThinF_IMG_20150918_162927_cell_5.png',\n",
       " 'C102P63ThinF_IMG_20150918_163054_cell_167.png',\n",
       " 'C102P63ThinF_IMG_20150918_163054_cell_168.png',\n",
       " 'C102P63ThinF_IMG_20150918_163054_cell_169.png',\n",
       " 'C102P63ThinF_IMG_20150918_163054_cell_170.png',\n",
       " 'C103P64ThinF_IMG_20150918_164250_cell_170.png',\n",
       " 'C103P64ThinF_IMG_20150918_164250_cell_172.png',\n",
       " 'C103P64ThinF_IMG_20150918_164250_cell_173.png',\n",
       " 'C103P64ThinF_IMG_20150918_164250_cell_174.png',\n",
       " 'C103P64ThinF_IMG_20150918_164331_cell_185.png',\n",
       " 'C103P64ThinF_IMG_20150918_164331_cell_186.png',\n",
       " 'C103P64ThinF_IMG_20150918_164331_cell_187.png',\n",
       " 'C103P64ThinF_IMG_20150918_164553_cell_167.png',\n",
       " 'C103P64ThinF_IMG_20150918_164553_cell_168.png',\n",
       " 'C103P64ThinF_IMG_20150918_164553_cell_169.png',\n",
       " 'C103P64ThinF_IMG_20150918_164553_cell_170.png',\n",
       " 'C103P64ThinF_IMG_20150918_164553_cell_172.png',\n",
       " 'C103P64ThinF_IMG_20150918_164553_cell_173.png',\n",
       " 'C103P64ThinF_IMG_20150918_165016_cell_158.png',\n",
       " 'C103P64ThinF_IMG_20150918_165016_cell_159.png',\n",
       " 'C103P64ThinF_IMG_20150918_165016_cell_160.png',\n",
       " 'C103P64ThinF_IMG_20150918_165016_cell_161.png',\n",
       " 'C103P64ThinF_IMG_20150918_165016_cell_162.png',\n",
       " 'C103P64ThinF_IMG_20150918_165125_cell_163.png',\n",
       " 'C103P64ThinF_IMG_20150918_165125_cell_164.png',\n",
       " 'C103P64ThinF_IMG_20150918_165125_cell_165.png',\n",
       " 'C103P64ThinF_IMG_20150918_165125_cell_166.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_176.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_177.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_178.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_179.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_180.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_181.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_182.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_183.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_184.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_185.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_186.png',\n",
       " 'C103P64ThinF_IMG_20150918_165510_cell_188.png',\n",
       " 'C104P65ThinF_IMG_20150918_170850_cell_204.png',\n",
       " 'C104P65ThinF_IMG_20150918_171154_cell_212.png',\n",
       " 'C104P65ThinF_IMG_20150918_171154_cell_213.png',\n",
       " 'C104P65ThinF_IMG_20150918_171751_cell_186.png',\n",
       " 'C104P65ThinF_IMG_20150918_171751_cell_187.png',\n",
       " 'C104P65ThinF_IMG_20150918_172053_cell_187.png',\n",
       " 'C104P65ThinF_IMG_20150918_172537_cell_149.png',\n",
       " 'C104P65ThinF_IMG_20150918_172639_cell_182.png',\n",
       " 'C104P65ThinF_IMG_20150918_172639_cell_184.png',\n",
       " 'C105P66ThinF_IMG_20150924_094800_cell_136.png',\n",
       " 'C105P66ThinF_IMG_20150924_094800_cell_137.png',\n",
       " 'C105P66ThinF_IMG_20150924_095034_cell_148.png',\n",
       " 'C105P66ThinF_IMG_20150924_095034_cell_149.png',\n",
       " 'C105P66ThinF_IMG_20150924_095034_cell_150.png',\n",
       " 'C105P66ThinF_IMG_20150924_095130_cell_155.png',\n",
       " 'C105P66ThinF_IMG_20150924_095130_cell_156.png',\n",
       " 'C105P66ThinF_IMG_20150924_095130_cell_157.png',\n",
       " 'C105P66ThinF_IMG_20150924_095130_cell_158.png',\n",
       " 'C105P66ThinF_IMG_20150924_095633_cell_126.png',\n",
       " 'C105P66ThinF_IMG_20150924_095716_cell_113.png',\n",
       " 'C105P66ThinF_IMG_20150924_095716_cell_114.png',\n",
       " 'C105P66ThinF_IMG_20150924_095913_cell_117.png',\n",
       " 'C105P66ThinF_IMG_20150924_095913_cell_118.png',\n",
       " 'C105P66ThinF_IMG_20150924_100655_cell_123.png',\n",
       " 'C105P66ThinF_IMG_20150924_100655_cell_124.png',\n",
       " 'C105P66ThinF_IMG_20150924_100655_cell_125.png',\n",
       " 'C106P67ThinF_IMG_20150924_102245_cell_164.png',\n",
       " 'C106P67ThinF_IMG_20150924_102245_cell_165.png',\n",
       " 'C106P67ThinF_IMG_20150924_102245_cell_166.png',\n",
       " 'C106P67ThinF_IMG_20150924_102413_cell_190.png',\n",
       " 'C106P67ThinF_IMG_20150924_102826_cell_143.png',\n",
       " 'C106P67ThinF_IMG_20150924_103245_cell_143.png',\n",
       " 'C106P67ThinF_IMG_20150924_103811_cell_180.png',\n",
       " 'C106P67ThinF_IMG_20150924_103811_cell_181.png',\n",
       " 'C106P67ThinF_IMG_20150924_103811_cell_182.png',\n",
       " 'C106P67ThinF_IMG_20150924_103811_cell_183.png',\n",
       " 'C106P67ThinF_IMG_20150924_104044_cell_167.png',\n",
       " 'C106P67ThinF_IMG_20150924_104044_cell_168.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_169.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_170.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_172.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_173.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_174.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_175.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_176.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_177.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_178.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_179.png',\n",
       " 'C107P68ThinF_IMG_20150924_221632_cell_180.png',\n",
       " 'C107P68ThinF_IMG_20150924_221909_cell_156.png',\n",
       " 'C107P68ThinF_IMG_20150924_221909_cell_159.png',\n",
       " 'C107P68ThinF_IMG_20150924_221909_cell_160.png',\n",
       " 'C107P68ThinF_IMG_20150924_221909_cell_161.png',\n",
       " 'C107P68ThinF_IMG_20150924_221909_cell_162.png',\n",
       " 'C107P68ThinF_IMG_20150924_221909_cell_163.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_150.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_151.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_153.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_154.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_156.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_157.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_158.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_159.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_160.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_161.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_162.png',\n",
       " 'C107P68ThinF_IMG_20150924_222233_cell_163.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_172.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_173.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_174.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_175.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_176.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_177.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_178.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_179.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_180.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_181.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_182.png',\n",
       " 'C107P68ThinF_IMG_20150924_222415_cell_183.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_139.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_140.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_141.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_142.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_143.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_144.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_145.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_146.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_147.png',\n",
       " 'C107P68ThinF_IMG_20150924_222535_cell_148.png',\n",
       " 'C107P68ThinF_IMG_20150924_222630_cell_174.png',\n",
       " 'C107P68ThinF_IMG_20150924_222630_cell_175.png',\n",
       " 'C107P68ThinF_IMG_20150924_222630_cell_176.png',\n",
       " 'C107P68ThinF_IMG_20150924_222630_cell_177.png',\n",
       " 'C108P69ThinF_IMG_20150924_223834_cell_218.png',\n",
       " 'C108P69ThinF_IMG_20150924_223834_cell_219.png',\n",
       " 'C108P69ThinF_IMG_20150924_223834_cell_220.png',\n",
       " 'C108P69ThinF_IMG_20150924_223834_cell_221.png',\n",
       " 'C108P69ThinF_IMG_20150924_224038_cell_188.png',\n",
       " 'C108P69ThinF_IMG_20150924_224038_cell_189.png',\n",
       " 'C108P69ThinF_IMG_20150924_224038_cell_190.png',\n",
       " 'C108P69ThinF_IMG_20150924_224616_cell_195.png',\n",
       " 'C108P69ThinF_IMG_20150924_224616_cell_196.png',\n",
       " 'C108P69ThinF_IMG_20150924_224616_cell_197.png',\n",
       " 'C108P69ThinF_IMG_20150924_224616_cell_198.png',\n",
       " 'C108P69ThinF_IMG_20150924_224909_cell_226.png',\n",
       " 'C108P69ThinF_IMG_20150924_225004_cell_221.png',\n",
       " 'C108P69ThinF_IMG_20150924_225004_cell_222.png',\n",
       " 'C108P69ThinF_IMG_20150924_225004_cell_223.png',\n",
       " 'C108P69ThinF_IMG_20150924_225004_cell_224.png',\n",
       " 'C108P69ThinF_IMG_20150924_225004_cell_225.png',\n",
       " 'C108P69ThinF_IMG_20150924_225511_cell_182.png',\n",
       " 'C108P69ThinF_IMG_20150924_225511_cell_183.png',\n",
       " 'C108P69ThinF_IMG_20150924_225511_cell_184.png',\n",
       " 'C109P70ThinF_IMG_20150930_102716_cell_185.png',\n",
       " 'C109P70ThinF_IMG_20150930_102716_cell_186.png',\n",
       " 'C109P70ThinF_IMG_20150930_102716_cell_187.png',\n",
       " 'C109P70ThinF_IMG_20150930_102944_cell_171.png',\n",
       " 'C109P70ThinF_IMG_20150930_102944_cell_172.png',\n",
       " 'C109P70ThinF_IMG_20150930_103113_cell_160.png',\n",
       " 'C109P70ThinF_IMG_20150930_103113_cell_162.png',\n",
       " 'C109P70ThinF_IMG_20150930_103214_cell_182.png',\n",
       " 'C109P70ThinF_IMG_20150930_103214_cell_183.png',\n",
       " 'C109P70ThinF_IMG_20150930_103214_cell_184.png',\n",
       " 'C109P70ThinF_IMG_20150930_103214_cell_185.png',\n",
       " 'C109P70ThinF_IMG_20150930_103645_cell_172.png',\n",
       " 'C109P70ThinF_IMG_20150930_103645_cell_173.png',\n",
       " 'C109P70ThinF_IMG_20150930_103645_cell_174.png',\n",
       " 'C109P70ThinF_IMG_20150930_103645_cell_175.png',\n",
       " 'C109P70ThinF_IMG_20150930_103811_cell_157.png',\n",
       " 'C109P70ThinF_IMG_20150930_103811_cell_158.png',\n",
       " 'C110P71ThinF_IMG_20150930_105319_cell_185.png',\n",
       " 'C110P71ThinF_IMG_20150930_105319_cell_186.png',\n",
       " 'C110P71ThinF_IMG_20150930_105319_cell_187.png',\n",
       " 'C110P71ThinF_IMG_20150930_105319_cell_188.png',\n",
       " 'C110P71ThinF_IMG_20150930_105319_cell_189.png',\n",
       " 'C110P71ThinF_IMG_20150930_105319_cell_190.png',\n",
       " 'C110P71ThinF_IMG_20150930_105559_cell_199.png',\n",
       " 'C110P71ThinF_IMG_20150930_105559_cell_200.png',\n",
       " 'C110P71ThinF_IMG_20150930_105559_cell_201.png',\n",
       " 'C110P71ThinF_IMG_20150930_105559_cell_202.png',\n",
       " 'C110P71ThinF_IMG_20150930_105729_cell_207.png',\n",
       " 'C110P71ThinF_IMG_20150930_105925_cell_221.png',\n",
       " 'C110P71ThinF_IMG_20150930_110052_cell_238.png',\n",
       " 'C110P71ThinF_IMG_20150930_110052_cell_239.png',\n",
       " 'C110P71ThinF_IMG_20150930_110052_cell_240.png',\n",
       " 'C110P71ThinF_IMG_20150930_110052_cell_241.png',\n",
       " 'C110P71ThinF_IMG_20150930_110052_cell_242.png',\n",
       " 'C110P71ThinF_IMG_20150930_110052_cell_243.png',\n",
       " 'C110P71ThinF_IMG_20150930_110404_cell_157.png',\n",
       " 'C110P71ThinF_IMG_20150930_110404_cell_158.png',\n",
       " 'C110P71ThinF_IMG_20150930_110404_cell_159.png',\n",
       " 'C110P71ThinF_IMG_20150930_110404_cell_160.png',\n",
       " 'C111P72ThinF_IMG_20150930_112430_cell_191.png',\n",
       " 'C111P72ThinF_IMG_20150930_112430_cell_192.png',\n",
       " 'C111P72ThinF_IMG_20150930_112430_cell_193.png',\n",
       " 'C111P72ThinF_IMG_20150930_112609_cell_169.png',\n",
       " 'C111P72ThinF_IMG_20150930_112609_cell_170.png',\n",
       " 'C111P72ThinF_IMG_20150930_112827_cell_176.png',\n",
       " 'C111P72ThinF_IMG_20150930_112827_cell_177.png',\n",
       " 'C111P72ThinF_IMG_20150930_112827_cell_178.png',\n",
       " 'C111P72ThinF_IMG_20150930_112827_cell_179.png',\n",
       " 'C111P72ThinF_IMG_20150930_113003_cell_182.png',\n",
       " 'C111P72ThinF_IMG_20150930_113049_cell_162.png',\n",
       " 'C111P72ThinF_IMG_20150930_113049_cell_163.png',\n",
       " 'C111P72ThinF_IMG_20150930_113049_cell_164.png',\n",
       " 'C111P72ThinF_IMG_20150930_113049_cell_165.png',\n",
       " 'C111P72ThinF_IMG_20150930_113049_cell_166.png',\n",
       " 'C111P72ThinF_IMG_20150930_113210_cell_171.png',\n",
       " 'C111P72ThinF_IMG_20150930_113610_cell_202.png',\n",
       " 'C111P72ThinF_IMG_20150930_113610_cell_203.png',\n",
       " 'C112P73ThinF_IMG_20150930_131254_cell_184.png',\n",
       " 'C112P73ThinF_IMG_20150930_131401_cell_165.png',\n",
       " 'C112P73ThinF_IMG_20150930_131401_cell_166.png',\n",
       " 'C112P73ThinF_IMG_20150930_131401_cell_167.png',\n",
       " 'C112P73ThinF_IMG_20150930_131401_cell_168.png',\n",
       " 'C112P73ThinF_IMG_20150930_131516_cell_177.png',\n",
       " 'C112P73ThinF_IMG_20150930_131516_cell_178.png',\n",
       " 'C112P73ThinF_IMG_20150930_131605_cell_181.png',\n",
       " 'C112P73ThinF_IMG_20150930_131605_cell_182.png',\n",
       " 'C112P73ThinF_IMG_20150930_131605_cell_183.png',\n",
       " 'C112P73ThinF_IMG_20150930_131659_cell_193.png',\n",
       " 'C112P73ThinF_IMG_20150930_131753_cell_167.png',\n",
       " 'C112P73ThinF_IMG_20150930_131900_cell_174.png',\n",
       " 'C112P73ThinF_IMG_20150930_131900_cell_175.png',\n",
       " 'C113P74ThinF_IMG_20150930_134355_cell_165.png',\n",
       " 'C113P74ThinF_IMG_20150930_134355_cell_166.png',\n",
       " 'C113P74ThinF_IMG_20150930_134355_cell_167.png',\n",
       " 'C113P74ThinF_IMG_20150930_134355_cell_168.png',\n",
       " 'C113P74ThinF_IMG_20150930_134355_cell_169.png',\n",
       " 'C113P74ThinF_IMG_20150930_134355_cell_170.png',\n",
       " 'C113P74ThinF_IMG_20150930_134355_cell_171.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_176.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_177.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_178.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_179.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_180.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_181.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_182.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_183.png',\n",
       " 'C113P74ThinF_IMG_20150930_134429_cell_184.png',\n",
       " 'C113P74ThinF_IMG_20150930_134614_cell_169.png',\n",
       " 'C113P74ThinF_IMG_20150930_134614_cell_170.png',\n",
       " 'C113P74ThinF_IMG_20150930_134614_cell_171.png',\n",
       " 'C113P74ThinF_IMG_20150930_134614_cell_174.png',\n",
       " 'C113P74ThinF_IMG_20150930_134614_cell_175.png',\n",
       " 'C113P74ThinF_IMG_20150930_134614_cell_176.png',\n",
       " 'C113P74ThinF_IMG_20150930_134751_cell_173.png',\n",
       " 'C113P74ThinF_IMG_20150930_134751_cell_174.png',\n",
       " 'C113P74ThinF_IMG_20150930_134751_cell_176.png',\n",
       " 'C113P74ThinF_IMG_20150930_134751_cell_177.png',\n",
       " 'C113P74ThinF_IMG_20150930_134751_cell_179.png',\n",
       " 'C113P74ThinF_IMG_20150930_134751_cell_180.png',\n",
       " 'C113P74ThinF_IMG_20150930_134751_cell_181.png',\n",
       " 'C113P74ThinF_IMG_20150930_134949_cell_158.png',\n",
       " 'C113P74ThinF_IMG_20150930_134949_cell_159.png',\n",
       " 'C113P74ThinF_IMG_20150930_134949_cell_160.png',\n",
       " 'C113P74ThinF_IMG_20150930_134949_cell_162.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_180.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_181.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_182.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_183.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_184.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_185.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_187.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_188.png',\n",
       " 'C113P74ThinF_IMG_20150930_135904_cell_189.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_181.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_182.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_183.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_184.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_185.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_186.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_187.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_188.png',\n",
       " 'C113P74ThinF_IMG_20150930_140646_cell_190.png',\n",
       " 'C114P75ThinF_IMG_20150930_150013_cell_195.png',\n",
       " 'C114P75ThinF_IMG_20150930_150013_cell_196.png',\n",
       " 'C114P75ThinF_IMG_20150930_150057_cell_172.png',\n",
       " 'C114P75ThinF_IMG_20150930_150145_cell_159.png',\n",
       " 'C114P75ThinF_IMG_20150930_150145_cell_160.png',\n",
       " 'C114P75ThinF_IMG_20150930_150555_cell_145.png',\n",
       " 'C114P75ThinF_IMG_20150930_150733_cell_149.png',\n",
       " 'C114P75ThinF_IMG_20150930_150733_cell_150.png',\n",
       " 'C114P75ThinF_IMG_20150930_150833_cell_164.png',\n",
       " 'C114P75ThinF_IMG_20150930_150833_cell_165.png',\n",
       " 'C114P75ThinF_IMG_20150930_151150_cell_177.png',\n",
       " 'C114P75ThinF_IMG_20150930_151150_cell_178.png',\n",
       " 'C114P75ThinF_IMG_20150930_151150_cell_179.png',\n",
       " 'C115P76ThinF_IMG_20150930_161825_cell_219.png',\n",
       " 'C115P76ThinF_IMG_20150930_161825_cell_221.png',\n",
       " 'C115P76ThinF_IMG_20150930_162038_cell_206.png',\n",
       " 'C115P76ThinF_IMG_20150930_162038_cell_207.png',\n",
       " 'C115P76ThinF_IMG_20150930_162038_cell_208.png',\n",
       " 'C115P76ThinF_IMG_20150930_162038_cell_209.png',\n",
       " 'C115P76ThinF_IMG_20150930_162038_cell_210.png',\n",
       " 'C115P76ThinF_IMG_20150930_162315_cell_256.png',\n",
       " 'C115P76ThinF_IMG_20150930_162315_cell_257.png',\n",
       " 'C115P76ThinF_IMG_20150930_163018_cell_252.png',\n",
       " 'C115P76ThinF_IMG_20150930_163156_cell_242.png',\n",
