CV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import cv2\n",
    "from scipy.misc import imread\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "import xml.etree.ElementTree as ET\n",
    "import re\n",
    "import numpy as np\n",
    "from skimage.feature import hog\n",
    "from skimage.color import rgb2gray\n",
    "from skimage.io import imsave\n",
    "import random\n",
    "#from skimage.transform import rotate\n",
    "from scipy.ndimage.interpolation import rotate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0011.tif\n",
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0023.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/98639498.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56047636.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/69526769.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84908690.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/69874753.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/65281723.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/67963734.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/38777183.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83435188.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/11432084.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9385606.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/26258294.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/82562188.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0033.tif\n",
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0052.tif\n",
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0065.tif\n",
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0074.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/85210780.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68731509.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34759312.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52140750.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/37409112.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0075.tif\n",
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0077.tif\n",
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0079.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75151595.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/65656259.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56468120.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/4676931.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78759514.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/24164399.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/39903916.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/43303123.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64214866.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/47862337.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/19233280.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/27794706.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/42708321.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78164514.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/45376074.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12585717.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44207410.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/67657817.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83349110.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\Anthopleuramariscali_MEX20@\\filament\\image0080.tif\n",
      "samples\\AnthopleuraMEX1@\\filament\\image.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/8675573.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84735140.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33150163.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/2231676.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9083535.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/4576766.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/76858823.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/63606202.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49992193.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/27795154.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33971484.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/39955416.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/29907395.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/28648926.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73538195.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56533718.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraMEX1@\\filament\\image0014.tif\n",
      "samples\\AnthopleuraMEX1@\\filament\\image0028.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/41073259.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/31567148.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/92881154.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/61097795.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/186047.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/31171199.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/90296706.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56299408.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/98855787.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83485196.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48180155.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraMEX1@\\filament\\image0041.tif\n",
      "samples\\AnthopleuraMEX1@\\filament\\image0048.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/8999365.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64950356.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52098038.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/3534751.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18106839.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99735727.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44308041.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33066666.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75878177.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46326066.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraMEX1@\\filament\\image0064.tif\n",
      "samples\\AnthopleuraMEX3@\\filament\\image0010.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/71855489.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/14718066.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/36072836.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73476405.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/87271244.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/57536213.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33235737.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/17819846.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraMEX3@\\filament\\image0012.tif\n",
      "samples\\AnthopleuraMEX3@\\filament\\image0013.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/43479765.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/81114562.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/6140106.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18041982.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/27037183.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/93321186.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/25301192.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/28224009.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66696488.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraMEX3@\\filament\\image0018.tif\n",
      "samples\\AnthopleuraMEX3@\\filament\\image0022.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/89544433.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83050007.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9421848.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54445952.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33344827.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/19980860.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83419973.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/23315606.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/14127994.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68912041.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/43589477.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/59460004.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/8076355.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/67842247.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/63773650.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/42067296.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/24006170.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30963743.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54370990.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/25244456.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30951386.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/31210067.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/47691133.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/91914469.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46308586.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/65617688.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/37123994.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/82639356.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraMEX3@\\filament\\image0026.tif\n",
      "samples\\AnthopleuraMEX3@\\filament\\image0031.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/19182135.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84303056.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/98053447.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7580971.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9547503.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52779974.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/71781936.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9072068.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/55020847.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/86180279.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/96371903.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66593643.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18825152.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/32678846.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72635129.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/25434279.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72601109.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0001.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0003.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/60397948.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/39896460.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49115280.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78417491.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78866633.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54887512.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12630836.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/15127410.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30607131.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/79173334.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46146034.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84242298.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49454130.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0004.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/26458638.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/24191875.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/5122278.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68321692.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54524216.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/21803862.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/22442811.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46503586.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83911070.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64994498.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78011643.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/5606564.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33999035.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/71592116.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48888621.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0005.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0010.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/93002303.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30938667.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12208484.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/86153854.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9312843.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/19279619.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/58743200.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/209120.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/89661661.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99326358.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/14184579.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/65426216.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0015.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0016.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99554131.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/38556423.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30821561.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34607620.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/3927354.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/22512251.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/10574644.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0017.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0023.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/27359693.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/10237405.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/5826201.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/82797172.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/53949105.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/31646542.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/86941861.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/10887135.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/29388992.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/82577217.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0024.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0025.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0031.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/2214448.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/86307412.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/3387510.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52168422.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/57525916.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/53736426.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66109547.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/19244119.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12441452.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12260446.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0037.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0040.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/53073296.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66329460.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66414710.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/23369712.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72799663.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/16924602.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/815382.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74535134.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/45751131.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/57119638.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49400726.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7038238.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84470809.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/55544940.