       " 'C115P76ThinF_IMG_20150930_163156_cell_243.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_100.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_101.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_102.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_104.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_105.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_106.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_107.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_109.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_110.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_111.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_114.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_76.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_78.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_79.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_80.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_81.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_82.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_83.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_84.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_85.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_86.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_87.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_89.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_90.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_91.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_92.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_93.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_94.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_95.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_96.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_97.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_98.png',\n",
       " 'C116P77ThinF_IMG_20150930_171219_cell_99.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_100.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_101.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_102.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_103.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_104.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_105.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_106.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_107.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_108.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_109.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_110.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_111.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_112.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_113.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_115.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_116.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_117.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_118.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_119.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_120.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_121.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_122.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_123.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_124.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_125.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_126.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_127.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_129.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_130.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_131.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_132.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_83.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_84.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_86.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_89.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_90.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_91.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_92.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_93.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_94.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_95.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_96.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_97.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_98.png',\n",
       " 'C116P77ThinF_IMG_20150930_171448_cell_99.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_101.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_102.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_104.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_105.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_106.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_107.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_108.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_109.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_110.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_111.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_112.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_115.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_116.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_117.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_118.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_119.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_120.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_121.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_122.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_123.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_126.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_128.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_129.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_130.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_132.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_133.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_134.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_135.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_136.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_137.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_138.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_139.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_140.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_141.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_142.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_144.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_145.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_146.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_147.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_148.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_150.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_151.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_89.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_90.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_91.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_92.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_93.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_95.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_96.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_97.png',\n",
       " 'C116P77ThinF_IMG_20150930_171558_cell_98.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_100.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_101.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_102.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_103.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_105.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_106.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_107.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_108.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_109.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_110.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_111.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_112.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_113.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_114.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_115.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_70.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_71.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_72.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_73.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_74.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_75.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_76.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_77.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_78.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_79.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_80.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_81.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_83.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_84.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_85.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_86.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_87.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_89.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_90.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_91.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_92.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_93.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_94.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_95.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_96.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_97.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_98.png',\n",
       " 'C116P77ThinF_IMG_20150930_171635_cell_99.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_100.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_101.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_102.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_103.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_104.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_105.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_106.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_107.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_108.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_111.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_112.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_113.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_114.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_115.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_116.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_117.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_118.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_75.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_76.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_77.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_78.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_80.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_81.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_82.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_84.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_86.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_87.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_88.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_90.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_91.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_92.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_93.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_94.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_95.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_96.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_97.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_98.png',\n",
       " 'C116P77ThinF_IMG_20150930_171739_cell_99.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_58.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_59.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_60.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_61.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_62.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_64.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_65.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_66.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_67.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_68.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_69.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_70.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_71.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_72.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_73.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_74.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_75.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_76.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_77.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_78.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_79.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_80.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_81.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_82.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_83.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_84.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_85.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_86.png',\n",
       " 'C116P77ThinF_IMG_20150930_171809_cell_87.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_100.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_101.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_102.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_103.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_104.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_105.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_106.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_107.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_108.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_109.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_110.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_111.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_112.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_113.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_114.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_115.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_116.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_117.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_118.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_119.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_120.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_121.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_122.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_123.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_124.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_126.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_127.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_128.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_82.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_83.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_84.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_85.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_86.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_87.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_88.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_89.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_90.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_91.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_93.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_94.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_95.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_96.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_97.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_98.png',\n",
       " 'C116P77ThinF_IMG_20150930_171844_cell_99.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_100.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_101.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_102.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_103.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_104.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_105.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_107.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_108.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_109.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_111.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_112.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_113.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_115.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_73.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_74.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_75.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_76.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_77.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_78.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_79.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_80.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_81.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_82.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_83.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_84.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_85.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_86.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_87.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_88.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_90.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_91.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_92.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_93.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_94.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_95.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_96.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_97.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_98.png',\n",
       " 'C116P77ThinF_IMG_20150930_171954_cell_99.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_100.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_101.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_102.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_103.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_104.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_105.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_106.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_107.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_109.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_111.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_112.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_113.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_114.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_115.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_65.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_66.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_67.png',\n",
       " 'C116P77ThinF_IMG_20150930_172112_cell_68.png',\n",
       " ...]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path+'/parasitized/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name='Visualizing'>Visualizing the Data</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C100P61ThinF_IMG_20150918_144104_cell_162.png'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path+'/parasitized')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "para_cell = train_path + '/parasitized/' + 'C100P61ThinF_IMG_20150918_144104_cell_162.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='Parasitized'>Read in Parasitized cell image</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imread(para_cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148, 142, 3)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parasitized_cell = imread(para_cell)\n",
    "parasitized_cell.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(parasitized_cell);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='Uninfected'>Read in Uninfected cell</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['parasitized', 'uninfected']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C100P61ThinF_IMG_20150918_144104_cell_128.png'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path + '/uninfected')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "uninfected_cell_path = train_path + '/uninfected/C100P61ThinF_IMG_20150918_144104_cell_128.png' \n",
    "unifected_cell = imread(uninfected_cell_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(imread(uninfected_cell_path));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's check how many images there are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'parasitized'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'uninfected'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(test_path+'parasitized'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1300"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(test_path+'uninfected'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name='averagediemnsion'>Dimensions Distribution and Finding out Average Dimensions.</a>\n",
    "\n",
    "## <a name='findout'>Let's find out the average dimensions of these images.</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In real life datasets, the images can come in difference shape and dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(139, 160, 3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unifected_cell.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loop through every images of uninfected images to find out average dimensions\n",
    "dim1 = []\n",
    "dim2 = []\n",
    "\n",
    "for img_filename in os.listdir(test_path+'uninfected'):\n",
    "    img = imread(test_path + '/uninfected/' + img_filename)\n",
    "    d1,d2,colors = img.shape\n",
    "    \n",
    "    dim1.append(d1)\n",
    "    dim2.append(d2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[118,\n",
       " 172,\n",
       " 130,\n",
       " 163,\n",
       " 172,\n",
       " 139,\n",
       " 151,\n",
       " 160,\n",
       " 145,\n",
       " 127,\n",
       " 121,\n",
       " 154,\n",
       " 145,\n",
       " 127,\n",
       " 154,\n",
       " 151,\n",
       " 148,\n",
       " 130,\n",
       " 160,\n",
       " 136,\n",
       " 139,\n",
       " 190,\n",
       " 127,\n",
       " 133,\n",
       " 136,\n",
       " 124,\n",
       " 151,\n",
       " 151,\n",
       " 151,\n",
       " 160,\n",
       " 118,\n",
       " 154,\n",
       " 154,\n",
       " 121,\n",
       " 127,\n",
       " 118,\n",
       " 130,\n",
       " 124,\n",
       " 163,\n",
       " 88,\n",
       " 139,\n",
       " 127,\n",
       " 121,\n",
       " 139,\n",
       " 166,\n",
       " 133,\n",
       " 124,\n",
       " 154,\n",
       " 139,\n",
       " 127,\n",
       " 124,\n",
       " 136,\n",
       " 166,\n",
       " 133,\n",
       " 145,\n",
       " 157,\n",
       " 181,\n",
       " 148,\n",
       " 112,\n",
       " 124,\n",
       " 112,\n",
       " 106,\n",
       " 121,\n",
       " 148,\n",
       " 148,\n",
       " 112,\n",
       " 121,\n",
       " 148,\n",
       " 97,\n",
       " 112,\n",
       " 142,\n",
       " 118,\n",
       " 130,\n",
       " 151,\n",
       " 133,\n",
       " 136,\n",
       " 115,\n",
       " 145,\n",
       " 115,\n",
       " 142,\n",
       " 136,\n",
       " 130,\n",
       " 142,\n",
       " 124,\n",
       " 142,\n",
       " 136,\n",
       " 166,\n",
       " 148,\n",
       " 136,\n",
       " 142,\n",
       " 133,\n",
       " 130,\n",
       " 139,\n",
       " 124,\n",
       " 139,\n",
       " 121,\n",
       " 130,\n",
       " 142,\n",
       " 133,\n",
       " 118,\n",
       " 157,\n",
       " 127,\n",
       " 109,\n",
       " 112,\n",
       " 139,\n",
       " 142,\n",
       " 142,\n",
       " 112,\n",
       " 118,\n",
       " 100,\n",
       " 127,\n",
       " 145,\n",
       " 136,\n",
       " 160,\n",
       " 187,\n",
       " 130,\n",
       " 130,\n",
       " 157,\n",
       " 130,\n",
       " 130,\n",
       " 151,\n",
       " 175,\n",
       " 145,\n",
       " 136,\n",
       " 157,\n",
       " 133,\n",
       " 127,\n",
       " 154,\n",
       " 133,\n",
       " 151,\n",
       " 130,\n",
       " 136,\n",
       " 160,\n",
       " 130,\n",
       " 139,\n",
       " 145,\n",
       " 145,\n",
       " 145,\n",
       " 154,\n",
       " 133,\n",
       " 160,\n",
       " 142,\n",
       " 157,\n",
       " 145,\n",
       " 136,\n",
       " 121,\n",
       " 115,\n",
       " 121,\n",
       " 121,\n",
       " 127,\n",
       " 145,\n",
       " 130,\n",
       " 133,\n",
       " 142,\n",
       " 127,\n",
       " 121,\n",
       " 139,\n",
       " 118,\n",
       " 118,\n",
       " 148,\n",
       " 121,\n",
       " 145,\n",
       " 130,\n",
       " 169,\n",
       " 142,\n",
       " 136,\n",
       " 148,\n",
       " 142,\n",
       " 169,\n",
       " 115,\n",
       " 109,\n",
       " 166,\n",
       " 145,\n",
       " 127,\n",
       " 124,\n",
       " 124,\n",
       " 130,\n",
       " 196,\n",
       " 97,\n",
       " 115,\n",
       " 133,\n",
       " 139,\n",
       " 148,\n",
       " 109,\n",
       " 127,\n",
       " 175,\n",
       " 148,\n",
       " 136,\n",
       " 175,\n",
       " 151,\n",
       " 154,\n",
       " 133,\n",
       " 145,\n",
       " 115,\n",
       " 130,\n",
       " 121,\n",
       " 133,\n",
       " 142,\n",
       " 121,\n",
       " 136,\n",
       " 142,\n",
       " 136,\n",
       " 145,\n",
       " 127,\n",
       " 151,\n",
       " 136,\n",
       " 145,\n",
       " 166,\n",
       " 139,\n",
       " 151,\n",
       " 127,\n",
       " 136,\n",
       " 142,\n",
       " 118,\n",
       " 145,\n",
       " 127,\n",
       " 127,\n",
       " 118,\n",
       " 121,\n",
       " 121,\n",
       " 121,\n",
       " 106,\n",
       " 88,\n",
       " 136,\n",
       " 151,\n",
       " 133,\n",
       " 88,\n",
       " 142,\n",
       " 103,\n",
       " 124,\n",
       " 148,\n",
       " 151,\n",
       " 139,\n",
       " 157,\n",
       " 148,\n",
       " 154,\n",
       " 115,\n",
       " 115,\n",
       " 136,\n",
       " 133,\n",
       " 130,\n",
       " 136,\n",
       " 112,\n",
       " 115,\n",
       " 136,\n",
       " 109,\n",
       " 145,\n",
       " 133,\n",
       " 136,\n",
       " 112,\n",
       " 124,\n",
       " 115,\n",
       " 100,\n",
       " 118,\n",
       " 148,\n",
       " 118,\n",
       " 94,\n",
       " 145,\n",
       " 115,\n",
       " 136,\n",
       " 103,\n",
       " 172,\n",
       " 154,\n",
       " 151,\n",
       " 172,\n",
       " 124,\n",
       " 133,\n",
       " 181,\n",
       " 142,\n",
       " 136,\n",
       " 148,\n",
       " 136,\n",
       " 115,\n",
       " 103,\n",
       " 118,\n",
       " 121,\n",
       " 136,\n",
       " 130,\n",
       " 154,\n",
       " 115,\n",
       " 127,\n",
       " 130,\n",
       " 133,\n",
       " 130,\n",
       " 133,\n",
       " 136,\n",
       " 172,\n",
       " 157,\n",
       " 145,\n",
       " 133,\n",
       " 109,\n",
       " 154,\n",
       " 130,\n",
       " 142,\n",
       " 121,\n",
       " 118,\n",
       " 130,\n",
       " 127,\n",
       " 106,\n",
       " 133,\n",
       " 112,\n",
       " 118,\n",
       " 76,\n",
       " 118,\n",
       " 115,\n",
       " 163,\n",
       " 145,\n",
       " 154,\n",
       " 121,\n",
       " 127,\n",
       " 130,\n",
       " 148,\n",
       " 118,\n",
       " 115,\n",
       " 127,\n",
       " 121,\n",
       " 109,\n",
       " 118,\n",
       " 124,\n",
       " 145,\n",
       " 166,\n",
       " 142,\n",
       " 136,\n",
       " 130,\n",
       " 145,\n",
       " 142,\n",
       " 148,\n",
       " 106,\n",
       " 124,\n",
       " 106,\n",
       " 115,\n",
       " 115,\n",
       " 127,\n",
       " 124,\n",
       " 163,\n",
       " 139,\n",
       " 124,\n",
       " 106,\n",
       " 130,\n",
       " 106,\n",
       " 103,\n",
       " 121,\n",
       " 130,\n",
       " 127,\n",
       " 127,\n",
       " 121,\n",
       " 121,\n",
       " 127,\n",
       " 124,\n",
       " 136,\n",
       " 118,\n",
       " 127,\n",
       " 115,\n",
       " 124,\n",
       " 112,\n",
       " 115,\n",
       " 124,\n",
       " 121,\n",
       " 121,\n",
       " 133,\n",
       " 133,\n",
       " 103,\n",
       " 118,\n",
       " 124,\n",
       " 154,\n",
       " 109,\n",
       " 133,\n",
       " 145,\n",
       " 124,\n",
       " 115,\n",
       " 127,\n",
       " 115,\n",
       " 115,\n",
       " 103,\n",
       " 136,\n",
       " 118,\n",
       " 109,\n",
       " 124,\n",
       " 118,\n",
       " 127,\n",
       " 124,\n",
       " 124,\n",
       " 112,\n",
       " 115,\n",
       " 124,\n",
       " 109,\n",
       " 130,\n",
       " 112,\n",
       " 127,\n",
       " 121,\n",
       " 112,\n",
       " 142,\n",
       " 130,\n",
       " 127,\n",
       " 127,\n",
       " 130,\n",
       " 148,\n",
       " 100,\n",
       " 127,\n",
       " 118,\n",
       " 112,\n",
       " 112,\n",
       " 121,\n",
       " 118,\n",
       " 121,\n",
       " 121,\n",
       " 112,\n",
       " 121,\n",
       " 121,\n",
       " 100,\n",
       " 130,\n",
       " 127,\n",
       " 130,\n",
       " 127,\n",
       " 124,\n",
       " 127,\n",
       " 118,\n",
       " 106,\n",
       " 127,\n",
       " 103,\n",
       " 106,\n",
       " 109,\n",
       " 118,\n",
       " 151,\n",
       " 121,\n",
       " 100,\n",
       " 139,\n",
       " 118,\n",
       " 133,\n",
       " 127,\n",
       " 145,\n",
       " 127,\n",
       " 127,\n",
       " 106,\n",
       " 109,\n",
       " 97,\n",
       " 175,\n",
       " 118,\n",
       " 127,\n",
       " 145,\n",
       " 121,\n",
       " 136,\n",
       " 124,\n",
       " 175,\n",
       " 172,\n",
       " 172,\n",
       " 130,\n",
       " 130,\n",
       " 130,\n",
       " 127,\n",
       " 142,\n",
       " 121,\n",
       " 112,\n",
       " 145,\n",
       " 115,\n",
       " 133,\n",
       " 121,\n",
       " 109,\n",
       " 106,\n",
       " 136,\n",
       " 103,\n",
       " 130,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 130,\n",
       " 139,\n",
       " 145,\n",
       " 97,\n",
       " 133,\n",
       " 103,\n",
       " 115,\n",
       " 130,\n",
       " 106,\n",
       " 133,\n",
       " 124,\n",
       " 118,\n",
       " 118,\n",
       " 109,\n",
       " 124,\n",
       " 118,\n",
       " 160,\n",
       " 91,\n",
       " 118,\n",
       " 130,\n",
       " 112,\n",
       " 124,\n",
       " 190,\n",
       " 121,\n",
       " 115,\n",
       " 136,\n",
       " 175,\n",
       " 127,\n",
       " 130,\n",
       " 118,\n",
       " 109,\n",
       " 94,\n",
       " 157,\n",
       " 100,\n",
       " 106,\n",
       " 148,\n",
       " 130,\n",
       " 133,\n",
       " 109,\n",
       " 91,\n",
       " 97,\n",
       " 85,\n",
       " 97,\n",
       " 97,\n",
       " 118,\n",
       " 115,\n",
       " 130,\n",
       " 124,\n",
       " 109,\n",
       " 97,\n",
       " 109,\n",
       " 136,\n",
       " 166,\n",
       " 139,\n",
       " 130,\n",
       " 163,\n",
       " 136,\n",
       " 136,\n",
       " 94,\n",
       " 160,\n",
       " 118,\n",
       " 112,\n",
       " 124,\n",
       " 94,\n",
       " 100,\n",
       " 91,\n",
       " 88,\n",
       " 112,\n",
       " 130,\n",
       " 133,\n",
       " 112,\n",
       " 109,\n",
       " 112,\n",
       " 118,\n",
       " 154,\n",
       " 136,\n",
       " 142,\n",
       " 130,\n",
       " 148,\n",
       " 166,\n",
       " 121,\n",
       " 115,\n",
       " 115,\n",
       " 136,\n",
       " 130,\n",
       " 127,\n",
       " 124,\n",
       " 145,\n",
       " 139,\n",
       " 136,\n",
       " 136,\n",
       " 112,\n",
       " 142,\n",
       " 130,\n",
       " 112,\n",
       " 118,\n",
       " 133,\n",
       " 121,\n",
       " 118,\n",
       " 154,\n",
       " 133,\n",
       " 124,\n",
       " 124,\n",
       " 148,\n",
       " 97,\n",
       " 109,\n",
       " 106,\n",
       " 112,\n",
       " 127,\n",
       " 88,\n",
       " 103,\n",
       " 94,\n",
       " 124,\n",
       " 85,\n",
       " 142,\n",
       " 136,\n",
       " 142,\n",
       " 127,\n",
       " 115,\n",
       " 130,\n",
       " 124,\n",
       " 142,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 148,\n",
       " 136,\n",
       " 130,\n",
       " 148,\n",
       " 115,\n",
       " 124,\n",
       " 136,\n",
       " 118,\n",
       " 142,\n",
       " 127,\n",
       " 154,\n",
       " 121,\n",
       " 178,\n",
       " 127,\n",
       " 130,\n",
       " 136,\n",
       " 145,\n",
       " 133,\n",
       " 106,\n",
       " 169,\n",
       " 133,\n",
       " 154,\n",
       " 118,\n",
       " 106,\n",
       " 133,\n",
       " 130,\n",
       " 142,\n",
       " 130,\n",
       " 148,\n",
       " 133,\n",
       " 145,\n",
       " 121,\n",
       " 121,\n",
       " 133,\n",
       " 154,\n",
       " 163,\n",
       " 130,\n",
       " 151,\n",
       " 139,\n",
       " 124,\n",
       " 118,\n",
       " 136,\n",
       " 115,\n",
       " 145,\n",
       " 127,\n",
       " 124,\n",
       " 139,\n",
       " 160,\n",
       " 148,\n",
       " 121,\n",
       " 154,\n",
       " 127,\n",
       " 124,\n",
       " 145,\n",
       " 124,\n",
       " 127,\n",
       " 136,\n",
       " 124,\n",
       " 115,\n",
       " 118,\n",
       " 133,\n",
       " 118,\n",
       " 106,\n",
       " 127,\n",
       " 121,\n",
       " 130,\n",
       " 103,\n",
       " 130,\n",
       " 172,\n",
       " 136,\n",
       " 139,\n",
       " 130,\n",
       " 148,\n",
       " 145,\n",
       " 115,\n",
       " 130,\n",
       " 118,\n",
       " 148,\n",
       " 127,\n",
       " 142,\n",
       " 121,\n",
       " 136,\n",
       " 130,\n",
       " 118,\n",
       " 133,\n",
       " 127,\n",
       " 136,\n",
       " 145,\n",
       " 136,\n",
       " 136,\n",
       " 118,\n",
       " 136,\n",
       " 124,\n",
       " 127,\n",
       " 121,\n",
       " 151,\n",
       " 142,\n",
       " 133,\n",
       " 133,\n",
       " 139,\n",
       " 136,\n",
       " 148,\n",
       " 142,\n",
       " 142,\n",
       " 109,\n",
       " 124,\n",
       " 121,\n",
       " 121,\n",
       " 130,\n",
       " 142,\n",
       " 139,\n",
       " 148,\n",
       " 136,\n",
       " 139,\n",
       " 118,\n",
       " 145,\n",
       " 121,\n",
       " 130,\n",
       " 124,\n",
       " 121,\n",
       " 109,\n",
       " 130,\n",
       " 136,\n",
       " 148,\n",
       " 127,\n",
       " 130,\n",
       " 142,\n",
       " 178,\n",
       " 121,\n",
       " 127,\n",
       " 136,\n",
       " 151,\n",
       " 121,\n",
       " 115,\n",
       " 115,\n",
       " 148,\n",
       " 124,\n",
       " 112,\n",
       " 118,\n",
       " 130,\n",
       " 124,\n",
       " 136,\n",
       " 160,\n",
       " 133,\n",
       " 145,\n",
       " 133,\n",
       " 133,\n",
       " 133,\n",
       " 136,\n",
       " 136,\n",
       " 139,\n",
       " 136,\n",
       " 160,\n",
       " 124,\n",
       " 103,\n",
       " 124,\n",
       " 130,\n",
       " 112,\n",
       " 154,\n",
       " 139,\n",
       " 139,\n",
       " 124,\n",
       " 142,\n",
       " 121,\n",
       " 145,\n",
       " 124,\n",
       " 145,\n",
       " 142,\n",
       " 133,\n",
       " 118,\n",
       " 151,\n",
       " 142,\n",
       " 133,\n",
       " 154,\n",
       " 124,\n",
       " 163,\n",
       " 139,\n",
       " 178,\n",
       " 115,\n",
       " 136,\n",
       " 142,\n",
       " 142,\n",
       " 145,\n",
       " 154,\n",
       " 115,\n",
       " 118,\n",
       " 124,\n",
       " 121,\n",
       " 136,\n",
       " 127,\n",
       " 124,\n",
       " 127,\n",
       " 106,\n",
       " 121,\n",
       " 127,\n",
       " 136,\n",
       " 109,\n",
       " 112,\n",
       " 124,\n",
       " 103,\n",
       " 139,\n",
       " 115,\n",
       " 136,\n",
       " 106,\n",
       " 127,\n",
       " 121,\n",
       " 130,\n",
       " 136,\n",
       " 136,\n",
       " 124,\n",
       " 115,\n",
       " 124,\n",
       " 121,\n",