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/3719556.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72539493.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/13498661.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/63984450.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52254188.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/42959735.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75859844.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/19291589.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0042.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0045.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/47845416.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/50745763.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68091384.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48064570.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74917517.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/87852494.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/38696962.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/61175662.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/62722679.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/77638872.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/76990378.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/43688080.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0055.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0057.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/32098627.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/45402863.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52037749.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/8202185.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/24939244.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78327068.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/21215090.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12640580.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7046966.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/41574274.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/22773033.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/21094829.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/80782198.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56691864.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/55452856.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66075143.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/98116835.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/16259115.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12155925.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33840634.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/63233816.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/6078728.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64523527.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/42914499.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/4915439.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0058.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0060.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/85195471.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7481953.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/21075402.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/35598483.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7876135.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44870851.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/40082987.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74674385.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0063.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56877087.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/29272056.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/71495015.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34997900.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/47525840.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/6578653.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34721440.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72290083.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/24513193.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/42068475.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0067.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0074.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9341662.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/65106595.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/15035472.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44334461.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/60436786.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/98234484.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/98381983.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/21861860.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84040480.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34839895.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78168034.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/95238358.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0076.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0080.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68468006.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/41632630.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12073377.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44245935.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46169842.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/32636230.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75722003.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/91345611.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/20130654.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7801913.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/43591307.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/51441150.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9315494.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/94340289.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73964105.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49\\filament\\image0081.tif\n",
      "samples\\AnthopleuraPAN49\\filament\\image0082.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/28690432.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75110335.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/3922217.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/16314686.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/51602237.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/1540351.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/93442606.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/31259168.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/38664219.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/8336268.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/45705918.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/36044653.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30693899.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/88897632.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/95396860.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/57410233.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/96665793.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0001.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/81185677.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48325544.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99000475.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/85518177.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/81849199.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9387937.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44290494.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99185003.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/77688250.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73004457.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33975528.