       " 136,\n",
       " 121,\n",
       " 142,\n",
       " 124,\n",
       " 133,\n",
       " 124,\n",
       " 130,\n",
       " 127,\n",
       " 109,\n",
       " 157,\n",
       " 133,\n",
       " 124,\n",
       " 130,\n",
       " 145,\n",
       " 127,\n",
       " 118,\n",
       " 130,\n",
       " 157,\n",
       " 142,\n",
       " 139,\n",
       " 124,\n",
       " 139,\n",
       " 139,\n",
       " 136,\n",
       " 160,\n",
       " 97,\n",
       " 139,\n",
       " 133,\n",
       " 136,\n",
       " 136,\n",
       " 139,\n",
       " 142,\n",
       " 127,\n",
       " 112,\n",
       " 118,\n",
       " 118,\n",
       " 115,\n",
       " 106,\n",
       " 97,\n",
       " 130,\n",
       " 115,\n",
       " 136,\n",
       " 127,\n",
       " 103,\n",
       " 106,\n",
       " 118,\n",
       " 136,\n",
       " 133,\n",
       " 115,\n",
       " 133,\n",
       " 124,\n",
       " 133,\n",
       " 133,\n",
       " 118,\n",
       " 157,\n",
       " 112,\n",
       " 142,\n",
       " 151,\n",
       " 154,\n",
       " 169,\n",
       " 136,\n",
       " 130,\n",
       " 142,\n",
       " 121,\n",
       " 121,\n",
       " 148,\n",
       " 136,\n",
       " 142,\n",
       " 127,\n",
       " 142,\n",
       " 127,\n",
       " 118,\n",
       " 148,\n",
       " 154,\n",
       " 154,\n",
       " 166,\n",
       " 118,\n",
       " 142,\n",
       " 154,\n",
       " 139,\n",
       " 124,\n",
       " 127,\n",
       " 145,\n",
       " 115,\n",
       " 109,\n",
       " 121,\n",
       " 106,\n",
       " 130,\n",
       " 106,\n",
       " 148,\n",
       " 139,\n",
       " 127,\n",
       " 112,\n",
       " 136,\n",
       " 142,\n",
       " 112,\n",
       " 133,\n",
       " 130,\n",
       " 112,\n",
       " 118,\n",
       " 121,\n",
       " 112,\n",
       " 136,\n",
       " 121,\n",
       " 127,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 124,\n",
       " 118,\n",
       " 130,\n",
       " 112,\n",
       " 115,\n",
       " 118,\n",
       " 154,\n",
       " 109,\n",
       " 112,\n",
       " 136,\n",
       " 121,\n",
       " 121,\n",
       " 139,\n",
       " 115,\n",
       " 133,\n",
       " 160,\n",
       " 127,\n",
       " 133,\n",
       " 142,\n",
       " 109,\n",
       " 139,\n",
       " 133,\n",
       " 121,\n",
       " 100,\n",
       " 130,\n",
       " 145,\n",
       " 130,\n",
       " 103,\n",
       " 118,\n",
       " 115,\n",
       " 94,\n",
       " 124,\n",
       " 133,\n",
       " 136,\n",
       " 121,\n",
       " 85,\n",
       " 121,\n",
       " 103,\n",
       " 139,\n",
       " 124,\n",
       " 61,\n",
       " 106,\n",
       " 121,\n",
       " 124,\n",
       " 127,\n",
       " 118,\n",
       " 130,\n",
       " 154,\n",
       " 142,\n",
       " 130,\n",
       " 124,\n",
       " 157,\n",
       " 124,\n",
       " 127,\n",
       " 133,\n",
       " 133,\n",
       " 130,\n",
       " 199,\n",
       " 127,\n",
       " 121,\n",
       " 127,\n",
       " 139,\n",
       " 121,\n",
       " 118,\n",
       " 124,\n",
       " 112,\n",
       " 160,\n",
       " 127,\n",
       " 139,\n",
       " 124,\n",
       " 118,\n",
       " 115,\n",
       " 127,\n",
       " 118,\n",
       " 115,\n",
       " 130,\n",
       " 160,\n",
       " 103,\n",
       " 151,\n",
       " 118,\n",
       " 109,\n",
       " 118,\n",
       " 103,\n",
       " 121,\n",
       " 130,\n",
       " 127,\n",
       " 121,\n",
       " 103,\n",
       " 130,\n",
       " ...]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dim1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='visualizediemnsion'> Visualize the image dimesions distribution</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=dim1, y=dim2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the smallest image is 60x50 and the largest one is by 220x221(around).\n",
    "\n",
    "Most of the images are between 100x100 and 180x175."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='important'> So why it is important to check the dimensions of the images?</a>\n",
    "\n",
    "+ Convolutional Neural Network isn't going to be able train on images of various sizes. So we need to make sure to resize all the images to be the same size.\n",
    "+ So we have to choose what actual dimensions should I resize everything to.\n",
    "+ What we should choose is the `average size` of dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.92538461538462"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dim1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.75"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(dim2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we dediced to use mean dimension size to resize all the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_shape = (130, 130, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name='preparingdata'> Preparing the Data for the model</a>\n",
    "\n",
    "There is too much data for us to read all at once in memory. We can use some built in functions in Keras to automatically process the data, generate a flow of batches from a directory, and also manipulate the images.\n",
    "\n",
    "## <a name='imagemanipulation'> Image Manipulation</a>\n",
    "\n",
    "Its usually a good idea to manipulate the images with rotation, resizing, and scaling so the model becomes more robust to different images that our data set doesn't have. We can use the **ImageDataGenerator** to do this automatically for us. Check out the documentation for a full list of all the parameters you can use here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50700"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "130*130*3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will be dealing with 50,700 data points for every image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='whymanipulate'>Why we want to manipulate the image data?</a>\n",
    "\n",
    "+ Currently we have total training images of 24960 and we want to expand our training data.\n",
    "+ One way we can do is rotation, resizing, and scaling so the model becomes more robust to different images that our data set doesn't have.\n",
    "\n",
    "### <a name='howtochoose'>So what is the good value for those random transformation parameters? How much should I rotate, stretch or sequeeze the images?</a>\n",
    "\n",
    "+ It all depends on the images that we are dealing with\n",
    "+ In our cell image, lucky that cells are kinda round in nature and we expect that future image will follow the same shape. However we can expect the images can be in stretched or sequeezed , based on the data source.\n",
    "+ for other use case like we are trying to detect the person image, we normally don't want to rotate the image 180 degree. Because it doesn't helpful for camera to detect upside down images unless someone is walking to that camera upside down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(train_path+'/parasitized/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12480"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(train_path+'/uninfected'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24960"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12480 *2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='deciderescale'>Deciding whether we need to rescale the image?</a>\n",
    "+ It seems like our image are already in scale (between 0 and 1). So we don't need to use `rescale` parameter.\n",
    "+ In the case of we need to rescale the image, we need to use `rescale=1/255`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.88235295)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unifected_cell.min(), unifected_cell.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 0.9098039)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parasitized_cell.min(), parasitized_cell.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='imagedatagenerator'>Image Data Generator</a>\n",
    "+ `rotation_range`\n",
    "+ `width_shift_range`\n",
    "+ `height_shift_range`\n",
    "+ `rescale`\n",
    "+ `shear_range` : how much we cut off the image\n",
    "+ `zoom_range`\n",
    "+ `horizontal_flip`\n",
    "+ `fill_mode`: `nearest` => tell where we want to get the image data to fill in the missing parts, after we cut off the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class ImageDataGenerator in module tensorflow.python.keras.preprocessing.image:\n",
      "\n",
      "class ImageDataGenerator(keras_preprocessing.image.image_data_generator.ImageDataGenerator)\n",
      " |  ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)\n",
      " |  \n",
      " |  Generate batches of tensor image data with real-time data augmentation.\n",
      " |  \n",
      " |   The data will be looped over (in batches).\n",
      " |  \n",
      " |  Arguments:\n",
      " |      featurewise_center: Boolean.\n",
      " |          Set input mean to 0 over the dataset, feature-wise.\n",
      " |      samplewise_center: Boolean. Set each sample mean to 0.\n",
      " |      featurewise_std_normalization: Boolean.\n",
      " |          Divide inputs by std of the dataset, feature-wise.\n",
      " |      samplewise_std_normalization: Boolean. Divide each input by its std.\n",
      " |      zca_epsilon: epsilon for ZCA whitening. Default is 1e-6.\n",
      " |      zca_whitening: Boolean. Apply ZCA whitening.\n",
      " |      rotation_range: Int. Degree range for random rotations.\n",
      " |      width_shift_range: Float, 1-D array-like or int\n",
      " |          - float: fraction of total width, if < 1, or pixels if >= 1.\n",
      " |          - 1-D array-like: random elements from the array.\n",
      " |          - int: integer number of pixels from interval\n",
      " |              `(-width_shift_range, +width_shift_range)`\n",
      " |          - With `width_shift_range=2` possible values\n",
      " |              are integers `[-1, 0, +1]`,\n",
      " |              same as with `width_shift_range=[-1, 0, +1]`,\n",
      " |              while with `width_shift_range=1.0` possible values are floats\n",
      " |              in the interval [-1.0, +1.0).\n",
      " |      height_shift_range: Float, 1-D array-like or int\n",
      " |          - float: fraction of total height, if < 1, or pixels if >= 1.\n",
      " |          - 1-D array-like: random elements from the array.\n",
      " |          - int: integer number of pixels from interval\n",
      " |              `(-height_shift_range, +height_shift_range)`\n",
      " |          - With `height_shift_range=2` possible values\n",
      " |              are integers `[-1, 0, +1]`,\n",
      " |              same as with `height_shift_range=[-1, 0, +1]`,\n",
      " |              while with `height_shift_range=1.0` possible values are floats\n",
      " |              in the interval [-1.0, +1.0).\n",
      " |      brightness_range: Tuple or list of two floats. Range for picking\n",
      " |          a brightness shift value from.\n",
      " |      shear_range: Float. Shear Intensity\n",
      " |          (Shear angle in counter-clockwise direction in degrees)\n",
      " |      zoom_range: Float or [lower, upper]. Range for random zoom.\n",
      " |          If a float, `[lower, upper] = [1-zoom_range, 1+zoom_range]`.\n",
      " |      channel_shift_range: Float. Range for random channel shifts.\n",
      " |      fill_mode: One of {\"constant\", \"nearest\", \"reflect\" or \"wrap\"}.\n",
      " |          Default is 'nearest'.\n",
      " |          Points outside the boundaries of the input are filled\n",
      " |          according to the given mode:\n",
      " |          - 'constant': kkkkkkkk|abcd|kkkkkkkk (cval=k)\n",
      " |          - 'nearest':  aaaaaaaa|abcd|dddddddd\n",
      " |          - 'reflect':  abcddcba|abcd|dcbaabcd\n",
      " |          - 'wrap':  abcdabcd|abcd|abcdabcd\n",
      " |      cval: Float or Int.\n",
      " |          Value used for points outside the boundaries\n",
      " |          when `fill_mode = \"constant\"`.\n",
      " |      horizontal_flip: Boolean. Randomly flip inputs horizontally.\n",
      " |      vertical_flip: Boolean. Randomly flip inputs vertically.\n",
      " |      rescale: rescaling factor. Defaults to None.\n",
      " |          If None or 0, no rescaling is applied,\n",
      " |          otherwise we multiply the data by the value provided\n",
      " |          (after applying all other transformations).\n",
      " |      preprocessing_function: function that will be applied on each input.\n",
      " |          The function will run after the image is resized and augmented.\n",
      " |          The function should take one argument:\n",
      " |          one image (Numpy tensor with rank 3),\n",
      " |          and should output a Numpy tensor with the same shape.\n",
      " |      data_format: Image data format,\n",
      " |          either \"channels_first\" or \"channels_last\".\n",
      " |          \"channels_last\" mode means that the images should have shape\n",
      " |          `(samples, height, width, channels)`,\n",
      " |          \"channels_first\" mode means that the images should have shape\n",
      " |          `(samples, channels, height, width)`.\n",
      " |          It defaults to the `image_data_format` value found in your\n",
      " |          Keras config file at `~/.keras/keras.json`.\n",
      " |          If you never set it, then it will be \"channels_last\".\n",
      " |      validation_split: Float. Fraction of images reserved for validation\n",
      " |          (strictly between 0 and 1).\n",
      " |      dtype: Dtype to use for the generated arrays.\n",
      " |  \n",
      " |  Examples:\n",
      " |  \n",
      " |  Example of using `.flow(x, y)`:\n",
      " |  \n",
      " |  ```python\n",
      " |  (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
      " |  y_train = np_utils.to_categorical(y_train, num_classes)\n",
      " |  y_test = np_utils.to_categorical(y_test, num_classes)\n",
      " |  datagen = ImageDataGenerator(\n",
      " |      featurewise_center=True,\n",
      " |      featurewise_std_normalization=True,\n",
      " |      rotation_range=20,\n",
      " |      width_shift_range=0.2,\n",
      " |      height_shift_range=0.2,\n",
      " |      horizontal_flip=True)\n",
      " |  # compute quantities required for featurewise normalization\n",
      " |  # (std, mean, and principal components if ZCA whitening is applied)\n",
      " |  datagen.fit(x_train)\n",
      " |  # fits the model on batches with real-time data augmentation:\n",
      " |  model.fit(datagen.flow(x_train, y_train, batch_size=32),\n",
      " |            steps_per_epoch=len(x_train) / 32, epochs=epochs)\n",
      " |  # here's a more \"manual\" example\n",
      " |  for e in range(epochs):\n",
      " |      print('Epoch', e)\n",
      " |      batches = 0\n",
      " |      for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):\n",
      " |          model.fit(x_batch, y_batch)\n",
      " |          batches += 1\n",
      " |          if batches >= len(x_train) / 32:\n",
      " |              # we need to break the loop by hand because\n",
      " |              # the generator loops indefinitely\n",
      " |              break\n",
      " |  ```\n",
      " |  \n",
      " |  Example of using `.flow_from_directory(directory)`:\n",
      " |  \n",
      " |  ```python\n",
      " |  train_datagen = ImageDataGenerator(\n",
      " |          rescale=1./255,\n",
      " |          shear_range=0.2,\n",
      " |          zoom_range=0.2,\n",
      " |          horizontal_flip=True)\n",
      " |  test_datagen = ImageDataGenerator(rescale=1./255)\n",
      " |  train_generator = train_datagen.flow_from_directory(\n",
      " |          'data/train',\n",
      " |          target_size=(150, 150),\n",
      " |          batch_size=32,\n",
      " |          class_mode='binary')\n",
      " |  validation_generator = test_datagen.flow_from_directory(\n",
      " |          'data/validation',\n",
      " |          target_size=(150, 150),\n",
      " |          batch_size=32,\n",
      " |          class_mode='binary')\n",
      " |  model.fit(\n",
      " |          train_generator,\n",
      " |          steps_per_epoch=2000,\n",
      " |          epochs=50,\n",
      " |          validation_data=validation_generator,\n",
      " |          validation_steps=800)\n",
      " |  ```\n",
      " |  \n",
      " |  Example of transforming images and masks together.\n",
      " |  \n",
      " |  ```python\n",
      " |  # we create two instances with the same arguments\n",
      " |  data_gen_args = dict(featurewise_center=True,\n",
      " |                       featurewise_std_normalization=True,\n",
      " |                       rotation_range=90,\n",
      " |                       width_shift_range=0.1,\n",
      " |                       height_shift_range=0.1,\n",
      " |                       zoom_range=0.2)\n",
      " |  image_datagen = ImageDataGenerator(**data_gen_args)\n",
      " |  mask_datagen = ImageDataGenerator(**data_gen_args)\n",
      " |  # Provide the same seed and keyword arguments to the fit and flow methods\n",
      " |  seed = 1\n",
      " |  image_datagen.fit(images, augment=True, seed=seed)\n",
      " |  mask_datagen.fit(masks, augment=True, seed=seed)\n",
      " |  image_generator = image_datagen.flow_from_directory(\n",
      " |      'data/images',\n",
      " |      class_mode=None,\n",
      " |      seed=seed)\n",
      " |  mask_generator = mask_datagen.flow_from_directory(\n",
      " |      'data/masks',\n",
      " |      class_mode=None,\n",
      " |      seed=seed)\n",
      " |  # combine generators into one which yields image and masks\n",
      " |  train_generator = zip(image_generator, mask_generator)\n",
      " |  model.fit_generator(\n",
      " |      train_generator,\n",
      " |      steps_per_epoch=2000,\n",
      " |      epochs=50)\n",
      " |  ```\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      ImageDataGenerator\n",
      " |      keras_preprocessing.image.image_data_generator.ImageDataGenerator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-06, rotation_range=0, width_shift_range=0.0, height_shift_range=0.0, brightness_range=None, shear_range=0.0, zoom_range=0.0, channel_shift_range=0.0, fill_mode='nearest', cval=0.0, horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=None, validation_split=0.0, dtype=None)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  flow(self, x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None)\n",
      " |      Takes data & label arrays, generates batches of augmented data.\n",
      " |      \n",
      " |      Arguments:\n",
      " |          x: Input data. Numpy array of rank 4 or a tuple. If tuple, the first\n",
      " |            element should contain the images and the second element another numpy\n",
      " |            array or a list of numpy arrays that gets passed to the output without\n",
      " |            any modifications. Can be used to feed the model miscellaneous data\n",
      " |            along with the images. In case of grayscale data, the channels axis of\n",
      " |            the image array should have value 1, in case of RGB data, it should\n",
      " |            have value 3, and in case of RGBA data, it should have value 4.\n",
      " |          y: Labels.\n",
      " |          batch_size: Int (default: 32).\n",
      " |          shuffle: Boolean (default: True).\n",
      " |          sample_weight: Sample weights.\n",
      " |          seed: Int (default: None).\n",
      " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
      " |            specify a directory to which to save the augmented pictures being\n",
      " |            generated (useful for visualizing what you are doing).\n",
      " |          save_prefix: Str (default: `''`). Prefix to use for filenames of saved\n",
      " |            pictures (only relevant if `save_to_dir` is set).\n",
      " |          save_format: one of \"png\", \"jpeg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |            `validation_split` is set in `ImageDataGenerator`.\n",
      " |      \n",
      " |      Returns:\n",
      " |          An `Iterator` yielding tuples of `(x, y)`\n",
      " |              where `x` is a numpy array of image data\n",
      " |              (in the case of a single image input) or a list\n",
      " |              of numpy arrays (in the case with\n",
      " |              additional inputs) and `y` is a numpy array\n",
      " |              of corresponding labels. If 'sample_weight' is not None,\n",
      " |              the yielded tuples are of the form `(x, y, sample_weight)`.\n",
      " |              If `y` is None, only the numpy array `x` is returned.\n",
      " |  \n",
      " |  flow_from_dataframe(self, dataframe, directory=None, x_col='filename', y_col='class', weight_col=None, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', subset=None, interpolation='nearest', validate_filenames=True, **kwargs)\n",
      " |      Takes the dataframe and the path to a directory + generates batches.\n",
      " |      \n",
      " |       The generated batches contain augmented/normalized data.\n",
      " |      \n",
      " |      **A simple tutorial can be found **[here](\n",
      " |                                  http://bit.ly/keras_flow_from_dataframe).\n",
      " |      \n",
      " |      Arguments:\n",
      " |          dataframe: Pandas dataframe containing the filepaths relative to\n",
      " |            `directory` (or absolute paths if `directory` is None) of the images\n",
      " |            in a string column. It should include other column/s\n",
      " |              depending on the `class_mode`: - if `class_mode` is `\"categorical\"`\n",
      " |                (default value) it must include the `y_col` column with the\n",
      " |                class/es of each image. Values in column can be string/list/tuple\n",
      " |                if a single class or list/tuple if multiple classes. - if\n",
      " |                `class_mode` is `\"binary\"` or `\"sparse\"` it must include the given\n",
      " |                `y_col` column with class values as strings. - if `class_mode` is\n",
      " |                `\"raw\"` or `\"multi_output\"` it should contain the columns\n",
      " |                specified in `y_col`. - if `class_mode` is `\"input\"` or `None` no\n",
      " |                extra column is needed.\n",
      " |          directory: string, path to the directory to read images from. If `None`,\n",
      " |            data in `x_col` column should be absolute paths.\n",
      " |          x_col: string, column in `dataframe` that contains the filenames (or\n",
      " |            absolute paths if `directory` is `None`).\n",
      " |          y_col: string or list, column/s in `dataframe` that has the target data.\n",
      " |          weight_col: string, column in `dataframe` that contains the sample\n",
      " |              weights. Default: `None`.\n",
      " |          target_size: tuple of integers `(height, width)`, default: `(256, 256)`.\n",
      " |            The dimensions to which all images found will be resized.\n",
      " |          color_mode: one of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". Whether\n",
      " |            the images will be converted to have 1 or 3 color channels.\n",
      " |          classes: optional list of classes (e.g. `['dogs', 'cats']`). Default is\n",
      " |            None. If not provided, the list of classes will be automatically\n",
      " |            inferred from the `y_col`, which will map to the label indices, will\n",
      " |            be alphanumeric). The dictionary containing the mapping from class\n",
      " |            names to class indices can be obtained via the attribute\n",
      " |            `class_indices`.\n",
      " |          class_mode: one of \"binary\", \"categorical\", \"input\", \"multi_output\",\n",
      " |              \"raw\", sparse\" or None. Default: \"categorical\".\n",
      " |              Mode for yielding the targets:\n",
      " |              - `\"binary\"`: 1D numpy array of binary labels,\n",
      " |              - `\"categorical\"`: 2D numpy array of one-hot encoded labels.\n",
      " |                Supports multi-label output.\n",
      " |              - `\"input\"`: images identical to input images (mainly used to work\n",
      " |                with autoencoders),\n",
      " |              - `\"multi_output\"`: list with the values of the different columns,\n",
      " |              - `\"raw\"`: numpy array of values in `y_col` column(s),\n",
      " |              - `\"sparse\"`: 1D numpy array of integer labels, - `None`, no targets\n",
      " |                are returned (the generator will only yield batches of image data,\n",
      " |                which is useful to use in `model.predict_generator()`).\n",
      " |          batch_size: size of the batches of data (default: 32).\n",
      " |          shuffle: whether to shuffle the data (default: True)\n",
      " |          seed: optional random seed for shuffling and transformations.\n",
      " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
      " |            specify a directory to which to save the augmented pictures being\n",
      " |            generated (useful for visualizing what you are doing).\n",
      " |          save_prefix: str. Prefix to use for filenames of saved pictures (only\n",
      " |            relevant if `save_to_dir` is set).\n",
      " |          save_format: one of \"png\", \"jpeg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |            `validation_split` is set in `ImageDataGenerator`.\n",
      " |          interpolation: Interpolation method used to resample the image if the\n",
      " |            target size is different from that of the loaded image. Supported\n",
      " |            methods are `\"nearest\"`, `\"bilinear\"`, and `\"bicubic\"`. If PIL version\n",
      " |            1.1.3 or newer is installed, `\"lanczos\"` is also supported. If PIL\n",
      " |            version 3.4.0 or newer is installed, `\"box\"` and `\"hamming\"` are also\n",
      " |            supported. By default, `\"nearest\"` is used.\n",
      " |          validate_filenames: Boolean, whether to validate image filenames in\n",
      " |            `x_col`. If `True`, invalid images will be ignored. Disabling this\n",
      " |            option can lead to speed-up in the execution of this function.\n",
      " |            Defaults to `True`.\n",
      " |          **kwargs: legacy arguments for raising deprecation warnings.\n",
      " |      \n",
      " |      Returns:\n",
      " |          A `DataFrameIterator` yielding tuples of `(x, y)`\n",
      " |          where `x` is a numpy array containing a batch\n",
      " |          of images with shape `(batch_size, *target_size, channels)`\n",
      " |          and `y` is a numpy array of corresponding labels.\n",
      " |  \n",
      " |  flow_from_directory(self, directory, target_size=(256, 256), color_mode='rgb', classes=None, class_mode='categorical', batch_size=32, shuffle=True, seed=None, save_to_dir=None, save_prefix='', save_format='png', follow_links=False, subset=None, interpolation='nearest')\n",
      " |      Takes the path to a directory & generates batches of augmented data.\n",
      " |      \n",
      " |      Arguments:\n",
      " |          directory: string, path to the target directory. It should contain one\n",
      " |            subdirectory per class. Any PNG, JPG, BMP, PPM or TIF images inside\n",
      " |            each of the subdirectories directory tree will be included in the\n",
      " |            generator. See [this script](\n",
      " |              https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d)\n",
      " |                for more details.\n",
      " |          target_size: Tuple of integers `(height, width)`, defaults to `(256,\n",
      " |            256)`. The dimensions to which all images found will be resized.\n",
      " |          color_mode: One of \"grayscale\", \"rgb\", \"rgba\". Default: \"rgb\". Whether\n",
      " |            the images will be converted to have 1, 3, or 4 channels.\n",
      " |          classes: Optional list of class subdirectories\n",
      " |              (e.g. `['dogs', 'cats']`). Default: None. If not provided, the list\n",
      " |                of classes will be automatically inferred from the subdirectory\n",
      " |                names/structure under `directory`, where each subdirectory will be\n",
      " |                treated as a different class (and the order of the classes, which\n",
      " |                will map to the label indices, will be alphanumeric). The\n",
      " |                dictionary containing the mapping from class names to class\n",
      " |                indices can be obtained via the attribute `class_indices`.\n",
      " |          class_mode: One of \"categorical\", \"binary\", \"sparse\",\n",
      " |              \"input\", or None. Default: \"categorical\".\n",
      " |              Determines the type of label arrays that are returned: -\n",
      " |                \"categorical\" will be 2D one-hot encoded labels, - \"binary\" will\n",
      " |                be 1D binary labels, \"sparse\" will be 1D integer labels, - \"input\"\n",
      " |                will be images identical to input images (mainly used to work with\n",
      " |                autoencoders). - If None, no labels are returned (the generator\n",
      " |                will only yield batches of image data, which is useful to use with\n",
      " |                `model.predict_generator()`). Please note that in case of\n",
      " |                class_mode None, the data still needs to reside in a subdirectory\n",
      " |                of `directory` for it to work correctly.\n",
      " |          batch_size: Size of the batches of data (default: 32).\n",
      " |          shuffle: Whether to shuffle the data (default: True) If set to False,\n",
      " |            sorts the data in alphanumeric order.\n",
      " |          seed: Optional random seed for shuffling and transformations.\n",
      " |          save_to_dir: None or str (default: None). This allows you to optionally\n",
      " |            specify a directory to which to save the augmented pictures being\n",
      " |            generated (useful for visualizing what you are doing).\n",
      " |          save_prefix: Str. Prefix to use for filenames of saved pictures (only\n",
      " |            relevant if `save_to_dir` is set).\n",
      " |          save_format: One of \"png\", \"jpeg\"\n",
      " |              (only relevant if `save_to_dir` is set). Default: \"png\".\n",
      " |          follow_links: Whether to follow symlinks inside\n",
      " |              class subdirectories (default: False).\n",
      " |          subset: Subset of data (`\"training\"` or `\"validation\"`) if\n",
      " |            `validation_split` is set in `ImageDataGenerator`.\n",
      " |          interpolation: Interpolation method used to resample the image if the\n",
      " |            target size is different from that of the loaded image. Supported\n",
      " |            methods are `\"nearest\"`, `\"bilinear\"`, and `\"bicubic\"`. If PIL version\n",
      " |            1.1.3 or newer is installed, `\"lanczos\"` is also supported. If PIL\n",
      " |            version 3.4.0 or newer is installed, `\"box\"` and `\"hamming\"` are also\n",
      " |            supported. By default, `\"nearest\"` is used.\n",
      " |      \n",
      " |      Returns:\n",
      " |          A `DirectoryIterator` yielding tuples of `(x, y)`\n",
      " |              where `x` is a numpy array containing a batch\n",
      " |              of images with shape `(batch_size, *target_size, channels)`\n",
      " |              and `y` is a numpy array of corresponding labels.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from keras_preprocessing.image.image_data_generator.ImageDataGenerator:\n",
      " |  \n",
      " |  apply_transform(self, x, transform_parameters)\n",
      " |      Applies a transformation to an image according to given parameters.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: 3D tensor, single image.\n",
      " |          transform_parameters: Dictionary with string - parameter pairs\n",
      " |              describing the transformation.\n",
      " |              Currently, the following parameters\n",
      " |              from the dictionary are used:\n",
      " |              - `'theta'`: Float. Rotation angle in degrees.\n",
      " |              - `'tx'`: Float. Shift in the x direction.\n",
      " |              - `'ty'`: Float. Shift in the y direction.\n",
      " |              - `'shear'`: Float. Shear angle in degrees.\n",
      " |              - `'zx'`: Float. Zoom in the x direction.\n",
      " |              - `'zy'`: Float. Zoom in the y direction.\n",
      " |              - `'flip_horizontal'`: Boolean. Horizontal flip.\n",
      " |              - `'flip_vertical'`: Boolean. Vertical flip.\n",
      " |              - `'channel_shift_intencity'`: Float. Channel shift intensity.\n",
      " |              - `'brightness'`: Float. Brightness shift intensity.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A transformed version of the input (same shape).\n",
      " |  \n",
      " |  fit(self, x, augment=False, rounds=1, seed=None)\n",
      " |      Fits the data generator to some sample data.\n",
      " |      \n",
      " |      This computes the internal data stats related to the\n",
      " |      data-dependent transformations, based on an array of sample data.\n",
      " |      \n",
      " |      Only required if `featurewise_center` or\n",
      " |      `featurewise_std_normalization` or `zca_whitening` are set to True.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: Sample data. Should have rank 4.\n",
      " |           In case of grayscale data,\n",
      " |           the channels axis should have value 1, in case\n",
      " |           of RGB data, it should have value 3, and in case\n",
      " |           of RGBA data, it should have value 4.\n",
      " |          augment: Boolean (default: False).\n",
      " |              Whether to fit on randomly augmented samples.\n",
      " |          rounds: Int (default: 1).\n",
      " |              If using data augmentation (`augment=True`),\n",
      " |              this is how many augmentation passes over the data to use.\n",
      " |          seed: Int (default: None). Random seed.\n",
      " |  \n",
      " |  get_random_transform(self, img_shape, seed=None)\n",
      " |      Generates random parameters for a transformation.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          seed: Random seed.\n",
      " |          img_shape: Tuple of integers.\n",
      " |              Shape of the image that is transformed.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A dictionary containing randomly chosen parameters describing the\n",
      " |          transformation.\n",
      " |  \n",
      " |  random_transform(self, x, seed=None)\n",
      " |      Applies a random transformation to an image.\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: 3D tensor, single image.\n",
      " |          seed: Random seed.\n",
      " |      \n",
      " |      # Returns\n",
      " |          A randomly transformed version of the input (same shape).\n",
      " |  \n",
      " |  standardize(self, x)\n",
      " |      Applies the normalization configuration in-place to a batch of inputs.\n",
      " |      \n",
      " |      `x` is changed in-place since the function is mainly used internally\n",
      " |      to standarize images and feed them to your network. If a copy of `x`\n",
      " |      would be created instead it would have a significant performance cost.\n",
      " |      If you want to apply this method without changing the input in-place\n",
      " |      you can call the method creating a copy before:\n",
      " |      \n",
      " |      standarize(np.copy(x))\n",
      " |      \n",
      " |      # Arguments\n",
      " |          x: Batch of inputs to be normalized.\n",
      " |      \n",