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/26209757.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/32563949.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0003.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0004.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74032890.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7738804.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/27245511.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66022335.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75189197.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/36182166.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/29916016.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/70455226.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/27579045.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/41580496.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49171771.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74975369.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9955580.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48842426.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75225876.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/80573197.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74289314.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49297308.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12226049.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83185727.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/23350968.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/2587380.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/11301181.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83685550.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83719502.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/25118376.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78036477.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0005.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0010.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/19485629.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73410304.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/2233115.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72291297.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/19480643.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78344484.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/80398427.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/63873931.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/95801609.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46981511.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/4672959.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84193291.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0015.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0016.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46429469.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/76790126.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/50125164.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/10028350.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44429657.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52534409.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/86751503.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/93629747.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/81574315.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66982079.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0017.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0023.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/58787690.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/90982637.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/82726169.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/69432550.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/89913581.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/8326784.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18524892.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/17861435.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/97179629.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0024.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0025.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0031.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/22298693.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/50258072.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/2678107.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72065616.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/29966170.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54197984.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/25021212.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/60366129.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/62783350.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/41022132.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/23708285.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/25967311.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0037.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0040.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34553652.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/89858466.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30264201.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7724866.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/28598754.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/82460077.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/37511475.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/8442945.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/20272109.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/4574619.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/20090157.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/94523091.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/6762122.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44318496.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/50211515.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/67607717.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52066723.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/53575501.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/77470673.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0042.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0045.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/22607990.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/40975164.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/264699.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/59609794.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/58181205.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/35488735.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/21828396.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74856000.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73506023.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7912622.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99342220.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0055.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/51830474.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/32217055.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/35569278.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/51474136.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56848787.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44145211.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34118577.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72717852.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54474449.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0057.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0058.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/81375351.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/65123206.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/61115391.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30631391.