      " |      # Returns\n",
      " |          The inputs, normalized.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from keras_preprocessing.image.image_data_generator.ImageDataGenerator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ImageDataGenerator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_gen = ImageDataGenerator(rotation_range=20, # rotate the image 20 degrees\n",
    "                               width_shift_range=0.10, # Shift the pic width by a max of 5%\n",
    "                               height_shift_range=0.10, # Shift the pic height by a max of 5%\n",
    "                               rescale=1/255, # Rescale the image by normalzing it.\n",
    "                               shear_range=0.1, # Shear means cutting away part of the image (max 10%)\n",
    "                               zoom_range=0.1, # Zoom in by 10% max\n",
    "                               horizontal_flip=True, # Allo horizontal flipping\n",
    "                               fill_mode='nearest' # Fill in missing pixels with the nearest filled value\n",
    "                              )\n",
    "\n",
    "# We can play around those values to do random transformation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Before manipulation,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPkAAAD8CAYAAAC1ggIjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAB3EklEQVR4nO29ebht21UX+Btz7XPuva/Jewl5CWlJQxKTQgIxBjsUjITQBrXEWDb5lCqlCiWiNAlIwfcpgh2FzYdUhEgQlAhEyWepSUQQLCo0aQhNJD3khZf+JXl57957zl5z1B9jjjHHnGvOtdc+3T33vD3eO3fvvdbs5xxj/MaYHTEzdrSjHV1cCje6ADva0Y5Ol3ZMvqMdXXDaMfmOdnTBacfkO9rRBacdk+9oRxecdky+ox1dcDo1Jiei5xPRbxLRO4joJaeVz452tKN5otOYJyeiAcDbAHwBgLsB/BKAP8PMv3Hime1oRzuapdPS5M8B8A5mfhczHwD4UQAvOKW8drSjHc3Q6pTSfQyA97rfdwP4nF5gItotu3uQ0J233YbHPfKu/IAIAPDBj96LD3z03htUqgtBH2bmu1ovTovJqfGsYGQi+ssA/vIp5b+jc0p/9Nmfje9+8f8OgI3BQyD8k1e+Cv/wR37sxhbu5qbf6r04LSa/G8Dj3O/HAvgdH4CZXwbgZcBOkz+4iAGwSPzkD2JmfbKjU6DTssl/CcBTiOiJRLQP4IUAXn1Kee3oJiQP9ZgZu31Sp0enosmZeU1EfxXAawAMAF7OzL9+Gnnt6HzTX3nBl+Axj3g4AICI8KRHP9oMN1JOZ+Dzn/VMPOwht1u8T9x/P777X/8E1uN4xiW+eHQqU2hbF2IH1y8svervfhue+elPAgBQCCDKOty+k0B4T+//6L34I1/9N3H98PCsinqz0xuY+dmtF6dlk+9oR0LMxr8cI5hKnywRgchpdaW4k/snRTsm39HpEgPZ4Cb3neU3MZgATkyuyDLGnSvupGjH5A9CuuXyJTzuEeWUKpf/FC9azNaaI23Rpf09Y9xpnORwC5pPzmwIhKc+9tE4WK87uTOuXj/Aez/4oYUlefDSziZ/ENLnPvN344e+rdxOwDGmP9cVDESOyfvtGJUIRCRTM8qXXGpeDac/1P6mCS532bmx6HQ/QIRIMBQwrALAjF9669vwwv/z725R8wtNO5t8RyUJz7AwEQMEAlMAU7QwDEYACcMRZUZXRmWXFrKm5sTwlBMCg0Eh2Hz4JK1G+fz3wO6pfV+KJx7ctGPyBympdmZ2iyW4ZED97hnRa/UJsq9Q4QQlNtJtIckW45fz6u067ahNOyZ/EBIzY13YupmFYoxFOAuRGM8zOfMyXWrpxCiMrc/Bs/C9R1pGX9Yd9Wlnk58C3XbLFXzvN7wYly9dchh2Go4I+Dsv/2H8ytvfearl+foX/il81qc/OTm2GHfceiue/mmPT2UgEGQaSxzhWUtreKQwuvy01OQCzMkc5/q+rHCNBkKy64kII0dJAwBRgDaaREnCxckChfyffOAq3vpb7xWbv8wt/U/4qTe8Ed//H/7TsdrvJqGdTX6WtBoGPPsZT8Ntt9xSPPc2KyAD/47bbj318jz1cY/Bc57+NHGO+Rks++osaM+bavsmhs/ON5rGqxjdU8GAJiBKB58G5LxvpZmWpUeE22+5BZ/zjN8lz1oQnwjv+p3fmTx/sNGOyU+JmBns4KSHqfbeLRQ5k7LYctJSq/rPuTSWkDjhslMsgMuFLpXTjgsnWsmo3BAERBO13Syf2vvnAKjecNox+WkQAzwCMS27pjR+KU0DTRzFZ1Ce42bUcpIVNrshBBZHns2edfZAOUZV2L64LAs8AdlhuOPyHZOfAD3+kY/A73p82llLhFuvXMYQgmMC0oko0Sxu7P3epz8Vt16+1Ey33I4pT0otXH1RAVLMXwEPf8hDJlNWm6aw/Cq1HCQ7GNTplvk8IwK182fTd7Ey4J++a5EXOH52vrbLGYzH3nUXnvd7f08Vv0qb/Ee2570/5Rd+43/g3k/cN1uX80o7x9sJ0Iue/zx864v+rPwgAkIe4DrIQ+jt6nVqNtgws3npGCMQnbMLAYV+cgtY4hgnJoIEmT4jG8wzFLMgqIVDna5/58NvpaErM0JSZx9AFuEkAVos0mltfElxStSQhAMhmw7Brdtxefh0vvKb/zZe/2tvXVyXG0A7x9tpkg42Us9TTIPFqanI0aaMyoHvbFZdbabpJJvdD+ag3myUTMWRC+GQU5+WVTRt42UONInjP+vvvfboC7Z2GnNpGvJmRkRslskLopZQkt9ek6d3I0NX5fEFPKR8x+QnRDILpIwu35kIlPA5U0y2uHMfu6mrIi3m7KlOf94OLVereb2emL81bZd8fERlEZYwWnZibQ5bCJ7G903x+5TrybGcxsuowR71004OErKQOW1mmGCW9t0EdW4O2jH5EWg1DIVTZ6AAnVJWB5uMsWgqiCEjSBidLD6HBpcDeUsmJ+dVoZ2rCAoAoJ+OwSDeboZbwkptrVzD35427FEtCFSb9zTrZE28Um5A49xZp18QE4bcZLo/UMoEJOV6kfMGRFcCQkJgkcFDRlKrYcDeap5dDpubaW487WzyLYmI8Ipv/gY8/hGPAI8yPG69cgV33Hpbd65WvnDbo2xOnmJyeDmDJQjLzAL3O9B68+q1aT5Hgdt1Pv7Ph5lMeRU/qBBq9qyTt9rPWl5dOw+WbayUnhdlcf0RXQFCcI5GZ89/5OOfwMHhGhSyU1EiyPeD9Rp/8hu/HR+692ONljoT2tnkJ0UE4K477sCjPuVhaXRkjVCv89ZnbVIN5rRsI05Lu5YrzpLm2QCH5+bGmxAdLs2FzrMWLK9jFk7DThpohZkpS3MdvLaNy1lnKFyAIvi0/LlvP+WOhxSCxEiZ/HCNYYNQvFG0Y/IjUF7EQlhitNlCGMOPMQ24BKaJHI9miMjgcmAywMR56DJKhtevPGXcmo/9llIVNFDlpv/oGPdxKbNOCYjlV3BTh9l8cK1kfop2O5WFdAn4rx3oX1raLveC95PQFHxvZdJ43ueJkMuuR1dNBCKbRd+s03mgHZMfgQTutW3pbpziV5L4iTF1cFlIgmNazp60zIEFMTOIa0autYqXAg2zwk8KO7lBOszN6sjfAwYU69T1+UJzwMqOoRAmEs+ZMq4KBTpJXEhMk3AwZiZQoKk81vZiABQlnRAEU2m45ExFaGyzLfLK8uI80oOayW+/5Qr+wpd8IYYOHFWNzQqHk9R+6O23Jy9sGwLXA6HWAK0VW/X7NoPQRLGW0F3CoCxaEV+CMnoCaqKokqffa2/7XqVrGrrmgxkGz6V2C1w8op7geyo/UxhWr+c0F/GaV+8aR1xkOWiNV6KketfcUXbQ3Qg6MpMT0eMA/BCAT4XIvJcx8z8moocBeCWAJwB4D4CvZOZ7j1/Uk6eH3HorXvyn/0TpNXVMw1FgNUcWeFs4tnIUXeFlzOPGfrkopD0Q5zzHrXcTpxzzJOXukJ+zw2ubv0OtxTXZ5tXc87rxtsBZVq6eX6EOo+WumZB9GFAhrLK8cI5BTIWVohUVRDcLcysdx1OwBvA3mfnpAH4fgK8homcAeAmAn2LmpwD4qfT73FLhAdaHxqiy7bFwQoEABMiWSFi8IE+hGyUppVN6k09ucJTTXo16zUeeTbtesbb1oGaA49Sc2SQ8jjPT05oG9NSqg0dBFk/9LVsW5TzMUvXoyJqcme8BcE/6fh8RvRVy0eELAHxeCvYKAD8D4JuOVcozoKkGozyDQgFMqi1nBnzD4cQJEVAod6HNaSUfZm5w5rCCNNjFmw/fMCGqqa6a/IKYYu14c3BXqEWtgwZCriFH1/bdQL5s+lt9J15gmdAlzvDbBGUyRVT1cZpNp6kuvJm0+YnY5ET0BACfDeAXADwyCQAw8z1E9IhOnFO/8PCuO++Ynet9+J13aFns0wZtw4tMqDq30gTyKA/SPIDyho70Uux7Ksd4gXbJpd+jBbZu8czZ7ZvGaM3wTQZyeXVxitq4NZJpaPmT0IatKTzxv9VTbOzaOBv+03pMHAxoNR6B8IiH3lH0SV2bGCM+9LGPb1Wfk6BjL4YhotsA/DcA38HMryKijzHzne79vcz80A1pnDjW2d/bw09979/HIx4qWXMsB6w6kC7v7xUDOtq+62S/MYPHiJjsc8oHhNtiGE8TJtc0VWiQpo08WNgt3pAHi+GixtsYzjE5+WeO5mB6b259kVNtgZ2/xPaee9dLv/CJECHjneRQJAK75xQgC16IZA7cEA4sPEIb9Ryu10VX1A7Oez7yETz3a77xtFbGnc5iGCLaA/ATAH6EmV+VHn+AiB6VtPijAHzwOHkcuWwA9lf7uLS3n2eivKOqOVgJzEnrsFsYafZaqRXUAdVa0RWqVVZesBg22OBgm9SpqUEgg3eTnT37tszXw9puegvyXJqf//Tpz4XfFi4bs0NPn9U36TzahgmS98gDHNj63lYokhcChP29vaIedZ02LYs9LTqy442k1X4AwFuZ+bvdq1cDeFH6/iIAP3n04h2TtDd9WzeYSeE0Rxgj23O3GYKr8D4+qixE87c3dbT+emF6VNvQLTt8kk6lfeYYqf7z9VkKree0/Zztvylcz+dQOFEbbaPPAhHIOU9Rt6F0tJhU6XvRDmm2xS8o8m3S+l6aS2dLxxEtfxDAnwfwq0T05vTsmwF8F4B/S0RfBeC3AfypY5XwjKjoBD84GYnxqXre67GkqVmu+jnqSscW85aDW/Ovpox68BmA35TRy8/C9+z6RthWOD/Ie4zry3tUR9YSYdMWFL67uXKMTO1ujhKmLvNc/hPhfYO4/Dje9f+OPgp87lHTPSli/fO2HhxrzE2p+N/G5Po9eXEh65jHcTSGrhlTNk4scypN5nd5ml5pEmgZo+VV12POzt34ri7vBs07V0dvvvTi6/clWr8Xrsd8MR0FXablR0OZLjOJ74UdY7p4hXkWxVO/qcya442gi7/irTXw2P7B1Huan1pMS6NnN7a1ap1SS2t5G7iltVsM7uMWz6rPsg7Tqs5R4TMw7/hmp9gmR9wSOL4pn0159fIu067aDDCnaIF7fLu1hhJHH9nlUfxqPTwzurBMTuh4kZv8SulQpRyewQgc7Ds5NE8J68UYq6OIxiINX4pNsM4PWv1dC4MWg9tFA+sRLfKipmb0yTTYBF5aZpM4dfiWEOrF6TGwd2S28miluRTm123XjDcCoIyKlMGJXfxApr1lGPSEXz4/Xt8vOYDyNOjCMrlC9RZjECcvOvR9WvLoNm5ImHLjR95rBRe3PxBbTFAvCW0xx5wTrXYqDcNQmBKWhvu3RS0hss301JwG7qGTbWhT3G1s+Tlon9PILUaRZVNLWqrLBbxLm2eSh52xTiZ82+4/D3RhmPyht9+GJz/20TbO91Yr7K0GAB7Opg5jvyfbT2clCZ7SnFptGkWYalEfNmDyNoN/qacdLM6iTZqv/t57Vpexx+hNIdrwKSzRpJucf3N+lJ4ZMFePZh9ofxX91hkJzkqrPfyt0bG/t8Kzn/5UHK5Hi/yOu38HH7vvk9O0T5AuzMkwX/KHPgf/7Bu+FjYFFmO2JRNTeodV6plCAwpon9rA9f1g6pjypW4yQfpXh4hb6jIZAsfRogbFO+Hn0p/Lx79T51WNAHpp1MxkqAe57ks8+p42+QP85yaUVaQXfJ0SsiOyxTDyNC1o8vkQwGH6XP4Gp8lzuRV56d9f+c7vwX9+/S/N1nshPbhOhhH/STmw8vx3srWqhS7MwOBsLKVCi3LJUEWe2qNcBrDunUBpb+SX8ZmrXWXOARaSP4Dd88JX0KFlkLVNROUxyD58zy5vOcbY1WOuXEBbEM6V3ec5x+DNekYHy6pjulyGuVCkp/kQ8soacvWqP8tyl8js9JXsxWFyY65WD1EdMJlX5WDYxAjpQcGwOYesCfxrrhi+OagZk77uCQcb0NMoW1GLAWvGXDq9VUPzFvXS9s8LIVHF3VSHnunQoy6KoNKfkdNRHw7gvbA8kQlkH7WpUAuhs6KLw+RI/OCguTxMLxNXkA+rr0w7+s4rsLhLZPp+epRDDlMP/pbjrTYJfPjetFqLKZdq5p6m7ZGG9XPdc9N9Pg//2y/1bYXbJCg2OTiXTMdt9IUwJRSIApVl1QDT/B5NQb8TyQkzVRtv094nTReKyQE425sBrvaIp49sS0tPqWAuvecaT142fTQbGK0XBnBaa5uq9eDvBmrZq36ByJJ0ehq7xWA9KN9j6NMc/HM+hDblNepQwavxC7OdDKX7aTRt25CO6Yba7txztm5TtqPRxWNypSSJbfgUDrgcjJJNNdfUaocTVUzZMRHmYOSkoxtQtcU4c6ZEF/LOaNslWq/3bpNmbAm0OQZf6kuYEyxzZd1GmGmfsutW8fGUZthGoZ/ii4mGYogU5T59Hr/ATA7AWlq1sWN86wiUsJkcJ9sxEU1HTMn8G0viBqbv5NZ9YrrIRsO30mmhhxaz98qxiSYe/A3ptcq5jVkwJwj13aZz4Jek7anF/HlcsHnd3VBJEfXDafOOv6HFxNou2yGMo9MFY3IC9MjiANtVVoRQ89l1XKuxU9/1c6LpdNIcbK+dLhqvdXkfAIzjCCLCMAzd/OeYZ5OW34Z69exp2drDve1gbnnKfT4+zU116QmhJQgEnMQ8Ubm8dUHZ58oyV4bToAvF5Aq9TQNDtDG7d/ZvwlFFQ6sEr50ulbZW7y9RaA7uOdt1QpOBpX8pb2c7pwSLdOtoORiZc8jawdVjopm4/M2Nx9o2XKdAbkrJ5V+3Q834S4TUSVHPP9ASWK24phzS4NH/InO6W07PCXSHRXJaSSmDxQkcII/M06cLxeQlcfHVeNZ1ZgH/ON8Vlm13p3k1LHPyrpZQTGG2h5beo15oFKelNAnJOz2Pemkvu3XS7f3RuYrlIG7bos5QLKgDK13aTSYgVwaWm1tb5Wtp36OgibIu85qxxdRz6KL1O8LnBWNiMdVS+44C6wMFBBX6Cb77HWyJz/VnGmYNqHkKdIGZvCKefreBwFycAuLf6984ygaQEEIBoevDIWrmakF0ryE9CpwMfr1gYIP9VmvwOvxpebBNKM6FaZlCW5gNPfv8ODTXnl4whYQKZ7W8opu6nyl/3mi6QExOSUQqh7ZDiUZ3Uh6QGyyRAH7FaDWD1Ec+6e8ek/t0ciGycOnPAChEpEkarUFHIQ/Olg291JHm42x+Z4cpZaHVaIce9YRP3W5H8SW0UMMmxp7+no/ny1vkk/rMTBiLXhlAZyQALhCTb6CpgVkwlE2RbbCrVavXg88zV23neQ0OLYYOCnYyx5jcPWSv633RS0YIyQxoVr3BTNsM/CZz6bOZcD3HXC+f2fw20JwTtEW14Gs5KgXUzbRT1TUaL8YIDpwhvZXRlXdJpU6ILi6Tm4MEcnIHV+88KnaayT9TiE5EZmPXtrclWdnM9dRYQR4tRM/luSyFIKkZMgkBdvmSu7a3rNPxqRASDNt+q1C2NbXlNVv93gvRknkSnjol82IJFaitWuTSEwqg8pmlEZIwucGY/cIwOSOt4kq/85np/nueB+esunMaHZio7/TZ3FFLXW8yprbzkoHMTqt7wRE3aOKjUEvzZiGTccgUEOW2aXnOa6dba22A+zXzrqRe3ZvmzEItX5fFxH/L8dexCe25ml2FBen67Yzk2IVhcvWKe2uxIEoWpGcOFkYnktM+IpdngfXsuhqmWxE6jN57TkSGMpSNmp7zHgw+IoMv1ZK5DFPbe1OcWsjVwtBr89rrXQtYv83V5zNnHvQEVss/0SOTbWmc1OmCARqcA1K95zrOyCfkqAYvp0zHvjWdiAYiehMR/Yf0+2FE9Doienv6fOjxi3nC1OpjB7m0I4dhwDAMxXTYYu1bpdUNS3K4vxXDoQVDDBFg/WvZtJUwuRHkmVPbSuvgzRxP3vTpmRn6PsbYNJO2Kd829QCS0FV5Wply3nyZMCpNhbRHZGdNx2ZyAC8G8Fb3+4ZdeFg0LNR0ZZvmKF7WcV18P6jkNNZyoUsr31oQ6IC0QVN0uhaQcr6VTQ9lXiIrruSfx5UfjNt6nlve795zA5qLYe7UR9EyhWrU1ENPPo9tfQ4tBNDKr/U8t2/La1uVqdLwgK67KMcOu7Y8K6F8LCYnoscC+BIA3+8evwBy0SHS51ccJ4/lZak7lM0xpI2bvaZeEk9hnTzOjN66WGCT3Vc/p8SsDMesztatGV2Z3JdnLu06TK+sbSYu33n72pe1ZbZs0pAtJlryu0UtNNBri5bAqOvQq1Ndbp//pKydNFOjpeuvczv6dM6KjmuTfw+AbwRwu3u26MLD0ybTlkB2gKit5BpbtSAz562Fs+kyDg8Pi0Ux3r4EppAPrii+PM30F9ZvonEa6dW+gE3p9WgJ1GzltWQw11q2934bzdey149Cm2x4orQqjlNYPXIMDB4536lmabDsp6Ac/yzoyExORF8K4IPM/AYi+rwjxD/ZW02VcRjl6jUkhkA5DWQrleAaWzuilrgFFMswLI6jTF2xgLBAMpVFFFJc53+tbDc/eM1m7aCK4jfl+gTInmW200WXa4nClGi8mzixGtrOa8JeOq3vvTrW+ffg81yd6rR7aGauHF5I9ISQlQkD8phJK9cpAMHb7EmBBIJtnGBZKnMWOv241yR9ORF9MYDLAB5CRD+MhRceMvPLALwMAOgEDnIUM6hkRqDhOHGM7uPqyiTmxstcZqTy2m9ygoA1PxsLpRvVykBlWnMM0BuQ9lwFVsObqCbCNjSnMVt2s4/XKufcu5rxanu7hY6W+AWWaPJaMPVQRK/sZfh8JJcKYJdT/pUEQe0iOm06sk3OzC9l5scy8xMAvBDAf2XmP4cbfeGh0+glXN/eM6t28tz7enD01jrPaSMiKjzRs+XxeamWijOM2U1tOQRWf8e21BNUS+N52qbvavtb23cpGliSl/fX1D6bFhfbWNI9Kbx9ex6VTmOe/AZdeJjgbnRawDckZ0cSNTRGC07LAGmbo/ODRSO0VnRBHGzstmdqkHQ3uqFjb7sV2aUrfCgrh6MOmSV2e0u49N63NOScXVun2TIVWlC7hSg2afD6ff09VKsG58qp37VWRPpPRoO2CIbYwopJLmbhWa2EOxEmZ+afAfAz6ftHcCMuPDQNXjFu9b52Itngco1eDlqLLszpkixt+RyBi/cFt8qH/lOPJffeUiGYHT6hBtzc1vHVCjfJqzZhGkVvpblEI29i0DlTpgX3e2hnSVnnwqo5OBEsmn/NsDoOKH23cWYFTtJ8tmgnQhdmxRtDpitqj7p+l4ZGtp1RafJWmgrBHEe2HE+5DEJUPMmM3hqA9a42DdO6F0zJh5tq+aPREm1blAFHH591u5+Ul/koAq5VrvZLTNpZsgvysleHxMjshH8ef/rrdOnCMPmENowb69DYb+TauULp30Dp3C+90ZKmAqC2/1Qg1Mca9yDiJq/yJH1wUedNTNvSnEex61vpbkt1nNbegE0OOF9ffdcTlHO+gi6jV2gqozQA7rrMDNFdREztKjUnX/yVfxx/7vl/DJSq/Nb3/Da+41/+m3YZjkgXjMmLxYclymTVPrIO27+rNa85rrnqWM3BDZLIOU6PwT3VHuPWe5/GEibvbZRYkv8SjcfadjPQedOzJTSXVtPJ2bDVl+RxFCavTTn2ThNoG6lGr/eRA2iJSmY8/QmPF95PTL7qnOl3HLpQTG433Dh7qWTmxN6eOSZ9mjsvBJoyun1nmfeMypT5dFXP8HM2aA39/QEUtTd4UgYviTZQA2k26lOWb5JGx7TpMdc2DFlr69bUWf1+jnr2+SakVZepiE/TcGUYnStnGwsqB6hMQrVHaatHTePk4fuFYvKa2NnltWNkqRbcNIjntIInD8v91I6P37uhROMV+enz2VxRhPVpLdXip0lzTNsSJl0t29DyHtb30q8ZuRZkRZsrY6LH6AQiZzJYABljxALhaYA5f5kIpAd1Bs1z86rLbemmZfJnPe0puOO2W+33ZzzpiSI1e8o5UaEtHPTukbzeFKZaM78BkvcERC/OEg/xUi1Xx1mmmUUlbSsUlsL7pV71JWltCrt0emwubHtKD/CiV4OY6Z4+E39rKOkD5Id33nYrPv9Zn1mMy1/89d/E/deuLalmk27aq4tf+Xe+Fc9++lMzHmeYJ1NhEI8qNaELDos0mFnOd2vARVc2/dYsh57gkjV1LB1sqYN9M6tQCCHkznemQ61d6u2YxTZUUj8BJgtx5jRfj5pxfJtWbdPShHNCqhWndpC17ouryz8HtXvkhdqccPNhLBwBoGoMkTe7NB35jPa+fJ5SFsccIa1vh9qZGeIbEiF80dd9C97+3vdtqt7Fu7o49wnDT5fl89UJgGOOkS0e0ZC+k3UegMnKscz0QOq24jngbepov1u2nBWJ8/eIKOvPgyRPtrY5DXRlfh0QkMMtGCxxKRTp6wBcMi+s9ZjTrAXTEuWjqBN0JTfoi1NrOsdjtcjDaqX6muS5dOa0bw9FbTJXmginNvfS50BuJsBpcOhimATF66wIgO8+tnFrbyE6aMQyo6xPNy2TK7H/V8Vgs000TIXQddCizRy9Z5RwWDmOpi6urAlUpGsRnZ9fmRnlYPILYoyB3f3Zpv2pjHdUe3ujWVDZpj10s0TItLTzNDuafX9U2qZ9eibUpDwTMzELRM/4zbhF38EEaCPRI9FNw+TD3GGBHqq7dwAmThOlJqTE9ITNWjMcZQADmanrMN4B1DrmqM67NYfcKsMmqk2TFhRfkp5nAgMpjbbuObV6ZWoxeJ1OC7a34s3lWdMcEmj1jYwbHwgolA5Rsb0UXpg7nVAIk+DqsNUqhTbdFEz+5Mc8Ct//t/4mQoLZHBl3PfSO3CEO6rAyvCdK2//Q18wmLBa2aY+Z+xGyo8XH98yl3xWu1tQTMkfV2pqmT38bqoOfnK71eRwv1ZYXXZ8vzdsLFM/o5kvABoVLmpaYkEQktnjIphBrflRGDGHAcVv2pmDyvb0VHv/IR4oNimTzmTMKKC4gaEhtb/edJOybczRNtM828mBGq/bK0KKeR9mXu6cRN1ERvhNviUd7E0xv/d5kU7doW0HYQ1TL08vOOPNfEMBc+jIkJLom0EmM15uCyc0VqT85b69kZhC88wMJujsm0RUJ3GDMPEKbWbdgtf7eRBPBUptxVVmWdOgmbT4H9VtxWsJvk1PK4sP5DapyLKGl9W3RnMCea4sebWo/H07DqnmVA9o/OV5i7vpcfIWNFkYiTBDBg4fJ09RQ5MzYkSNYV5t5nF1dVgCkAVHt3xXhkJiLNZXS3pujbTUJ0RTSnQSymBM60ipTG2TOo16Ha6XfChuZ7aLGXh6byrANLYl/FDNmSXqttijLIwtj2nHVQCdZ0xGTxtcZUQfZs/A/XrlvEiZvQEGdGza7nOsIlSPOdQjKYd8b9HPTPts6cUxaN97X2rTnQNqmvPn7ZpTQGrze/pwrW44377BqUc9OnitrjXrqOHMOvVZereezgpOnMxDT+gI2wlhWtrWQInMZNiO+ytR7MNjknpoDoQe57XfDNcJAa2Ae1bHVg3Q2KGuhVMXbZAO2BIsvc++qolb5ltjftV+hl+4mWO8FxVyc2fq7cb5JANbPegJ5zoTxfdfr1yVk3nVLr98P7mHRdieBRm4aJs/Mkjo6DCDWRSNOkzTbJI+SHhvNDfZeeVpOqz61JXI9aLcdREthrw9bryjz3zdD0Q4Rpa3V8+VfqmnLACnu5lIUddjKJEjdc6S43QTb5bOVjjp2vACTUMfMu6SbhskB2AAicscnkWhpY/MJrHffKY+WFkS1tCtm6HU8VVK3LGpp33NCDn4Q+pVd/vkSp5Lm2fOct+pWh2tB8ZqWlKMoD5B9HS7dur2XIphtteem+C1NKhq3jNNrk1ogt30WANJCKXWoTeIhMzdYVj+Sg/hF3x5Tm99cTJ7IrM3UEBmtc/EeKOUpVU+XDOpZ+O4dJK7D2rTMttoGURSD1xeIUayF11dLtPT2DsX2oJdiZHQ1EcDqGNG247rp+n10Eg7LOu2lde4xP/t/mNP+8KmtXhdbBUyRXlWu4wL2m4fJRyC1RvpM66NZzj8vGTy1ZPDh5ZFpFNQMUr5X6q0wM8GSOjW4Ncw2eFOcyCKl5XHOTU/61HyGYZjVqvrc27kTzZiYZbIbr7Jr52jTySwtaiGg/NICTdNJXVMk68tORdEdHy13jG4SCHO+jlb9Wmn6vtX6WD+NEaSbimwBjBtDDSRxEra40rGYnIjuhFyR9BmQMv8lAL8J4JUAngDgPQC+kpnvPU4+7//ovfj2H3hFzhfAX/ziL8TjH/GIqSPGlKy2YsPejZlF/QirB/eslqOcdlN7uTj62XOetQbkJofWJH5WJdmsceG4FWdD2lqOTc6tOt0lzjBN28yGIrCrSnrkL55wyx662GiT82pp+/bebxwfm2gBsDsvjrd/DOA/M/P/TET7AG4B8M2QCw+/i4heArnw8JuOk8nH7vskfvg1/6V49iW//3PwuLvuAlB3DBtcJS8u7fXUzul1WE217UwE8ExP1QO7xdBL7O96YE3szHqh0CwDe19Gn7aFsj0bu9euS9Kv7Xjvy5AHZbo9YXlSzNIqa13fXFZ7kt9VNolHK/UGo5OkI1+uQEQPAfCHAfwAADDzATN/DGd14WHPIcJ5oDcl+DGyLKGm/C3pDh0ILSdTPUCWathyOyfP1nmS3oIy17QNhNT97v4Iqxbzzwm3FlQu9tEvpE1mxqZ3vTLNpuXGB3OJWjhycytuGfdk6Tia/EkAPgTgXxLRMwG8AXKN8ZleeEiAc+wI70vDpiN3avjcSqMDwZpOOMrypWevbhoMS23GOag58VZz+b6VDiWQy+nfo2i4peXuxanDEdHEht8E941hUl1amtTH8Yip5XDseclr6rXrZNx0E8hedDkSKjTKkVPQd9/45/807rv/AUv4v7/51/Cqn/nv3XLWdBwmXwF4FoC/xsy/QET/GFvcRU7HvvBQp6WSdWZ2abbWlths/vcm2IyUcmvvdzGgOvnO2d6tQdcamC2YS6mutQlS1694RvPOq7OgbXwD9fNpvJNzVC2lVr/MaWMVTEDqMy9cOuYlEeELnvOs4tknH7i6FZMf537yuwHczcy/kH7/OITpP0By0SFow4WHzPxs7hxZs5kUEgF5A4uDhmVu06j+5wYY7cp8ZDi8VGPWsLv3e1vy6Zw2OxR5bVFWD8lbkL9rgvH05JX6vf88aZrzKZQKgidD8Sj9uW01jnPh4fsBvJeInpYePRfAb+AMLzyUjtfyzDVYmr4yTd+3kZVsYKQoDHTvqFs6qNUW8/m2ylGn0dLKapuK5uhm2SvwxiA9pNNrt7oNlrTJXFvN9U3vfT4qq922S1BLbf/XfoUl5VCjaArlCTKBnhVUjh/SX1nWIm89HmzL/j6ud/2vAfgREs/6uwD8xVTSE73w8PL+Pj79sY8p6nbrlcv2vTdUctOShbRvFXTuQukEv70H1DPqxDbuIILJ3HFFNXRu/Z7Uz+fVEASbaC5MbafO2eLb+iCWUK++82XxdvG0H2rzZ668dZxemaZlSL+bYfOZ7CWnLvNtpId4+J134DM//UlFtLe8/V3NNADcHKe1Pu3xj8NP/r2/jYCQ7JYKurau7uVklbPr8I7N2mJya+BKU/a0bE9b1EzutbB/3irLHESdlGfmuqeaiAgUgk31d4VkZ3BvEj5zabWoJyB7cevlxq3wqTc0g0leLd9GnV/PBzLJyzvd0vfRXZU9DC6dIRj6IkI6vNMpEiKEVYnURNlAwrny+7w/7cv+l5v/tFYCAD/gzdFWkrc/LGjytMuzhbaxziU37PdeOj07vvWsxSi1oKgHWIyxv9tsQ3nq78GE2Hw5N9ESzbgJCbSeeeflnPnSdTCSgGZ/Zp/X5BNnWafcdV9s0ubKeDFOwzO7VRWczD99lv+Z1sVR6+68TajtpmHy5NZufM/eStSOjcLpMYVwOVirwZKX3AbMNOychuunm995SdxllDQIirecvbG6rLZH/QHA+YOyHWkZFdG23wabi78ZhTSR1IL4vTTLskoFj4pYu0LEUmfXhlnrFkI8R26WcVKyFnrzUi8nJbpuQ91uCiYnJGZrwC/KE0hZc6tmq+vuGr9lT9eMl8f8Mk3k7fQ57VVrkt7BjTkCQJzuznSmSW8JbTOJQmvJevr8MvssLK16erAjjJYigDlt6dthruwt2qTFFAFtG28uL9/P0fWCClyKnhtlSS6FNoO3Cw3wGIFAdmxUWY5ct76xlelcMvlXf8WX4XnPebYNgMv7ewDcQC1C62GO83BqjloOpm3szqW25FHKxVKAjelv40wCYEdniZa2UBpYGD/BSdZHFYrRv5aHeZvyFTMFLux58BfNEk3R4cQ3w5Cz8lEqAnmQTcKJPiLCxFZMF2swIZ/0iptUkz/2EXfhmU95cpbCUZYDZkYuw2dNzsXTOR7bzs5zeRaQqZTsddzeIF/inVaI7qF67e2tbc1N1PNE63cvRM0cECkDxFi0O4PtNpecQst86Pse9PtSX8ZGoeH76IjyYQ5xTH5Te4x5Ta+CoOmg88KV2nn3TJpt6ncumdwTMWW7g8uhmZ1wUOPEx5yGm6FaixcDkqfjpsWom+y3ufIU6XGugYwlmoSb9SxXZoOP1ypTS0P3qGA09XKDwSHAmzU5n4lEdl8JtbLaVCfJNw8E6xePKoq+n2ee06C+wJXSZgZPQsIxfOlOyMt2J30Uy36do/PJ5CwL++XY5c0dUnst9QtXYeo4redKLS2Z+P3YMLxF3q+iWrIuqy9X610rzJK4tbnSQjWUzgtnKm+C0fZQuzfG9nlzkoj4FsjBXBuwFfQ1AefaxXsOChxSy3f0BewmWoqKUmBgk6Di5PJgMpPHbsoZAEoXotUlVRTgx38uF5vfiYnM3u/R+WRy6OaSLPmk8ZFbQ7XrjKbswpuUjnpGiwMkGp5eH7Hu/00MtXiwOXtAq+nroa+dD6yoTFD73ZdF/7QsoOK8eitX4rwmk9tUkObtjq5G1qJI+Xsty+5fLWmWlFU/1Zzr0+VqhqEhsL1/YNq0bcjbE5DbCYdpwX05pH3IGLZVNvIa3fdilXShcIACAczR+WTyBL/tNhSIc4bSjzyAoWJy0oAiMcsBRuXom36G3JCbJPo22nzp4Eojonge41SzeugqkDf9Vo95FPbzNra1XUove5yTI8egcNSMrDhqlzOHpg2aQXlCXk79egHKlIWUrn8q61anWvZHLWQnbboA9bXiKjUFBDbzUVs+tdJHEVIFpxeW3v9jQrUSvPrcHLMbCng+mRxt6FjbcFkEOAnYgG5L8rH0O9Tzgp4YVcqM2Xf21CaHC0uc4DQPzhFGJth0/pYjI67zSiyzmYnzJXtJwOaaUobDDvUUfougbSNpqHANgSDnHaW0YppwIpLpoeSQ0lNfah7z/ajv9NmEibTIeneBXd9OhaDpUXeGZGPM+TAJqTedtABg+819e1fCoHDkVRQ65fZ0LpncM3ghrXXgcsVsnG24TGb1Fb/myNt/xXNryL7Hfs6Z1XrmtawZq8roDaZuedSVP3OiytAOCgM2zOMYYSdYm6aXREK0OQrXjqpZKAnPxEFWhlTGqt3NQohIxzSX4QEYM6rJ1RzEmo7TbAyke/CQX2T7K5fCMU/dni0Hae/dHOVmmj9vLr8i/6JISMvpEUvW7lQxOhdttglVnk8mR8ncCs1N2hXa3T3ymq9S8JM8KgGi8Whq9JYlY0wad6n3vvVdMbHX2lNHy1QDpISgJovwk4gpHrmA+VrXvObbtTELk3Oo4LMyIKWGTIJDzMss9AAgxgwhC4YLNdPkeFx1JcM5Tyu4VjA6gHGcPypbyhQNffk2bDGFZyK/cGaTiWXjq9U3ml9ZG0wHJllf1KaY5tsU8K6Om8bfuWTyHo1xTMhyAYgiLN60MS+FG3aaHyjHxO/1IOgNwiKvVqeOglEJhHg44uBwjXG9xjiOmoqcCLsaAAYOD9dQDy4RIwyZyZQ5jGchyliZGcSgweOkLGR1kUZR5kKT5bIbQ1HaW6D5Vu1RM1vpUyjz0/bzHv5Nmq7wcRxBm2+C6yJ7SwbtTR+2FM/UqZjbcUlZzyWTq62WnSkQ2NK1x9pQueVV9TCnrQnIObPk94bCLjIFJuVu9Q3Ji+jKpb52s9GjQFECYbXaB4MxjhHrg4j1OIJHxuHBiPV6RBwZMR1lTUSIgcEDg4kRGWZLYyBwVKTEYkszIxIgUzxstqNxP1NC4d7sgEPNbs7aUBjBRXGCoBYIlMBD3a8pbGTJX5EMpE7WF0GOWRJnLZXxi8Zub0AxHbsJqXmTwZlHRd2Ml6Ws0TOnHhleAqOJk62Z8QxKrelcMrlSMQ2UH/b5A1nSa1zPwDUU840ZQgBHFBfDA3Ujt23yno3XI2r0UAgBnIYAs4eaaUdThHjG14QBAQED9vduwTquEQ/XGK9fw+G1NdYHI9YHwuAckcaDmiGM9TACAQgrwrBHopGZAYzG5GIWMpijoCaD6XqAQkB2A5daMDWIsb6hrgqea/VE4HotnOKSMEQxZYTE+BwEVjDAY7TwukyUWISQ9aM1dc08UybOa/iy8K5RgoU1IYOJSvcam9Tfkhja8vNDwN9qijJME1FWpsgcnUsmjzE6mLmZvM3mmTyOYxO6tWweL8llS+cwyadGA9vPqfoy0+R7AcbSACICxvUIRALFgPGQwVGOAxwP7kM8ZIwHI65+8gDXrx7g4PoahwdrcJTBHUAYwgog4BCHODg8QBiAK7ft45bbL2PYIzlwZAhiHZLUPYS8eINDMLszUHl6SdtepKI/mibPmGfbC0Q1uHydgylQ9vJHTIV0oRETxSgWL6Fl17a1pO/NyO1DRgoiuBNbKPddEcfVMZjbP5nzGbX0zENftiXgsqZzyeQFTaDLtIbE+bEsCklcP5T7iXOSbXhv352Dhj0kS9+mnThXfF9+dwBAqpB5plPdiGVAj+Mow5MgmoshmnkUjT6OIw7Wa8QDRjyIuPqJ6zi4usb6YJ2mycjgPWgEiDFixJrXCCsgBGBvbwXmAYMKQlcYFY4CKwM4OMY2yJ7r1a1/BBg6956ZNzVkgVxMiLjkPFPKJxk6AavbhY3BhGNUm6sGbZVzurbCvzMhVfWjF2ac+hD+E6V5qPlQI5w0I02+53JOncMqGJa4pZTOJ5PPVKBpJyWeFmEaYHOxFU0dOPo9oR8PN61TXTzCzMDYkrwWdBXJUz5ugDIJXB8BigReA7yOOLh6iHjAGK8zrt93iMOra4yHaUNPirteR+iGSA4RWAE8Eg7oEIdXRpmvHgKwJ0xOUJga0s2xyXYcQjHQbQYD+TtrgX0fVV5++0zhbUqI5KopJp4wDqn9b3kII6fOgqlF1jiJ4dXP4Pt2hlRXzPVxIbSr5y10ZgPT14cqrzuVpoGN8fTv3CzCEjqXTF443ihzVs/BVSC11GDzWjYNBBtLGnv+HDYwbJ3wnEd+G/KmhvwWOLdakTnOVjwgjiwMfADwITAeMA4/MWK8HjFeHzF+gjFek+fr6weIURx4h2vR3kwRtCJcuX0f4dIKWAXwgTjisBcQEMThNoiWp0HWROeFLtM6EoVCMBZCKT3j9ZTJ6++SmGj8kdnSFcuATIjrtOBcE2cZQcijYplkzrx4cuveLZVQI8qCzS3fujx1KbIQ8HWbL+u5ZPJiLTlQauWMnzKUZldNd+xO4VhL/2kSRXOqRtex6TFegR3TR2OUtaR401Hjvmdt6OM5TUSyOm3ACoGAgIjILAcKHALxesT6gTUOr65x+Mk1Dq+tcXh9jfFgjcgyV359fYgIcbatLg/gNYP2GIFJFpWMUWz8GAEmBBa7kdROVGienEe6gq4mRQ4xql2hmhy5f2yBSm5zST8mWK/tAHBywAUC0hWh2dFltjKlPtUluJxMLSiCl3EwRllDoEKr7oNJXba4eKKFLKvfHBiBG8d26WYfNyC9w1UbSxk7/wVQ2Hz+n9K5ZHLzRAKmmYlkkYfBF7Xpai1RMLklUUAiVHEEPpXLW1uLENKPXEwfv/G9KFermsW7ciGqPAkAj2nJqrwbxxG8JrHFr0Wsr65x+MAah1cPcfDAAQ6vrxHXYvfGyDhYH4ARQQNhtQpyO2wkEXfFHv3kjR/SctSEiImoWIvuWhReFiqDi0ffa3UfziXiTk/hYrWf5pveORNd7qR3Jaj8JgxdbecZL4+ZcgtPaQPPMWmLCuFdxWuZlJPn2SKbChqvMFAzeG2Pt/R9Sce91fTrAPyvKZdfhRzJfAtO8FZT7ziTgeg2UNQdEzktedTnqpHIrg/uzo+rvVflWzRoByf2NEIr3OSZ5ZeGKUt5x3VEXEeMB4x4eACsCXwAXP3YdYzXRozXIq59/Bqu3X8dB/cf4vp9a1y//zoOr6+x2tvDalgJIx9GxDgirAh0a0Y0gQirEDCEgCF5tCmIFh/29kADoFNSEWlencgcXwVFdzdbhHCpqutRl9nGYgrTTt4lFOsCGMhHkyuP6gIbKBIYc3jOXunYGeyeQTbRpqOzNb1N77xDDdQ42mqK1hvpSCPUTF5GPEVNTkSPAfC1AJ7BzFeJ6N8CeCGAZ+CYt5oWnl2v1TdBk9wuKais1U6J5kHimNIfPTTGMdmC1NwTvVlmbkeFcMjKUTZyjKPYs+uIg2trjNci1g+MuHrvdYzXI+I1xgP3PYDDq2usr42I6zHdd844uH6AdRiTOUAYVisMK/HQj+OIcQwg7CXbNx/2X7QPU5ruIRA4rdtI9rA62hJGZ05aNuZn+j6mU31ijIijm5KibNPHyFmDI/Wd2CfJS+7bh22zXe1Qy19FOuh8vvan9yGosJf8Y/Esp7dZKMi4aoe33zNHNRk817oUzOzPac/0c296C37sv/6sS+sUNXmKf4WIDiEa/HcAvBTA56X3rwDwM9j26mLfeR7y1MEaMEu1g8F11iUeWSrWaUjaXmL6wQCLt4nBayle23b1dx8v2+fJM55QCZgxrmXu++DqIa5fPcB4LWK8NuLg2nWsr49YH6TdZclUOTxcA0gCay9gNaywWgUTnDqXTTQYk1NxHVGG2pRsYmdNu45gKDqXtpd/FU1xhK2jH2MUc0vhdJCFLyEAuu8FyRPNgWRln3I+Z0heuEsYKR21YzNJ29daML/LeqMy96o0en1sebS84j5eT064MuuY1eBNM9Hl+c733YN//zP/byfhKR2ZyZn5fUT0DyG3pFwF8Fpmfi0RHftWU99weja1/k7pJm1SdYKXwsiDzn437OlxzCMmDAHDMFSwfLPu9h1cM2+L0XtxNTvmfJoqEWGgASOiDHwQ4nrEwbUDXLt6HYdXR6yvRWBNCDRgGFZ44IFruHr1KiIzbr/zdtxy2xXsX9lH2CcMKwmzWqW/VOe91QphRcAAWS1HjCEJBRoG2xrKUY3sqhEco69HWXEHJmBUhmd3AAXL4h6K4EBguPPj1gAPev2x9qIwvQoom3dPBpahLsqmXFavPY28PSabc9L1tL44/tjK0VQylL/7cVQneRSPP3A8uP5QyF3kTwTwMQA/RkR/bov4i241lYoKk4RAGapBtZE+Y4O6urZb50gLuF81tCzE0IzyKSQ5DJVxKts9EE3SlGKUGyh0UGVHkbe7tB5ey6Y546RO1VbevxTAB8B6iIgsO7IO1yNwyFgFQggDrtxyC1b7+4iI2L+8wrC/wt7lfdz2KbcCe4zhEmHvlj2EfULYD9jbX2FYDWK3DwKTMaCAyARlsIyedbqaOXn9IyOuOU39MTDK9laOMC97brhULyaMSbAxgEuX90DJYh8YCEyyDTZwyiNi5DGjDiD32UAYwpCc8U7Dpz8rt2ny3CcqiL2ZtoSpeIzSBpHbxzCl8aG5ZcSYpirtPKyMNjww4ZCQEaTwCwyICR0Hrv8xAO9m5g9JXehVAP4A0q2mSYvP3moK4GUp7lYiSmzG/NuUvf6hgsyAMbh95tRg2GHSgu0mpco69yvuTNtwPYAI5Tx8QytAzQrXyRqaCCEQwhAQhgHDnjAmDSMoyECj9G5/JYzNxLK6bRUQVgP2ruwj7APDPrC6NGDYy39hFUADpblyLtebgCcDXheU+TrHKEuJ4xhlY8xaNs8gMbmHpeZgUxPe+i0kFoc8jJy2rMZkBmSEUzShMo5dQ5T8Bcnl4Bm86MsZaKyMP4fCYlpDT6Fa2dbII7Vk2Y6oR0J2L+qb33j3b+Hq9QOr53s/0GSpLh2HyX8bwO8jolsgcP25AH4ZwP2Q20y/Cydxqyk7xkkGoMI/QJvEMYYeEwVnbyHH63ZqDY2qR3MedJu7BpoMrt+ZgzuSip3mqOB8QhY6rcey9QwUIFpsFbB/5RJuuf1KguiHwCGwt7qEIaywjqM4zQKBMWK4FIAVg7HGpUuXMVweMFwKuHR5hdXlgL3Lexj2IMdfhYy7PZgBkW3i4ZjMCcedOuc+rmUHHI8MXoujDwxQ1Bn2lE4SEgzklW5AXszCggKIZGZkHEfz4IfkXxD7PR0NFoAwkD0XxlY8lxmr5Vgr+6gM25sGq9OYYGvXdFlQo1i7PiZHJqV618QsQvLF/+h78bbfvruZ/hI6jk3+C0T04wDeCGAN4E0QzXwbTuhW0+y9TSdcOk2tcDiEkIS2wLnBeYh9J4164SBgTibAMWKrkStHU5vRZVSaa4oAdoPFl0ProYwt2lk2wozjaLBWGEkG9BhjPnwRjDUOZe54j3Dl9isY9lbYv7zG9QcOwTHiMF7HSBHDMIj2vjzgyi2XsHd5D3SJsbplwN6VFYYrhGF/QNgj0CDMIlNolFZnabXZ+kEHrNiZo3jOR/nOybMvKEu4VzRnSGfmp+bKEjhpaiBSXrue+5UQEUU4MGFM02aFbUv+d9UtlPEWUUsLV+PMfdZ+lRZ5f0sxGlpCRDOs5AE3AaxzCjrUcxw6lnedmb8NwLdVj69DtPpiuvXKFXzWU55stfnUT3lYzqPIrw2gTTcmKF4zeMFwAEINPbWz1C7XzDDt5LoTrUyVCVCsCrNye6zqKqXpKnSNnJmJo4QNAAZCGAi8kvcDVlhBnGSHcY31oWhTImHcsAL2b13h0q372Lu0wnA5YO+WFfYuDaA9Bu0h2+AKiALsvDeiPOC8oJv8xfIkGjepBELpm/C+jVRJaxCxpjRdl1/6j6hebddg7rqvGgzeC1sz7Vy8pr0+xd6tmFDjukR68o7rBKi1vnA7Ohcr3p74qE/FK/7WN2fDrFjQ4uEvMtxLXltvwQDePmzPfUrysRxwyJY5K1asTXdM4Zyn4rnZl4BfvyYyJDgGzhrPUEr6UwZnjuL15gFgwuVb9zFel0Mi4ko0XggkG0EOZHPK5Uv7IBJP+pWH7OOW2y9h//I+VpdXuPWOywh7hDUOEfYBWkGcbMrcusNW1DCgwy5pWZnvLhckxWQrm01KeqoriWNJ57ttP3puM+knBkIE0SACQw+AC1W7LmBYE+hx2h9lP00Fff19CZOb78e9K8yAOg1WVpb5e+9s47RgC27txuaJ2810Lph8nrKEpzRfS1Td35y8690UzPGVuWjakewEcQXlCzfYdDC0GD9PrQC5r9VYZIMlij6I9KRVr9kCwhDADIQVgxAwPOQK4lo2q6wP19g7GLA+XGHv9lXiScKwkpVsYRiwf2WFvf097O0PuOX2ywj7AALLardL4nAb9kI6TYURabSBmLxluepIdRkAjOIEBCI4hJR38qpzdO7MrO3zeKfys+YDjoiRMAzZorUFLUHblGAONapF/bQvfF+V2lPrlc08/b3NlBUBxamsLnf3ScbgPl/V4l4omDlyfB4/T0zuJTaZJzTbVVR61K0TyBgfyHZMkgwZUpv2yK/VLk5PJlBNVxvlASDhetpgUqNCOzSQAah4p+jVw9UQAsYgm0cQkGxoEm/4HhD2BwxjxOpwBbBuYUw2+RAQ9ghDOgUmXIIsHhnU4x5k2iwgrxFXSTexjUrBZ3drk0zvhVR4ppiPNdKFLlDtxbn9qEqa8mmxyrCk7neXb2l/u++VXZ417NRsW0JLwmYPf2GMleMI/kjotjLwY69o8hNgcOAcMbmH3koymDjbg+ovLbSws5F1KaxJwayGMhoQbVrDde+cKQdUWSZ2aWq56zL36pe96e1wCtHHMUPf1WqFGA+E6QdpgwBZohliEJZggJJdHCNjvT6Qee8QEDFitSf2Oe8l5+MqYO+WPRukMeidZp16iJR1qINseToDCKsBgQZEYmBcp5OZkrRQ00vRAXsGTIIvzQQg+FIk4cO5PLk/vCmXnK+Uw0mTzHOIT2cyPbiQwckJJnvuhGF6ULwjE2icj6yGjr9gmkAFofqRjkPniMkBgyzeccbTMPl3bmLbKcX9TuoxcV5Z1Q6veZXCxXuDY+GIqtMfhsFQAFDasHoqitzemlGHVWEg7O3tgQeZPZD13wSQzEnL0AmJ0QFiYO/SYAN/CAGX9lcYVivQHrC/f0nWsQ/JxuaIUY8vBuCXLFgZFGYUD+VRZJatJqsgvDoyVkx2+AMzI1I0x5m2X6BgNikP5XFaQbeEtrrRmznp5ziOCJB+tL6vtHoJ01Xrl7Mgc5RNOW/GnSxNFMaxXW5C54bJleqpJz+oVPjaEypeJ8mqjF8LCC7ClZpB/tpTZEvK6kwHp1FKO08KG00YVJ1adDBlMKKCJeS93qoVQ3LiAcLcBELgYKesiPNNF9AQwl6QwxsDpf3asdB4vaorQ0SOCRwJQ0U7cN0qIR96khRruQlxSDvPtN3TdtZAARiyQI2c+8Wgdup3O0sd2QxjcJ6/dyhB25MqWVFD/pZjttkGvReKUICyv4t2KVonm0I2VlwY7xNIL4/L6ueGycUeTM1VaHI/CEU7xRiLFVft9AgtoKMDTJkApGEx6bDa41o7ZczLnMomn7lvlbn1QAYdqL7XFL7zGB3Ug5klMcY0HhIq2BvkOUcMGLJTi4EQhmSyJG0ZAvb3VyK8gsyL6xbSGEdwcqwFyuZK9gkU0lO0sh6TLhABFAMoap/p+nM7IlZ8CAMhQs6tGzCIIAokK9ECrB+MQeLa1jEwr3O/2IyIK4PCWSY5RiDqNBvMXyCypmdSTVFhHWbO/AKcRYiaQVX3p2cpP+0b7Wu7408dlEUZTkabnyMml+rF6pRWolAwnzVk0hSl/oOtKFKYKNC2zGsYqpNYnQk/1cCYfG9Sel3McTsbNGjHJgGF9EynApkZAyXoOxB0ZsmXnQGMlIWEMj8g8+dqeqxWVxJcJ4S9Ia+yC4S1FFLObBiS0VtdQqHTj9oOwyDe+jgeCiNLBTGQtHccGetxbZo98piRzQoYVgGEgPU4ylr/QTbGmKaOo7WXdJ8IjKiVhDC5LSLSslFqFXdtctq+BgTCaljBTEDfVZO+nNrnfgzU8YMbYz68tjlSKbVvIsUivgorAkAxa30TYidsCpwbJtd2Ng3KJW/W0w75+p4Mk0trUaDixBGCcgP/HESfY3a20Q6f8cTGy34DsaO9XK+JkfrXaytfHiBfTAjC4LXNkFEKDSGv6iNCvSW5GOI22vLzGsXUJpMyJFsfqcTlNFhzellTy7nylMrjbW5zojuhPRGpCl9ToBhj2hCihRDfBKF933dR/4lpJGNobg1E7c9RCqls/vm037Ki8XPjLoecT0MpHZfOD5PDNaYd2FcZLZ5x1DbTYaYCIg0+kJOlVQd5J0/29LY7uOV88w4/6LcNjK5CKV/fWw0EhcRmixUZ2Hs/oPI66NJ5hZDySXZzS1jZoRyA3KDiBKvC3Mjp3nM1GwrmVyCcbHvO0MPMIMpmkeQXUt65TqQQKlXaNKM2a/qdBbMzJSLbfnKC5DWEIS0t1jHU7NYJadvp+vha+E/9OICaer1M/FRh7ZBNFTD5ydHlp8IP9cr7o9G5YXK/QslXKia7XJi/fRkd1A6zn2R2qDWc28vsO8nFsp8tzT7RblvSnJZov5giCAOCicHDarBBM2IsBlAtnPynF3J13YTfxMkmR2YF2+IJE6JppRsU7Zt0kPTSSa8xsszpJygxrIZswgzOFmHfT6VQimn/uh14IeKngOv5ua8j0v32SzRjycyTtzOSgoEK6UwduKVzc5pWMV3G+bRag/Cbir+BzgmTt21f3cY3kfBVWJOmFazyn/78r6ztc1rdxq/zajJH28nXS69gquqZ5WkIuLQRoQPBa5ukUWomt3wS6jF8rWqS6kbQf1y9CVXbO6+3BeOEMHSNubap8n0KGHKWRXPrYCYkNU/anSDvnDALqQGHGSYQKFBaOJUZxbf1HLVQXk3FdVuuOD6exeeSycsyZBTS0vq1T+aodC6YnFNDEKjBWGSSsldfawsd4O5dHuhleA/1p+XpD4amMCiw+rzkrxFBKy9mlksUqnAhBGPyyIxgDF9qq6zlUNjwuvBCAHa5ZLXW/DbYtX4WpvICJ6FBlDzmY9bEwq/cbO8sAJAEhHwGu8MslcsuNkxaneXkmqzhghOIaaiw4jJl7s2HMxZtV31vzayY8NRyVsLV2lJNOVJkok7N1CZU9p3vA037uID9XDB5Tcqw0pHBGiikjvXSMguCDWk2Os9TT3K3mNB3/lItUXe+j18zszpgatQAn5fzD8QoFyeYzTfk8+X1AEZmTMqa/RHR0IDOCRFRnoVIduqkfZTRKDnTmDAyFza2XngY/WWSSMc+6/g3xyAD/g46RSgA4notK/hiBLMsAhKNH6S+Xbm6LYNkydfrW49s6m3L09RKlJcRnAhBS7tVfjWFjsfj54PJM5pUmS91Fs2lgyKYqPZwSGBNxQCNDOo2bNnYPa1ax2uRDsAS/bbL5AeFXKrHFtqYPOYwrAxI7s+Xj2B2b4bySXu5Q/h92fO+9lrAJciboLvO01teFdxkloGuPo8QsmYNKqQBhHTohRd2dbnUUebbK8PW1B4hOxz16uSWsMztMy+op+E1bH1MV37fcsj10l2qheslv9kMOz6dCybPpJ0Vk6RTEa/MnOegkd+kATADkTX1DkzexmYr0q06tD1w2mvy83cpoQ5mqmx1G986mNwgMI3iV2SAgXRUkrwMzbPEi7qavcP1Ay0lJjDdBdcB6RmAqEQfcsSzPk9mR7QEXFZseQmF3L0EgGUFX12nSdm2oJaPpBXG6lQ9r8uxcVy4NsoRYU1ubQhqXGyxPZ0fJk8d6IkV+qlV1z34vm1HLc66EaenGXrxm9pEi151kt67TZRPlJF0ACQb1G/MaTF4KjBA5RQXwLb4hYHqsgmNJr9FS6e0Yk5L6jFtQzvPTE0l335EoMA2f11rbA2XTQTICTTes67VghM3HEtxQ7KYyZtrqhQUtajTTVDOMbGuyzebGsHaQ2cdcrg2gzO4YGK1yeczRV5EdQw6N0yuiwAm87qpHWKMCC581nT10YdlmDyAqMdzuQwNLdmjlqe8GS59hsrGbqWlEczXYI97NaxzktoJCkq2LMsqOu+2IB9FbxNlLspROIlSDBEK5WITuxBBmznI7SzalvXquYkwdAggUMDoDmo0JkY26WryzKcorxBQC3m8Bck1jbySLWtzE5Scz3JpOu0qp+zEQerCmLD05kydxhHo3DA5ik7UKRSFbqnCDdsa6Gvt2sGUEs1ZLoLb03Ra0G4O5qkntpd+SsCKpgzO7p2VH26cNAZmrqrC5GrBj0fkOuqoVX6xv9s2ZoLvycRgKxTlz2YVp+1FlNd5ExrlYBXkGb3UAqnOY3KEkuQ0V7QyZLOf6lVqZZ7dvm1pldYzD2YKv4C7CfKIdE6YXCSYnBAUinOmsx6pYszA8t4g8HZu6HTKkruwNBwzF+uVtVwt55KFqwVEunRQec4eW6dnLdVtB4LswiLxNgvlFWL+0EuG7jt3R2ClY51EpmY0o/PPycxHwGDbZOuB5y2IWJzrpvXiiVaX6se0rToikrsDHX61ImE8PBRPtqu3NzsAyKYZpwhUgPAIs+tb/d7Tll5o+h5Q6E1DmAgmrwCyAKsyA0sHBxSdms0wF5iWT//1aCOTE9HLAXwpgA8y82ekZw9D51JDInopgK+C3J/5tcz8mmVFcQ2q88GAVVg7ryntnb3TtOsbtK29Xcczm7qxDt4/r1GAt99TtZ2NmxFMCFO0AGToaDa6Vn1QoSKDYrQdcjmdcRydHatTdUjXHMdiu6bAZ+sQaAP7q5ayXc6gdLSMyIxSyHaQdnpHstEl9e8I1cZiZlj7OTNgHPOFh9amKYw3E1QAZjuvp8bLts5CKMOewgQQ6ZPrUKMdD/dJhEJ3vBmYynXJqGgm3hY0vU1tSj8I4PnVs5dALjV8CoCfSr9BRM+AXHr4P6U430tE1ZaveTJmdhoxext7kQAkkOZttNngc/nX+VK2p1tOpFZc/S3X/YRJugWjSuACatfMXJdFmdBrLdhgEmdUjHoJYjQmN2cRcwF91YFl4V28GhmVdc+MULcJpzyU+UuHkxcCDjX4uI7ChvZr9bkKC/ekElD6X8/0Kts+p9ISCH2aOGMtDUyep0QnguQ4tJHJmflnAXy0evwCyGWGSJ9f4Z7/KDNfZ+Z3A3gHgOdsXapG5QqGqJ/PJlUx4taF2Y5azD8fYQI+irSCXi+c/mpSIQLbIJK1SIwR6/Ua6/Uah4eHWK/XXc0QgtyJthqGIh9m2bs+z+iYMEqzPhU89Xlo3GEY7G+1WuX91s780feUzI8RjJFjwXjaLnZuQJ0nalEzLRPaxZ0I89b7OfLTYrVS8EKLKC1IOuagPapN3rvU8DEAXu/C3Z2ezdJ77nk/vuo7/z4U2P2FL3we/sgzP1NeJulPILPV/WDyNrH3SNd2cosmji1M9wq3nGw5Wvmszmdig/rPI8AwdmXVwV68dVNnSFDcVgkSyV79GLsOzFZd1CbWxiraJXsKoQ44X+cu01eZNxmx9qtw1smFqdBAfl772s0sNkakhXqbRgqHIGc7XF0dm9BDUU2aObjEoRsQiq3Bei88jjZMJnTSjrdWrZvFpOrCw59981vs3Rc8+/c0B0ktfRt6vfiq2/e8DVwKBAfRqvRaMHUT+YG2tS3lDNeW8FD7matnGhXRDacEx/3g19mJlAHyJu6yECp/zCxMh3bAmSvWdBmpm2Ap2npTlZ02azVIwcwtOO1Ms5aZlWpUaE3PuNjA6J5Y42M6Ttp1ywxeLG21fMhkZ/Y2tdvhuHRUJu9dang3gMe5cI+F3Fk+IZ658JDgmUUZu8+Q8G/U5nMMUzO6T6dRLvucSvWSlqAFF2AS15cpD77pYC7sPzeovXOJ4aV/bA74YRiS04rSwNPhlRGGON7UJCSXJidNjfLaI4kI9aADsFV77WaohG5igGJRTvKvFD6BatZDEUa29SmtYadycFSCJOefGXearv3I3zm38Ry6U0GQpwVTebjsW+gv5W0vsFN7zzP/clrieGvRqyGXGQLlpYavBvBCIrpERE8E8BQAv3icAhZ94DUl5HigsesYOk6uU83Qc6CZTTyTRj3NBmSmcaWevK/TmnuvYQKl9f4sZ7KN64jDwxHjWhayDLRCQEBcQ64ZXjPGNWMc5TuPAI+y02u9jlinuKx3tBVlgB0DbeaB1q3SvIVHv4DWKASEN7nqNNQpmNsWm/vGmwKk9nCJ6rppTFt48qRFwqjkf6Q0q/PskiPQC7PYGsuLcu3Tkim0fwPg8wA8nIjuhtx99l1oXGrIzL9ORP8WwG9ALkH8GpYtQ1uRVl41uY1vZpO+1hDOVpvTphs1bRWmlugibKce5Gb5q05qhtlYmmm5epCSQGmFWxoQnDdXKKzOiqTWToliXubSKmdub/+yNHkAx7QutdLM8HVqCL4WLK80pwmLFF9uMiW7H9wBHpeXlsHZIqptPeNxqa2Luk1qU4XIlSsYvRxLOWxtr/s6aXlLRXA02sjkzPxnOq+alxoy83cA+I7jFMrXilKHcOJ2bQJ/fJHaqzXVzLGtEPAdr8KlhNdVsTkvfJk87+Y8T82ysw1Xez6EvQRrOc13Z8+FaANCHLNZYPPdkdP6dh3gOV9NWzR1dVACKhZ1/Khr57P5SU4YiEYLNJTt6xhcNZueBNTS6Mycrk4aTBPr3vkaI7VIhlM5i1C3dy0T/Xv/vRbq3AkvMlBtobRzkNpjz7fFcb1v52TFW0WVHdWqow5CW7m1INmWXd4KU+btN39MtWnPXt+UZx1PJb8+b13YaIPZpa0LQ4SxdNplVTjJJH+2MABjHNcpESS4XNZf4kpYheFa0Fwb57yjtg+hV3d/k8x6vTYoTnCMZvvHpT10MY9cViEwRE/8CelaKEoanUl8EIIK26vGmmixKGdLVByD4RhyOo6aKY2ttz4PgqKUo2cJnFMm9x3N/mHP+VU9n6DI4l0ldT0a6MDhVvye86V2LC0l0co+HUUATotFmbN2oewje29ZDl7IvCfOLYadS84gkM4rM8C6hlYRB6f138assNteCzs6pQ3I8tQsLDS9XEwNZ4PaCRU1Keo/QRnln8YB62xBRAhyC6o/00+uHJL1A64Fyzbn0vE56TPOZbe1fU4gzJlluR31PQqHchrkZZ+H3J8uEpapsD6dSyYHKubJ42qW+WyAVThrzjOuaAAo4a+P57WnX9vuw9YatrbfZ2G+FVvNDk43nGQzRNeb8zrHXw17YotTvq2QGYijXsjg6ssEihm6AwPW41pQuB5wniCywW0tzRBsG6dcYZSXjZruiWVe6VvxLyWISskpaO3JAJhACDKPn5bFxtGtwGOGOhb0rHpJeMC4jsBK6ifOM/mzVSdZhk3setcDbQXASMLDnRMP35fVwaCc+8EyJYjAISfkHEplMHQ3IKATDPKMT2Bp67ll8iVUON+czbcttSD4NnF6UL4YSBV0bQoel573wCqD1M6ayCN4JIAiQgy2vtsuGNA0zHubmSrQIMwSBZYTkWhKXThnecuFFzxquwyydVVvULUq+Tlkzyx+owwg03vV0UdjtBR0Hb0ura3biVnvZBdnW9hbyQq41YBhT+18xsjrJKS4MCXaztWsUXsIzTR6QxAY7IfKIZeeOfrE3xGhXTMVMDFGvOUd78LXfc/3wskSvO+DH8Zx6FwyeUtweYYpOmACl/vp9Ji3hvx+QNQe3663vAHjayfOEoncdhKSaQF1Yml6DDnIUTc1RbMxW7ZeyLZsjElTAtCyMtuZ91Yv/TfZkCIUFH1wLpcoHqfN3PMkAKDaOKWjZTO4Dpe+PVNPtWvTZNPTIPe3r1bJHneOt1JLl8iqFuQqGJr9k4AhcXa+5niwtnZVyG2i2ZNzaGoZGQA8/JE41w4O8e7fef+0HMeg88nkjQbPsK4dJzOHNKTYoDN51MJhQRh9VjN6b3tq4Shj7hW9KTi89xuQnYlEIV3nyzk9Y3RNK4C5PINdnw9ECSoz1mPEep08t9EPSi5GKyfYSCGpIWa5olhe5z/lSnY2ZMzTWv45xQyh1SegzG7mQwS4gP2jc8iRXJc8iPYehkEWwgywNpPTYlSj9s21elAVgpEAEKXz573wVSVQrX9w2jcFkPwdTK8duppmGIbZNRfHofPJ5Fx6tA0S9aA4lVNr6VFaWHC6ZfQaotYO2zrfgDzcKKUha6+DbD0l0YRmCsSYnGh6RHEojjBWFSgnfopnWjzUSbuuY1q9pgM3e+MJfrtk0sgBdnKL7E0XbG/ebpTwHaQnp1RaM6UndRDt7b3ofvrIflMsFhXJhppQCFGoYysJpOIVagGdNb1/X/RdElC6h16E9mD5SEF6Y1KbIKMwqq5WDqvBglr/n8KAPZdMrtSC580pKwelyo4sYdgs01Fe8DJ91Z8mm0uzdB62RdTG6TcZIaYkQnAXyaRTUtVra+1QDFoyZRWdU4uZEEfOHKDhE89Lmipcs61pwsD7F6IyChc+T/Uj1Jc4UMpWmTqOMmsg38f8nNdJkLFtEMmONRWsCfkls0XxQtC7zyc2vY6PUoN723qOzNmYtLwHCcX4cUMwC3zF97B+fd0vvgHvuef9Um4Cfvv9H8RJ07lk8mLVj2fwBptwDgjx/ro7vigUzpIltnWPoevnCsXnvP1L8sjuALN+i4GjZ6iRqQY5UKI8pD/nKbaxt/tyYnF0DrjkdAOgFnOCyCmOG7+sTiOO2b60equnPM/LI2i9dGGQM9RBtgw2MmMcI9h50fOc+YgxHlrZh71yyalB24C0P16gvs4rB2UqoGhr3/7mMOO2v6ccb057E9IpPP3JLQFRbsoRuV1VOIEIP/HTP4fXvP6XOqmcDJ1LJteGmNjlzjYCXIe4cB7mq43ow3YZr1OUObu8PiVlST6NHJJqc7+BNHlWpkEEcOB00V+ybQOJs2yMiAq/4Y5RcivLAD0fnsExna6CNHUFnZPP9fDIKDutzIw3hs91TzVYy3lLsi8927bqPwDlBT8cI8bDw8zkB7LvfRxHjLTGarUSz7nuLx8CaBXkJJzQnnXI7VgLdUZ21GyGxQrRhyEv3NF6TrrX4Ln8CA7GR8CuxQ56go812CnZk47OJ5M7qpl9ic2Sw+j8t9M61eDVPFrG+xzj+neTtOAHhBNEs+aCJpy+u5MkKI8eC0tAuh4lwWFb6CKJWDkA2zUm2l/huzJIsNNaieKkrirMspMpwXWF8Ql2K5zOJ7ymW1OsXqXHW30KMUasD9c2XRbjCL0hVRl7b28Pe/syVSbe9HSYBMEfZQeWw65ze2tzkTZsp+lr/wn5DlH/g4wjQTPShoUPBtksmfpjpotu7K+rXk6OziWTP3DtGu697z4bFEMYcPstVyb9lOFTz2ZWOIrc+ZiH0j7devpsknr1vk5zMhXVyrf+qXaeG2j2DFX8tNdbptDUMZRtdlvMootL0jluuvCE9D/H0O3vnskjas0dI5v/KQtRKa8KAQlX3krLMSKux3xiDadTaCDe/GE1YH9/H3v7e9i7JN5nSifYhCCaXK94Mt5GnDCORxs1PK/7Te1t70XP/ZT39U8ovVdkYWMI2fHIzLj3E5+UfiIChYCD9bqV2okSnYY3b+tCVPvJ91YrrNypJ0993GPxym/7W2JrssUBABscmGVIiVf6warVaA1tXd9V3RIMc6e7+jixoSGzU6oqNwNyc4iE1eOYmGVbqNq+lHwOYACHo8x7rxmHh2uQnrXEwLgeE4wnG8WE0sZVIaBr4f10DmucWpOn8o3jqJeRNtf516gHAA4PDxHHEXE9gnm09mcwwiCMvH9lhf39faz29jDs5/KGvWwbRzvwMtopsa2+qpm80MJVPzHUl5Dj5EWBjNUq68aI1F7VirbCfk8my/s/ei+e/3UvweHh2oTS9YNDjAtPCN5Ab2DmZ7denEtNfrhe49BJuGsHB6KtfaCGo8SONbIOtWs8k/cdJom9xVsMAJ+HdlhD6s853FpUHAVM5kYT+aSynhw0J0CmsYINEqljNNuZBjIDORLLNvIgTAJ1p40xzVULg+sac/U+2+CO2flVO6Zy0bNA8k5HG9CcPP2c20i949lsSYM+ytw+kWwuUf8UB8YQgswbrwLCKoAGAANsbTqr/PeNqA/Iv8jlLrSr+gZyh1hwgiKXSghosBo1mrD27YSiz8SkEL/BA9euF2P7LOhcMnmTknOFOR3VCxSqucVaetmeeocLCV70CE+fVXnnoFy9msK/Omx9Dh3BC4tc/kB+IYQ6rCrISDHViEEhHeZA8heJgQCEVZD1z1HWPhMlLzRHWzPO4CK/ug7+t4euTcZWJMTZRNAqZFs7loyT/gmBMKzEvg6BkpBK0HwF0ED5Lwgzsp23lhrQhKJhdpfLFKGpo471ue/2Qgs3hC8wYXKfQO0EVAGQw5493TRMbhBQL5hXhJ0GF+Dkd7IJY9QrbqfEzhGmUHpw+dRe8xYEbDniPBXHKrswLUfiBEZypWsqDZIhsVSRhoQWIuUVY5SWhVMJlwWZEJidGZDmp+tyT+xXLffozgKp6j6xvV0dNe3cvqPMFARCGISp1YuOFfJ3E3alxrR2cwydci5+187ReT9oGmBkIqIYRmVfcA5/Bk60o9BNweTv/8hH8dLv+xfORw589Zd/GT7tkY9sOrQ4MUhQewiArhTT934/c2spYcv+zh5lngiDubhzWr773E2r+UFVayTV+EQwTzMFAtlxTQSMMqhDHBDUGRYjmKI54Px0YEolJZguHOTcrgRgdOfLxTHCg5CaqU14DEM66CHdksMRkQnDnq5UAyggedEHEVzJN8AhzzmX2229AEp9gqpPrM0yZ5eONSdojbGlprV/QenX3v0e/KvXvq5ifm03FEJJH129ft18HmdJNwWTf/z++/Gqn/254tlXfv7n4Qmf+qnQedcGSwJwgI0b9hTazFy/Mw2Y0mox6JT5YHE35ePD2uBTbprJp3geYPPnZh8SEHS3mK3jlrXro6XPxuRqrxuEpWTymE2Rh79XZOyVmQkpLWsApY0sYSVeemFamQUI4LR7LIUPjBDShpOVOx+PxLfAQL7yuNLWXVIkAwIZ86a2aAmDrtWWX7zvwx/GT1Rj8rzSTcHkTQokCyJGCM5WiiLRVesAAIiqM8r7VENiIiq87K1wGva41EqjZSK0p3y0XOmpzmmpdo8uXCQMqqFS+GEIGDik7aBa17S6TeGtY3hZWSZhgoaRAgrspqkWHAbv7FvZdFkYyCD7MFC+PGGV6zki3f4Chp/Gm2u7KZkkgnKyae/aNndpTtJO9b1Z6KZl8gIKVtpSNGIwmCkvUX1xEC5BUb2coO5cS1cSt2eljdfWKBuhe9fZ10cH/bT8h2j1MABMcuhDANJlYwCtAsaRszJkAEwYAtyusJiYm8XeVx43VJOYV7g6t0Wau/bsJHDcMTkBK1oBQaahKMCchmFIjkLnS4iYZ2o2ODFtR8/Q9knN0GV3GALwSEDpRKa9zoRuWiZ/9z334PL+PsY4AgzcceuteNwjH1F0UgClPRXJG52gcN6CCBhnsP0jT1taFeXAKIXK/FlvngoInzxAJcTvC4eWQywPwlwdK2PQVVVkVrbcUEoyHadOugjocgVBQkBkSuedu/ZiXz5hYVt9RpJXRC6XTpMpk5uJEIJM4w0E5iQUkjbPDMjldBdlU8F4l53fxUpUtEBuS0qtQMkD3lPG1PmuOXFbQJxXOuqtpv8AwJcBOADwTgB/kZk/lt4d8VbT7egl//xlxe8v/v2fg3/y4r8m86lptFNawx1jBCI7ry9BT0MxzRJ0oFfzqQ34Pqe1t1lcVC/K0GfbxG8XBDagKQwAh7wzjRgk+0ywGoJs8xzl3HXxvEOuAAYQBBkbo0oCKi6QHYODMnIKx7K9Ne2SBZFsmCu1pOwFpSHYcZAUApgIo3gGCwa2a4qS4DazxGQNw/OtteuQT3GdI9JFSerM2ERb9NONpqPeavo6AJ/BzJ8J4G0AXgoAdAK3mh6LBipGVMGkgeQ+aZseAkxfTOaEYc+VivnORH4/uQ/n/3pz6D7Npse8msJrCZwiLQQQDSCkPxqgy1blTNMoRyvxiIgx3QVPoFXAsDcgrIRZEQAMDBoYYQUMe7LCLAwC/cNKp7ryHwLLgQ36FxgY5FlYkdjWIYULDIQIBBYkwaNNYYKAMFC+GJHKGjLSdGdifk5mlt9aY5cnaP0gDGzdRwDcbxkuZOafxd3E6zeRTb6Ryblxqykzv5aZddnO6yHXIQEndavpUUj7Jovy/LxgvErr+CQ6DL6J5sL2nGn21wzrDkisGL1P1Tr0epSqdvcDWNsh6LlpeRpLobNMeSWG1ueJES1OsgOMOZVXhvy9YHKXv8FmZ6uXbAsUKlrr0m4CV1eqwnrnQ9Xmlnduw7m/m4nBgZOxyf8SgFem74tvNaXqwsOToDyLmyigtYvTvnpoR0CaWJ9q9bwkc6rtLb05LTuxuatdayQbFvSwBq3JUlvf0u54kih9yuxacGNdl8sBw0qPm7JCmpHPzghV4VGvYAvJxmYAGFP7eowPpO2W7nliflv3HbJQi3HMQihJJu+0Wy785klMNbJ2ct6NkgpnDBYh+vNCx2JyIvoWyHVIP6KPGsGaPcAzFx4ehX7+Lb+OP/Ut32657a1W+Kd/48V46O23yWAxriYgnYgSAQREZCme5ooju+NX2jQ3hbMJWmuYrHXV76c6LA1y6DFHeU94b0PMEAY0oX2LAZShdDWcGsXM4JDPYydO+RFB9wFoVYJLW5xqbHXQC1M5tSWR7skmOxhC60TWOXIqbEjPh2EFRrlwJAvfLDjlWcw2uguXD6wgi4fkRBT5ZV5BERxEVqe6f7/t5T+E33j3b0Fz+dgnP9nsh/NIR2ZyInoRxCH3XM4tsvhW05Omj33yk3jT295hv/f39nA4juLMqee3TfO5neaF0pna0bNM06CayXtz7PqOADD1DzRoOela5kUrn9osyAm4cAqdLe2qGA4KK7NY+u4OMmbk3YLmDkmMlaa5/PQcJ0OaHYNm5NM+1ccY1Z7VhfVt5N+QCYhcFyouQexp6Lff/T686e3vaL8853QkJiei5wP4JgB/hJkfcK9eDeBfE9F3A3g0TuBW0+OQHfSXNJOd+IoSQsugA4hHO5pItdK21HK01dC2Di9zy9O18krj2Dp91TM40mEL0/R7gmHOGUjGGZwWEVEzbb80eBJfootdznltPDOnM9ejHP3EMkE+MWkwbQf2b03mOkHWLIevf55GDSHBGZTiwQQVubPcbnI66q2mLwVwCcDrUsO+npm/mk/oVtOTIBXOQbf5jenoI0Rg5MZgZ8ipqOoRH2FLPjq8PtHQvbJUAkWf+U8ppcDG+paWehdb/Snweno9sqc5oWVowsc3aKtamYp118HNiXmkUAi51AkcxTQC52Oz/K01vlytGpizi8stoi2TaSIIE0LJ3xPy8P4E1eaWhlrmDaR3E9JRbzX9gZnwx7/V9ARojBGv/cVfxh233ioPGHZCigyqjM/NBkXAs5/2NDzyoQ81vKqTUJ4i59NB6mmy3ty6fvdUP/faq2Uu1PEdIi7SbPkDSuab7qhrxfcCSRxpGggOtqPgTALc1JYz92sPuQtfHqjRZ1zrNc5tVWh8KpnXmFofO8XOrm18XYiA+x64ip9+w5uLVXYf/vgnmu11M9C5PBnmRtL3fd3fwHOf9SynId0xYub5LW/f3DgX7j5b7/Mgrg5YqN5XEVMcGLcHKrdxArAz0bXclqZLuyVQ9M/n7RGGRxd1fca13qfGxrPZBk9MybAyRCCtW5edZ548YtA58Sw28nJXcdYN9t3VxgkjMiYnd9WxFwJEhLe9924872u/6Vhe+xtAN9fJMDeSIk83Pyjp4KnvRj/OYGBUJkPFN/VxSn6vt0Jghh5EwYUPqhYwGh+ALFftMKnWy8ro6jqxnVsORmLxtrMwd0ZOaXtpjPDHU4UQMl5yWjo7JdkugOScGcTAyYJrro1n4bZpea/yLw7tmLwmrgcuJpj4KPOzvSm1Io1iUr+kPEec/8jHJ6R5dmUQTApe2twOCWg6dZro19UcU1V6pXkis5EWrtmOspfAEFLFXz6fArY7W2ETWrL1E+40VRWMytDegLtotGPyDnkPsY7Olr19EpCuFio+XWUaIhKImRh8kmt6Jh7hFHdhvn56MM7Ux5fFEANlVqEGo2cfCAC3X8AgNzPUn