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/26379994.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/95261178.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/40251871.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/90289702.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/87277960.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/67692056.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34170397.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/41450282.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0060.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0063.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/50154458.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/30619217.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18754090.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/17909442.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18970968.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48851591.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/71182856.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/15695701.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/45649064.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/6863041.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/22488902.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75423667.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/45741932.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84566001.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/59880493.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/11411336.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/65816560.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66559305.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/94642325.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/62143613.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99550898.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/36624143.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/92201327.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0067.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0074.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52990717.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78972122.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73050571.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54948269.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/17066497.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99205152.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/67904232.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34839288.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/97733624.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/76567239.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48234627.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0076.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0080.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/53127877.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/17111691.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74928305.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/80922667.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/41079538.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/32663795.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68659844.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/43473828.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/1061013.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/51029631.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73589245.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/53196983.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/53885907.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49!\\filament\\image0081.tif\n",
      "samples\\AnthopleuraPAN49!\\filament\\image0082.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/6914866.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/16660986.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7780161.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66480433.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/16581424.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/31902056.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44215632.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/88112172.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74694320.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/88315881.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0004.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0005.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84463079.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66445288.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/8294152.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/38150963.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/87637696.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/47573052.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78764957.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46008943.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/10550574.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/22972964.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/77029.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/32331407.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0009.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0011.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54720127.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/11530503.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/47937461.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/92384285.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/86415661.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/11681375.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/2343922.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/4225991.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12058291.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/78361827.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18633459.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66361215.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/98107988.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68119081.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/57948995.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/92773048.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48507278.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/10800178.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49237632.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64176110.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/52300745.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/60223756.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18687090.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/62266180.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0018.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0021.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73033831.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18689728.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34573115.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/23782126.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/90636069.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/16126099.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/7300915.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74814908.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/39515088.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/32611357.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/85784499.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/28307087.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56060048.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/20844151.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/89185240.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/24452329.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0024.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0033.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/95293810.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/83368478.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/73971776.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/26930553.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/10017000.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/50641780.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12967075.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64944738.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/79825817.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/88611969.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0040.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0041.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/25203892.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64321252.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/27675051.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75831387.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/20602444.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46621426.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/40751142.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/57195977.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/28064619.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/17918900.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/42362506.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64619400.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56069721.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46913498.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/98048750.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/41807359.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/82520584.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/34895030.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/5243695.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0064.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0073.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0077.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/60888405.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/3346615.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/59695721.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/64866538.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/79686553.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/51217395.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/31767881.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54877023.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/40377146.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/12856133.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/40504872.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/76501992.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/24687656.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56684116.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/29580727.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0089.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0090.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74005089.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/45052844.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66653493.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/38093500.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/48393127.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/95806518.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/27065747.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/70135927.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49186293.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/74722053.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9513351.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/61257908.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/65208056.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/61195051.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/14291214.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/92103412.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/15405216.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/11249634.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/50050735.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/62370110.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/62541629.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44260576.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/66453501.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0091.tif\n",
      "samples\\AnthopleuraPAN49@\\filament\\image0092.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/47566137.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/42892727.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/55413949.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/82980716.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/33906868.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44521737.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/39017305.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/55386105.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/56226198.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/99183082.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/49458714.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18216989.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/63499677.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/88710725.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/53485472.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49@\\filament\\image0093.tif\n",
      "samples\\AnthopleuraPAN49_\\filament\\image.tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/46855176.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/93833521.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/44326037.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/71948854.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/39801820.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72823138.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/18922979.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/31698344.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/40564777.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/21956146.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: cropped/0_samples_AnthopleuraPAN49__filament_image0001.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: cropped/90_samples_AnthopleuraPAN49__filament_image0001.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: cropped/180_samples_AnthopleuraPAN49__filament_image0001.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: cropped/270_samples_AnthopleuraPAN49__filament_image0001.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68218963.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/57626126.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/84145499.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/68939717.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/13878550.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/77381415.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/10280752.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/81983894.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/79429927.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/81156597.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "samples\\AnthopleuraPAN49_\\filament\\image0001.tif\n",
      "samples\\AnthopleuraPAN49_\\filament\\image0002.tif\n",
      "samples\\AnthopleuraPAN49_\\filament\\image0006 (2).tif\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54591192.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/75592705.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/28452518.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/24665811.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/9115351.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/72006127.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/5379038.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/85093138.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/54015660.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n",