0cEW4xTCAvkfODyET9lmIRv9YmZHsH7F6QMh+uIMZ2xTiBcPzycxL+ZacfkFU09tOi7zdGGt/759nnLd7UvC7uY2U72rB16tS2OVGyqGNm/nztp1tLgZAKoYyIb04WzSnb4FBEBZNMmxpiWyA4gDnZ7ar7vLEXhabupUFEk40+lKcK47+3+8O0lLURE+L9+9Mfx4z/9c5bc2h2FdRFox+RLiJLHWH+6QVRDySIatT3d/rtnYtvq2YOd6tWv3tcOMo8CYr3vmTEJV5QHhVnvmLMu0/RCCUCPZpoKPj8NJrHLW2hqpi3rN0UK0FxaJgh72x35tFpnwpg2J/Gmf+jej7UzvwC0Y/IWuVE+VS55gG2U9qUfzEcv5nsBHZ9ZO7bsYrWZ7RN6c2v5VxbBMZqbNvTp1t5ocQbKPyZYKg6cTulJZTnKuXGMcqox17P0BVAgBA52oIQtPa5rwS3HX7VboSUkiPLprih9FZ1rEi4c7Zi8Ik5bJIubQQX3ymd0TLsA0dmGE5I/Mo0CU5uewbtOOZTaTN/IWp+pc6pTu1wxEIaV17iV0y+FC6v+nHoP7se0/smOvffCRwWLLiu3D5kQs11ggWw7KiivVpg6+9imwmohQCQHY0S3P6G27Y9iVt1stGPyiowJnWOJ7GpefV5O8RSQsfaWV88Y7rzzCqHWA67+LcrVwU0N4zR7izjjXUMXaqMqY+Q56LIdNGWbzlrAFAWTad1tsUFqWKd+Qxm5qZHNqZjnB+WjcyCEn43wDsODw0P8i5/8j3JhRxKsb377OzfW6WamHZPX5LUpQRxXDh5OiNvPDXZXc9+bpns2eewL7/fESdhGAUWe/ns1/72J6rLPORt7Aot1zlwFJcqZAjONqJrW8r8VcnuHo2QyFX5UHrl9cLjG97/6P+IT9/stFxebdkxe0dS7XjKOrHZLjBid0e0VTKFs2p7eOVgOTI8JtLF/RIjZdPplB8DGOFrGeqVbyw/QczDq71qoUAh5AQxQCCOk+8blj0tNT1OhqMxewnN3k2jjjP2LTjsmr0ntQfiBmrQdI1/zm2Cn2aWm7N0ySosvmovT9wlLOTu7q8VTGL1TrZ4ym2P8FrNy4zd1YL84H/2hCsKYE7PFvVcTojBnFD04FKHfqWhvZXCF2SiYu2BUb67oOW+E5MgT55rVTdN6kNGOySv6yMc/jrs/9GEbDJf29nHXnXfmy0fVd2X+uCnTNhnVBmIKk74XhkDl+5Jo01HpzYkismPDMlo+ZbQsn/oaKgZFtsn9C1ZTxhjOB8gnyLC9VuEl78URhrywT23+iVQUp94HPvJRN9WV6xwo4OF33oFL+/umudXON+WdtP79V6/ho/d9HHKJIXD/1WuzC34uIu02qFQ0VHD0D/zuz8APfutL5Qdzvr2D2S7z41g+j3p+OxwbpB/m0EqOLz+8PavVXmIgHxxpz7ydqlzTkhQwNk+v2b8oyXipZDwNVt+GqrMPvbJrGSd2P+f2IOcd5ygOwA989F584dd/U3f12Q988zfgc5/5u8v2qc0AIvzof/mv+Nb/++VF3PUNuKroDGi3QWUp1XdFj3E0uF5OQUEcPczp5FPkwS441azM2oHk53m9I4w0gP+obNfSe1/a6vqQW1Nbio7lgjg3914G6zoELRm3hTShAD2bLjN2+al2dm021NOBIJJz+UBACDgcxy5D+lkBD/8paXVOi3JijBeVqRfTjsmXEOV70AoES85GpQxXSZ97pUpt7QwX35N6mQsIHah80IhfTokVAQqBMLcSr2dueAEzgbweUmv4lsCohRpRusO8bJ/pCrtGfb3J4B0G6Xd54uuDl3ZMvoHMeq3sU/X2UkicrBczBHW6J6ZI4Xv7r405KiYRMDD1WttW0fSuuSBFhYFfi9ugOYFT5K0Mt9C554mr77V54nlTHGaS1rAKs3P/GoGcs0SWH9w81xedFe2YfAOptjHgGRIsZAbrdb/gdJsoQBwQ9ASajmazga12aUfrTbS786bXq7v6XnmN3E67p8VbC3H883pPvX+nbFfXqDW1RkRy4WFqY1vcsoHDv+eVP4Effs3rqjJO2+B9H/zwfEIPAtox+QLKg7fGg1y88wyg1wDLuxKK+qOLSL3SQPZoO6dU7SQzD7aZBhqHMUHQhrA5haGcu5kDOe/Jwh3Nlko3nFSDrazZCcguADmfAbl4mYmzE9JluFF9C73pbW9fFnBHOybfSNWgY/eNAXOGFcGYERBgh6Iwg6u7wHSpqS05BWRtO1HerAHkc+l8WVI8OyqKW1fhqLTw3vg8A1ArvemGmcx9gTL3yTFOaWtoMjHEvvbSIV1DVDMxqmzJCxU2QUHJAbejk6GNTUlELyeiDxLRrzXefT0RMRE93D17KRG9g4h+k4i+8KQLfObUgLn+b/KsCIy0EYNNudUOuFqjtlaQtajOe1rOTnW8wKj/qjrmfJLTQaWJXiLoTAVDAYTiGSPtlIMimsJysbrXQuY8TO1eFFqiyX8QwD8D8EP+IRE9DsAXAPht98xfePhoAP+FiJ7KN+hY5pOgj33yfvz8W0r59llPfQqu7O83GcmGplv5RhVjF9NHfknmhjXk3XczDNGdEluQTzHlBSQnl64ka+fb9c679JgZb3zb23FwcJiYX+8zzxD/o5+4L+1N39FxadFiGCJ6AoD/wOnq4vTsxwH8bQA/CeDZzPxhkmuLwczfmcK8BsC3M/P/tyH9m0ZsByK89p/+Izz5sY8WN3pFur7dPQBQOdcqB1pN3ove8p57RhK4znLSCpxCNu9etv0n00wNmiAEIJ0hi8nz3pr12klXIgPgcD3i8/6Pv473fWjnFDtBOtnFMET05QDex8y/UmmExRce3syUHUzFwzb1NGbtaUdel14GKzd0tBaUMOAW46hTL5dVfW6F36s5Fb5Z6/vvfndX5ZK0sEW5NdObR6ZfCNqayYnoFgDfAuB5rdeNZ80epVO41fTMyFa/mUGNHpe358bLM1qo8XwSVqeYqFxBV07VuTfq0ONyPtrsdyq98fZ4oo0BheiTOjXKy2qmJGdc/p6LNmMp7OgU6Cia/MkAnghAtfhjAbyRiJ6DLS485BO+1fRsqS5uG7bOUW8hyiScBCq1aZ1jYvQ2w9HkWcq4EMmWng9ujG+FNkGjv5uOQkop6tZQuCOl0LRydnSKtDWTM/OvAniE/iai9yDb5OfqwsPTIio0p01qb1pgVmpgoAl7Tqp83m5unhe3IA3/qeWtF+689wMfxP/2nf+wvbOrlCOFU/IDH713YW12dFw60oWHzNy8C43P0YWHp03Oh5agr9qc7LzOjW2oPq6FqsI01pUvYs4eOiAqnH3NihSPSodZ4Ukv/IeMg/Uh3n73+3ZTXueYjnrhoX//hOr3ubjw8KwoO8actvSrDwp7vWGUtngjMaWnOSZqMTV5KcTuwAo75IKaaKI1919jkMLxeLNZWg9C2q14OwLpfWl+/Xab2H32vOztGEDDCdeK3tHErTl5oFxz3k2/Xq0CALTsHLgdnT/aMfmWxMx49c/+PO566J3NhR/MjEt7e/gTn/+5WA1DpZSzn3vKMOT+VdivsVqOrQnIT6/UENb5+TyNVsTnOmZOlpKG1g22TFlY/eJbfxP/4z22/gkf+tjHZxfj7OjG047JtyQG8M9+7N/Phrnz9tvwpZ/7+7G3WjU83OUimBISl6FsbToAXQ5bp5MSy9/Ns62wG8X8uTjVq7l2lyoV8UvxwgD+08//In7w/3nNbP13dL5otw3glGkyhdUIcyOdVkX53NTYji4O7TT5KZJfrbbJDu4tb+29qwLCb3utBUuTkXVZbCutKs6SE2F3dH5px+SnTFtNgXXi1Mw78QUwT5izOY3myM9Z95at+mf1rrkd3Tx0Xk5r/RCA+wGctx0LD8euTJvovJUHeHCW6dOY+a7Wi3PB5ABARL/c20Vzo2hXps103soD7MpU087xtqMdXXDaMfmOdnTB6Twx+ctudAEatCvTZjpv5QF2ZSro3NjkO9rRjk6HzpMm39GOdnQKdMOZnIieT3Ky6zuI6CU3qAyPI6KfJqK3EtGvE9GL0/NvJ6L3EdGb098Xn3G53kNEv5ry/uX07GFE9Doienv6fOgZludpri3eTESfIKK/ftbtRI0ThOfahU75BOFOef4BEf0PInoLEf07IrozPX8CEV11bfV9J12eCemhAjfiD8AA4J0AngRgH8CvAHjGDSjHowA8K32/HcDbADwDwLcD+Pob2D7vAfDw6tnfB/CS9P0lAP7eDey79wP4tLNuJwB/GMCzAPzapnZJ/fgrAC5BTjR6J4DhDMrzPACr9P3vufI8wYc7i78brcmfA+AdzPwuZj4A8KMAXnDWhWDme5j5jen7fQDeivN7AOULALwifX8FgK+4QeV4LoB3MvNvnXXGzPyzAD5aPe61ywsA/CgzX2fmdwN4B2TcnWp5mPm1zLxOP18POQrthtCNZvLHAHiv+33DT3clOX76swH8Qnr0VxPkevlZQuNEDOC1RPQGkoMvAeCRzHwPIMIJ7iiuM6YXAvg37veNbCeg3y7nYYz9JQD/yf1+IhG9iYj+GxF97mlnfqOZvLUS+oa5+4noNgA/AeCvM/MnAPxzyMGVnwXgHgD/6IyL9AeZ+VkAvgjA1xDRHz7j/JtERPsAvhzAj6VHN7qd5uiGjjEi+hbIUWg/kh7dA+DxzPzZAP4G5EzEh5xmGW40ky8+3fW0iYj2IAz+I8z8KgBg5g8w88hyo/2/wAnDvE3EzL+TPj8I4N+l/D9ARI9KZX4UgA+eZZkSfRGANzLzB1L5bmg7Jeq1yw0bY0T0IgBfCuDPcjLIk9nwkfT9DRAfwVNPsxw3msl/CcBTiOiJSTu8EMCrz7oQJFusfgDAW5n5u93zR7lgfxzA5D64UyzTrUR0u36HOHJ+DdI+L0rBXgS5weas6c/AQfUb2U6Oeu3yagAvJKJLRPREnNEJwkT0fADfBODLmfkB9/wuIhrS9yel8rzrVAtzll6+jmfyiyHe7HcC+JYbVIY/BIFwbwHw5vT3xQD+FYBfTc9fDeBRZ1imJ0G8wr8C4Ne1bQB8CoCfAvD29PmwM26rWwB8BMAd7tmZthNEwNwD4BCiqb9qrl0gl4G8E8BvAviiMyrPOyC+AB1P35fC/snUn78C4I0Avuy0+2y34m1HO7rgdKPh+o52tKNTph2T72hHF5x2TL6jHV1w2jH5jnZ0wWnH5Dva0QWnHZPvaEcXnHZMvqMdXXDaMfmOdnTB6f8HNrB67caCWSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(parasitized_cell);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After manipulation using ImageDataGenerator's random transformation,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image_gen.random_transform(parasitized_cell));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name='augmentation'>Image Data Augmentation</a>\n",
    "\n",
    "Now we are not limited by the size of the existing images. Using ImageDataGenerator, we can randomized the images and can expand our datasets.\n",
    "\n",
    "In convoluational neural networks, it is important to have a large dataset to cover images with different sizes, shapes, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name='manipulateddirectory'>Generating many manipulated images from a directory</a>\n",
    "\n",
    "\n",
    "In order to use `.flow_from_directory`, you must organize the images in **sub-directories**. This is an absolute requirement, otherwise the method won't work. The directories should only contain images of one class, so one folder per class of images.\n",
    "\n",
    "Structure Needed:\n",
    "\n",
    "* Image Data Folder\n",
    "    * Class 1\n",
    "        * 0.jpg\n",
    "        * 1.jpg\n",
    "        * ...\n",
    "    * Class 2\n",
    "        * 0.jpg\n",
    "        * 1.jpg\n",
    "        * ...\n",
    "    * ...\n",
    "    * Class n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Setting up directory to flow images from,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24958 images belonging to 2 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.preprocessing.image.DirectoryIterator at 0xb947b952b0>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory(train_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2600 images belonging to 2 classes.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.preprocessing.image.DirectoryIterator at 0xb947c166d0>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_gen.flow_from_directory(test_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name='modelcreation'>Creating the Model</a>\n",
    "\n",
    "https://stats.stackexchange.com/questions/148139/rules-for-selecting-convolutional-neural-network-hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout\n",
    "from tensorflow.keras. callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "# Convoluntional Layers\n",
    "model.add(Conv2D(filters=32, \n",
    "                 kernel_size=(3,3), \n",
    "                 input_shape=image_shape, #image_shape = (130, 130, 3)\n",
    "                 activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(filters=64, \n",
    "                 kernel_size=(3,3), \n",
    "                 input_shape=image_shape,\n",
    "                 activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Conv2D(filters=64, \n",
    "                 kernel_size=(3,3), \n",
    "                 input_shape=image_shape, \n",
    "                 activation='relu'))\n",
    "model.add(MaxPool2D(pool_size=(2,2)))\n",
    "\n",
    "# flatten out the layers\n",
    "model.add(Flatten())\n",
    "\n",
    "# Dense layer\n",
    "model.add(Dense(128, activation='relu'))\n",
    "\n",
    "# Dropout layer\n",
    "# Dropouts help reduce overfitting by randomly turning neurons off during training.\n",
    "# Here we say randomly turn off 50% of neurons.\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "# Output lyaer\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# compile the model\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam',\n",
    "                         metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_6 (Conv2D)            (None, 128, 128, 32)      896       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 64, 64, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 62, 62, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 31, 31, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 29, 29, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 14, 14, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 12544)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               1605760   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 1,662,209\n",
      "Trainable params: 1,662,209\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Early Stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping(monitor='val_loss', patience=2, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Batch Size\n",
    "+ typical to use power of 2 as batch size.\n",
    "+ depends on your hardward, choose a larger size.\n",
    "+ the smaller the batch size, the longer it takes to train as you are passing multiple small sizes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16 # 16 images at a time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name='training'>Training the Model</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training and Test Image Generator\n",
    "+ for the training, we want to shuffle the images\n",
    "+ however for the test image we don't want to shuffle the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 24958 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_image_gen = image_gen.flow_from_directory(train_path, \n",
    "                                                                               target_size=image_shape[:2], #only care about width and height of image\n",
    "                                                                               color_mode='rgb',\n",
    "                                                                               batch_size=batch_size,\n",
    "                                                                               class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2600 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_image_gen = image_gen.flow_from_directory(test_path,\n",
    "                                                                              target_size=image_shape[:2],\n",
    "                                                                              color_mode='rgb',\n",
    "                                                                              batch_size=batch_size,\n",
    "                                                                              class_mode='binary',\n",
    "                                                                              shuffle=False) # shuffle false"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'parasitized': 0, 'uninfected': 1}"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can check the classes\n",
    "train_image_gen.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'parasitized': 0, 'uninfected': 1}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_gen.class_indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Actual Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = model.fit(train_image_gen, \n",
    "                            validation_data=test_image_gen,\n",
    "                            callbacks=[early_stopping],\n",
    "                            epochs=20,\n",
    "                            verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the pre-trained model (if we skip the actual training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('models/malaria_detector.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name='evaluation'>Evaluating the Model</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have skipped actual training, we can run model.history. Otherwise, history visualization should be here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Metrics and Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['loss', 'accuracy']"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.metrics_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "163/163 [==============================] - 35s 215ms/step - loss: 0.1403 - accuracy: 0.9531\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1402682214975357, 0.9530768990516663]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(test_image_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "\n",
    "https://datascience.stackexchange.com/questions/13894/how-to-get-predictions-with-predict-generator-on-streaming-test-data-in-keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction Probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_probabilities = model.predict(test_image_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.4360905e-02],\n",
       "       [1.1797619e-04],\n",
       "       [2.5177896e-03],\n",
       "       ...,\n",
       "       [9.7454494e-01],\n",
       "       [9.9452138e-01],\n",
       "       [8.2132727e-01]], dtype=float32)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_probabilities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Predictions based on prediction probabilities,\n",
    "\n",
    "`IMPORTANT: In here we want to consider accuracy-precision-recall trade off and adjust the number.`\n",
    "\n",
    "+ **Use Case: We can use ML model to make preliniary predictions and alert the doctor if accurcy is greater than 80% , 70%, etc.**\n",
    "\n",
    "+ **Like which one is more important for our real life use case: Type I error, Type II error , etc**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pred_probabilities > 0.5 # can change this number based on our decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2600"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [False],\n",
       "       [False],\n",
       "       ...,\n",
       "       [ True],\n",
       "       [ True],\n",
       "       [ True]])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Report and Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_gen.classes # can get the acutal classes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.94      0.95      1300\n",
      "           1       0.94      0.96      0.95      1300\n",
      "\n",
      "    accuracy                           0.95      2600\n",
      "   macro avg       0.95      0.95      0.95      2600\n",
      "weighted avg       0.95      0.95      0.95      2600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_image_gen.classes, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1219,   81],\n",
       "       [  48, 1252]], dtype=int64)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(test_image_gen.classes, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(confusion_matrix(test_image_gen.classes, predictions), annot=True, fmt='.0f', annot_kws={\"size\": 15});\n",
    "\n",
    "#how to customize seaborn heatmap annot format\n",
    "#https://stackoverflow.com/questions/54506626/how-to-understand-seaborns-heatmap-annotation-format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a name='predictingsingleimage'>Predicting on an Image</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\Phone Thiri Yadana\\\\Desktop\\\\Dojo - Data Science and Machine Learning\\\\Complete Tensorflow 2 and Keras Deep Learning Bootcamp - JP\\\\04-CNNs\\\\cell_images\\\\\\\\train\\\\/parasitized/C100P61ThinF_IMG_20150918_144104_cell_162.png'"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "para_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Resize the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resize it to desired shape\n",
    "singel_image = image.load_img(para_cell, target_size=image_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=130x130 at 0xB94A7FEAC0>"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "singel_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting to Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "singel_image_array = image.img_to_array(singel_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]]], dtype=float32)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "singel_image_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130, 130, 3)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "singel_image_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing Shape Dimensions \n",
    "As our model expects the image to be in (1, 130, 130, 3) \n",
    "+ batch_size : even if a singel image, it needs to know how many\n",
    "+ width\n",
    "+ height\n",
    "+ color channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "singel_image_array = np.expand_dims(singel_image_array, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 130, 130, 3)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "singel_image_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'parasitized': 0, 'uninfected': 1}"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_gen.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.]], dtype=float32)"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(singel_image_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we see our model predicted cell as `parasitized: 0`"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv-datascience",
   "language": "python",
   "name": "venv-datascience"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}