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\io\\_io.py:132: UserWarning: negative_samples/86167870.tif is a low contrast image\n",
      "  warn('%s is a low contrast image' % fname)\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'samples\\\\AnthopleuraPAN49_\\\\filament\\\\image0006 (2).tif.lmd'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-97-96d7bea4155f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m     \u001b[0mpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetPointsFromLMD\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'.lmd'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpoints\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m         \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-97-96d7bea4155f>\u001b[0m in \u001b[0;36mgetPointsFromLMD\u001b[1;34m(filename)\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgetPointsFromLMD\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m     \u001b[0mtree\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mET\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m     \u001b[0mpoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetroot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Jeremy\\Anaconda3\\lib\\xml\\etree\\ElementTree.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(source, parser)\u001b[0m\n\u001b[0;32m   1193\u001b[0m     \"\"\"\n\u001b[0;32m   1194\u001b[0m     \u001b[0mtree\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mElementTree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1195\u001b[1;33m     \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparser\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1196\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mtree\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1197\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Jeremy\\Anaconda3\\lib\\xml\\etree\\ElementTree.py\u001b[0m in \u001b[0;36mparse\u001b[1;34m(self, source, parser)\u001b[0m\n\u001b[0;32m    583\u001b[0m         \u001b[0mclose_source\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    584\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"read\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 585\u001b[1;33m             \u001b[0msource\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    586\u001b[0m             \u001b[0mclose_source\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'samples\\\\AnthopleuraPAN49_\\\\filament\\\\image0006 (2).tif.lmd'"
     ]
    }
   ],
   "source": [
    "def convertX(p, metersPerPixel):\n",
    "    return p*metersPerPixel*10000+680\n",
    "\n",
    "def convertY(p, metersPerPixel):\n",
    "    return p*metersPerPixel*10000+512\n",
    "\n",
    "def getCoordsFromPoint(el):\n",
    "        x = None\n",
    "        y = None\n",
    "        for e in el:\n",
    "            if e.tag == \"PointIndex\" and e.text == '2':\n",
    "                return None\n",
    "            if e.tag == \"PositionX\":\n",
    "                x = e.text\n",
    "            if e.tag == \"PositionY\":\n",
    "                y = e.text\n",
    "\n",
    "        return [x,y]\n",
    "\n",
    "    \n",
    "def getPointsFromLMD(filename):\n",
    "    tree = ET.parse(filename)\n",
    "    points = []\n",
    "    for c in tree.getroot():\n",
    "        for c2 in c:\n",
    "            if c2.tag == \"LMMeasurementItemPoints\":\n",
    "                coords = getCoordsFromPoint(c2)\n",
    "                if coords:\n",
    "                    points.append([float(i) for i in coords])\n",
    "                    \n",
    "    return points\n",
    "\n",
    "def getCalibration(filename):\n",
    "    with open(filename, 'r') as f:\n",
    "        metersPerPixel = float(re.match(r'.*<MetresPerPixel>(.*)</MetresPerPixel>.*', f.read(), flags=re.DOTALL).groups()[0])\n",
    "    return metersPerPixel\n",
    "\n",
    "# https://stackoverflow.com/questions/25349178/calculating-percentage-of-bounding-box-overlap-for-image-detector-evaluation/26386665\n",
    "def get_iou(bb1, bb2):\n",
    "    \"\"\"\n",
    "    Calculate the Intersection over Union (IoU) of two bounding boxes.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    bb1 : dict\n",
    "        Keys: {'x1', 'x2', 'y1', 'y2'}\n",
    "        The (x1, y1) position is at the top left corner,\n",
    "        the (x2, y2) position is at the bottom right corner\n",
    "    bb2 : dict\n",
    "        Keys: {'x1', 'x2', 'y1', 'y2'}\n",
    "        The (x, y) position is at the top left corner,\n",
    "        the (x2, y2) position is at the bottom right corner\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        in [0, 1]\n",
    "    \"\"\"\n",
    "    assert bb1['x1'] < bb1['x2']\n",
    "    assert bb1['y1'] < bb1['y2']\n",
    "    assert bb2['x1'] < bb2['x2']\n",
    "    assert bb2['y1'] < bb2['y2']\n",
    "\n",
    "    # determine the coordinates of the intersection rectangle\n",
    "    x_left = max(bb1['x1'], bb2['x1'])\n",
    "    y_top = max(bb1['y1'], bb2['y1'])\n",
    "    x_right = min(bb1['x2'], bb2['x2'])\n",
    "    y_bottom = min(bb1['y2'], bb2['y2'])\n",
    "\n",
    "    if x_right < x_left or y_bottom < y_top:\n",
    "        return 0.0\n",
    "\n",
    "    # The intersection of two axis-aligned bounding boxes is always an\n",
    "    # axis-aligned bounding box\n",
    "    intersection_area = (x_right - x_left) * (y_bottom - y_top)\n",
    "\n",
    "    # compute the area of both AABBs\n",
    "    bb1_area = (bb1['x2'] - bb1['x1']) * (bb1['y2'] - bb1['y1'])\n",
    "    bb2_area = (bb2['x2'] - bb2['x1']) * (bb2['y2'] - bb2['y1'])\n",
    "\n",
    "    # compute the intersection over union by taking the intersection\n",
    "    # area and dividing it by the sum of prediction + ground-truth\n",
    "    # areas - the interesection area\n",
    "    iou = intersection_area / float(bb1_area + bb2_area - intersection_area)\n",
    "    assert iou >= 0.0\n",
    "    assert iou <= 1.0\n",
    "    return iou\n",
    "\n",
    "def randomSample(im, x_extent, y_extent):\n",
    "    x = x_extent[0]\n",
    "    w = x_extent[1] - x_extent[0]\n",
    "    y = y_extent[0]\n",
    "    h = y_extent[1] - y_extent[0]\n",
    "    \n",
    "    while get_iou(\n",
    "            {'x1': x_extent[0], 'x2': x_extent[1], 'y1': y_extent[0], 'y2': y_extent[1]},\n",
    "            {'x1': x, 'x2': x+w, 'y1': y, 'y2': y+h}\n",
    "        ) > 0:\n",
    "    \n",
    "        w = random.randint(50,300)\n",
    "        h = random.randint(50,300)\n",
    "\n",
    "        x = random.randint(0, im.shape[1] - w)\n",
    "        y = random.randint(0, im.shape[0] - h)\n",
    "\n",
    "    return im[y:y+l,x:x+w]\n",
    "\n",
    "for p in Path('samples').glob('**/*.tif'):\n",
    "    print(p)\n",
    "    \n",
    "    points = getPointsFromLMD(str(p) + '.lmd')\n",
    "    if len(points) != 4:\n",
    "        continue\n",
    "    metersPerPixel = getCalibration(str(p) + '.anx')\n",
    "    buffer = 50\n",
    "    x_extent = int(convertX(np.array(points)[:,0].min(), metersPerPixel)-buffer), int(convertX(np.array(points)[:,0].max(), metersPerPixel)+buffer)\n",
    "    y_extent = int(convertY(np.array(points)[:,1].min(), metersPerPixel)-buffer), int(convertY(np.array(points)[:,1].max(), metersPerPixel)+buffer)\n",
    "\n",
    "    im = imread(str(p))\n",
    "    \n",
    "    '''ax = plt.imshow(im)\n",
    "    \n",
    "    for i in range(0,len(points), 4):\n",
    "\n",
    "        for x,y in points[i+0:i+2]:\n",
    "            plt.scatter(convertX(x, metersPerPixel), convertY(y, metersPerPixel), c='red')\n",
    "\n",
    "        for x,y in points[i+2:i+4]:\n",
    "            plt.scatter(convertX(x, metersPerPixel), convertY(y, metersPerPixel), c='orange')\n",
    "            \n",
    "    ax.axes.set_xlim(x_extent)\n",
    "    ax.axes.set_ylim(y_extent[1], y_extent[0])\n",
    "    plt.show()'''\n",
    "    \n",
    "    cropped = im[y_extent[0]:y_extent[1], x_extent[0]:x_extent[1]]\n",
    "    for i in [0, 90, 180, 270]:\n",
    "        \n",
    "        imsave('cropped/%s_%s' % (i, str(p).replace('\\\\', '_').replace('/', '_')), rotate(cropped, i))\n",
    "    \n",
    "    for i in range(10):\n",
    "        s = randomSample(im, x_extent, y_extent)\n",
    "        imsave('negative_samples/%s.tif' % random.randint(0,100000000), s)\n",
    "    #plt.imshow(cropped); plt.show()\n",
    "    \n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x270860a7898>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAChCAYAAADJLnTIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x270860aa7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cropped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x270860a28d0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAChCAYAAADJLnTIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27086086c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(rotate(cropped, 0, preserve_range=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x270861bf4e0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAK0AAAD8CAYAAAAFfSQRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsvM2rNduW5vUbY8w5I9be73uuijak\nLFDEno0SCm3YVRA7Ykesht20U6BgR2z5B/gBdgSl7AkilA0bBWLDjiBSZVGoZaIUopiaKEKZ97x7\nrRVzjg8bc52bJzHT+96sPFnnkmdsAvZeO1bEioix5hzzGc/zSFXxU/wUv06hf7s/wE/xU/yq8VPS\n/hS/dvFT0v4Uv3bxU9L+FL928VPS/hS/dvFT0v4Uv3bxgyWtiPxTIvI/isjfEJF/9Yc6z0/xJy/k\nh8BpRcSA/wn4J4HfAv4y8Oeq6n/4Iz/ZT/EnLn6okfYfBf5GVf3PVTWB/wj4Z36gc/0Uf8Ki/UDH\n/VPA//a9v38L+Mf+oJ37cavj7R0QSgSqEASAol6/yeuv/ep+RV8v7732rwKagEIlIvLd7vtIUkgV\nKSAIwu++FwqpvV9JIfXddzopee1TCSKA/uLTUK/jUlQpSP7iM39/HitAi9c1vv4r8nt3kKJqX+7v\nXndCyT7N7+5Goojsa5VKRCBeZxX0e/fpdV+kvn+i13Xn965FvndVglQRr+uW1/+lipLfvTZ5fdbv\njrzv0esI8v1P8LvPDXS/sb476/7/x9/8v/7vqvp7+CXxQyWt/D6v/Z46RER+A/gNgOP2zj/8T/xz\nRBqFkyI0kpJkEMw4sfbE/eAUuMwBGNGhhKckhzxZ+ZmujuMcDtmEoqMsojolXyhuuCWk8SbJlAYV\neCWjOmFPevyMZy2GBQAzlUMepCjdjWUnTR9k7qnq6op5QHZSlKwHJ53Lgvc4uYtiEaQE1ooPKW51\nQ7gTfiD2se+JvxPNsQqyDoYsPkwZHkDnqInrwvXGkQ0yqEquHjTpEEJDibooOhZ34jA8DqxdXPu2\n8aYHqxYjOykTNSNqoJXABIQQRel4fUAaXYsHHdHAQhkYD+mcPEmcJOnV8TK6JABZMNvB8GI1xwBK\n8CqMQgtUndUadgn/1V/8d/7Xr0muH6o8+C3gT3/v778P+D++v0NV/XtV9Wer6s/a+QZRqDiUouKc\nZVhCxQGa1Oy0WmQlMhsyG0EidqdL4dUQuZC8MOB+AGZcUiAN00VglDidQQMWgtQekw5tFMWZA69J\nl6Bqb5+08OqINMqMMz5wLwpl0qiYBMHVilaA3AgpRhVPm3Qmy4ROYCKcqUR9cIVxaqI+UB9Ef9ID\nSgbKnQyjAkoKdHENY+hJZbJacvXFbMl7KkRhCHeKOAtvMMc7VZ1hgufBKYNTBhKJtMmlwTLjzqQy\nWCQPO1A6gSC5GLyho+HaOcUZ+UbTPQI3W4gILW4cQLVEFcKMMCPFGZmkLiQdyaJlUnJgLZgjuWQw\nZrL6+urk+qGS9i8D/5CI/AMiMoB/HvhP/6CdpcB0MVIYIqQ0LnWiDKuAUFIdEXjSKHnNyCRRB5KF\npJEkpUbSGVlIOANnkjwI1BQYCIWSkIJm0Fjc1gdLBJdGNVDZSdYJphTNfE/tmcyeFAdmimCcdAad\nFolrciS4CSsNi060oOtFNGNNIas43ThEuZM4J86J0fEBqRNVA5sMgTM7IyAimQRDhRXBCsMyKVXC\nikWgGsxpaEGXi+ZGRNJT8BH4CMoa4ichRQMMpaM0Kc54UnJnKIQl5KTSqUp6GZLJMsVVaFOxcmII\nyUGEElqkXqReLA0ihEYjQxAxypTGQnLPCplOtcbpXz/p/yBJW1UO/HngPwN+E/iPq+qv/4H7A9Rg\nqrMIjnnjKY2hTiiETgZK14EpHJUclQQHGSB1Ry1BB1qDA1gcXFroUjrJqQdVg9QAD0ShGmQaj3zj\nb7Y3mgA4JonT8Ox4diQudpHpWEsiG4qQ02h18czBrGBQVMKzFeMyHLgk0QhE3lhlmE0yi2RxHw+0\nHVR3qjtGosuxVVgpTYReQogwTXgnyHgjltIpblGIGlcWRwilSaMw3eVP5SemQWoCwttTeXsqzp1o\noN0RTyQMl4ueBfmJJ52MTkrj2Qt4Bw5cThAnM3gPyH7ngYI7EwVTRoKm7q3eaPqAKPIwyp5AoVUU\njoYwBKYUS+2r8+uHqmmpqr8E/KWv2VcQopIqMCDsW1ocTGscKcDAEajJoJNt10waSTbF6o1v1fjk\nyWqOZtvTVhrXmFQN2hTCCsNJeYdSJBdpH4xqXDLQDJzEQrBaeDv256sbhHAyedLpVaQmp02eVdw8\ncQ1KDnoFHoG3zq2SSxuZkBWYC6VvmD3x/MTbfJLyQZPjF/dCLZA6eKJInTT1XXfW4iMPmj0Ia5xe\nzDZp2lAfiBTqoLL4xODeiqo7VcYtOrMHz9w1utZAlzHa4qENY9HLCBOm3tFQRgpPvVhy45twvgwh\nahFlnAVTJlaKSge7yBxoLqKKjJ1W3fbsIG3RvFEMnuI0aUR0zIqswVs9+Fb6V+fWj6IjVpWkBoqx\n2CNvl0WvvutWkk5QCEuCKQdTDqQFwp0HwVjC0iLSqXQ0lMyk140mBuY0SaAjtfBchFxQnZKT4QuX\nRSCgnRSgJtREwnadTNDkuVfLWfyOGZqKA5JjX8drRWyVzAG1Bi1/Ro9F9YujkhQj+UKJIQlBERR3\noKqRaZCTLsUkad4Y2fbCqaDY9yDzJMoRuyiM0Qq3N5YEIs4CGsVlD6SSXie9TvDESB45oBzXgUew\nHMyhacPbgzLjrRJvk1z7yyjNEYUmJ15jL5bDOBHQRjRDtaO6F4bJoFAagagyEJJE1KhsnHYx00B/\nv7X77x8/iqRFhQjZq2t5omYkIL5IjG4Xng1XQ6oYORk5ab5vyqdoIJOlCjUoOXf6a2dlMWWv6l0u\nDjGKya2Snp9RBlITkYHmwY1GJHu/Oqg6yHbh+aSr4HWjGIQVP3sUSyC0CGs06TRTSjspQVtK04us\nD2br6LrxYRNFOLRhVcxqtBRaCu/ZcJSpA6ORcqEVPE25qhPjQuugVweNXWMy0OpQwUzQvHiW0iMx\nBM2LxgBNsmJv1l4LiSRNUQS3jonQy3G9cDGITkSxajDKGECk8AzBdZIipBl3K5Y+aH7bNTxJkLgt\nUhqghA1Ip8s7JYXKoovjpUgLhn99efCjSFqpRPiEa+DyjkUylnBvhtXFLOPUL1QtjlqoKqrKbI5S\nrBfu2ksY0lENona9eetF41uWCK0OogSxImSyypnieBirnJGK1EJrsioZwAA6ztmMe54MlEsKLefn\nZqgbxpNi8eTOFKOTNB0kRmmBGmOB9icNEHE8T5LCWmLqmDrLgkbS5EmJkrox6lZgFJWN0gtl1/ja\nAiWJGKQ4WYVifCqwUs4Unv0zOo3KpEqoElwWLkWk0BAkk26dsCC1EcvQMFokqsotnKYXj7ZXwKMW\nUkpJkj6RMjKNyx5km5jcMblz5A23jX7cqihNUj7oJOWDSwY9O7EO7FfIxB9F0hZKyQef8+AEXBsu\nyVtOyM4tki910MrwUq4QrhDIjovhrVApGk4AMwMX4ZJBLJBQ3muSMll1UWE4J9kmI4NqxanFcyRu\nRdSBllLmlDktDJeDz3px2Z2hP2dU0eTB7EkvmE2ATveFFYQbdLBYuE66PbjoPHQgAaaTiXCrJKoT\n1VE3lt7oKYg8kJx0BkWS7SLFGGX0anwhudpFpVD9WxpGb5PgxqMLaRcXnbc1+Z23gDr23G/OKMNq\n0GOPiQeJy6SqY2W8C8gLzkpXnq2R0vlmFVJJmQLBTYojE6dIK1oZ6oPGZxqfSbk4fSESPAgoozAi\nT7QbIolkIFVo/LqVB0DT5CqDmVzq0CDLME2Wds5SvIySjqliqjSZvLsj2tAYPEmyAINbOarKaoq0\nRsrrpg4BGuhCU1n9QPPiYc6xnCrD9KK0mKHMUJ50pGDWQFJIv3HXjdF+ymTJ4JzQMglzpgTruJPh\nhHYM5SOUXknPoFWjUnkvmHJgUVgUEDSKMqEz0PyGiweqFz0+YylcojwoBkm7ButQQgYPDe41cO5Q\nRtDRcrIVn13p8sRePykJcpHakTpYJOHKAB4UokJWILrIkSQJNO5N6ZIceeF0lrwhDBqdVgnauWoA\nDuyaWlqg1bmsKNqGznB222fykI5Y38/8K+PHkbSyP0jXB4/euUXf4LU4ywSRSbRrr6xzT5VGAQeX\nNTISa0kXBelYOd4Kq6TFxK4N6Esa6WA26S5QSlsLUeFYnaWOVEPy4EIY4YxwTC6iHrxF7TpSjZ7G\n2+o8FVKVsGSqItVwGocftILUYInRrREovTt3M5YGTw5aLLxNvE2iT6pyP1K5IVbcSkgZiN4RCY68\nEDnJHFBCu4QWhaQilpzyJNkLW5NkcnHp2jgqd4I7+WqvZhVnKcLJrU9WBk0aXkrPzkpABM+F1oMz\nEqPhcmzIKr9l9kTrjkTjkifdnlQpVco3VUi84Szew2gkjrNKgInEQNqkx+LrsYMfS9IWuBYPKQ4c\n4cmoZFUwXUk6qw4UI/ti8mTypBhc2niKISksjMYXKgeWbyzbi7PoxcM2y0BTqOqETUS+0KVABlUd\n4WC+RqEBrL43y4bQ8PbBpeARJMbTanMR8kJKMYJSoZXiXAQNycaZkNG4VZAOt+ocdYNcoIXkDckb\nPYTMwtKZ9STcuVAsjFWQ4kx9o+TJTZ9US1Jkf1ERPkcHTrQ5Fgew27ufxcnkF6jLLscaqs7UwGoR\n/o61DT9aTbIVhxhjGamDybkfVSpLbHM3vBHZED5hEgxXohJ/bXd1Uh2k71ZvKY2Oto6EbTxIky8K\n0edXp8uPImmFYjhEHVglqY2FUpzcJCE6wgPJJys6ag21htvPsQw+cSfkwCq4xDjEeTDRaQi76yUh\nTBHkNXUhB8bJs4Fmgj4JhJNFSONKdr2YggtICVGfeIvgpknXpAtEnYCyqnEAEkKY/IJ6Urp4KLhO\nfq5FpZD14KkT+oXzjtX12hStJ5kCbkhrHHVQdbHUONZg4ATOpU7jjaa77W0E9xKWDCQeG7CXE0O5\nx8nsyVsab/kqEXJhNXAtGIurLXoV1RZpRoXyEEXUeeNOp7jEqNZJSToQLWnRMFlUvqFcvOfuxmlB\nREdilwKZSm+PvdCTIEyI1qjs3DQgjv//JPle/CiS9qf4KX6V+JEkrYAcfLYHpYr4xmvhAGmoTE4G\n3jodpWJvlPCGbwBbvtAFBsUVfcNfJPYC+8nOIfdN1qiGcrJq8HblhqWqMfRJUHgFpwClUMrqSenC\n68lTXvVoOC7CqCJpvKXgdMruSCVBoZlonHSUUZuGEtJAz10CRSf0QcpJyslSGKqYFaaOsJgIpWPD\nXbLIAhND6p2UL1QEmXsxtqGoQPMb+jJaPZAslOJ26QvqWiBPzBpSzm0VKwZHgKuTAZENA4aAi/Ks\nN579ScvavISaRBnFQfSJE2ib6KsZRN9bb8rsHZNCSwm/UVnc3OmR9Pp2c0vyDcuvz5YfSdJCcfHw\nN4QgbLC4oeKUKpadioUkrP5khDJCKTrLb+gLU5VQQDnbYgqsdmK16AWXTCxPtIBQWvwOxpMPW6hs\nbuiio/6OYsw6aJY0S8w3o3TpoF4U3m7KJY0FDDb7zCVZeaPFtfFMFNWksigDiYOjhMoLkTfqRbcJ\neRLyfBUuHSNo1qholFy4BEayZJIKWptSSAb7a1okwWiD0LnrbHMua0juXuJSI7KILEaMDTWp4G03\nNgQhvXOkIblYOLMSyjl0cfruHPYE9xPdlCN6FSrJrCI0uHqR1cnquCpSN/RpqNamc8riLjeKRnrH\nqpC2vsdd/uXx40hagQ8aT4oHg17FkcESaBn07lwtOTKwdSIGYmDVaDywEq4cfCu+F0x+w5i4JErj\nyaA1WNJp7jzbxWVvBHDwxqMEl6IC0h601kHuaCVaSaui1WLItfHOFAjl3YOm4CjZJ+jkwFA7eYvG\nA8erUQqTIO16kcVhxCJ0sOqJqCAqYJuIM+sdZzcAEmEEHCQqB5GGS+0aWz6B3YkqhiRX1uZdmGIU\nRyiXBSq227j5Rs837jRmbeq3hSKqhExEhdInXYSOcs4bIoFGEGkIT3BDbb06ZIJ6IQnNBBfFSliv\nLSPpTOJQXJJLFJNO9Qva2k2gXqgvUr4+FX8wwsyvEgUcqZjBUnhUpyEkmw9rMUl7494meiUPLoBN\n5WufCXnS3PhUjqQy7bk7LPrA/eDdnHsYWsF9CC0HTWuLG3yD5MGJZeItKX/yrp1HfqdM2A/IkN2R\nCketMXF6NOaLoZQ5OHBaLT60cYog+qQKPBVMcF1EbR6ay+IgIfdjaC8eK7LZbo2DtAtPYT/TG1WT\nt6U8rXBxQhqnGRVOaYI2MpNZyk0dCyPKoYyrb8LMIYGnIr5hsGwnVymforisYxStLqIlViepTstk\nWeBihIHFgyghzLiFsdwYElTGvrfAW114FUHSareSowLNorKjulixORslf/v5tL9SSEFTKJ5YbtAZ\nXRwspCbBG5oLmW3LYOTYG4rUBZmoTR7WYdwZ0rE0ZH3aDYZ0UhWRjhWbVpgCZWDxku3cKXGkFBVj\nVaG6UF2YCBG68VBPpnxmpqA1CPY029y4rY1SXHUymrLUqDiJDEwHU5LwQS+hRcdyot5ZBIsg6wup\nm99reaLygLhRTfHchHAt4RKjqdNFaNmYutvDLY/Nwy3jrQovQ1Q5VcletHBaOJHOIUrKRUlD+JYu\nyqVvHCJUGEvfCTtxqc1/EKH8E2XQM7gAw9FKsk2cyVUO1miltFIe7cBb40JIcZ7yXXt50DVYedts\nZxXarxthpigmRYmhJKLJGYXpB6OEqKB0oJmkdV6iJUKDTEOygEYPuFfjKljyRCSZmmQJZwaSTzpF\n2ELkifjBZbctudHiQ/TVv4cpSYtBiwEVIJMsOE3p6iBOyJ2UZMyJ2iC7skwpMVYEkoKzyevkl83U\nkjvondCkuHG14xfnWfkO4UQK2L6mSya3CJoorRZe0OR6EeQL0WB4chlMktOfHLEIKcI6JRcriyNe\n7XKUEQMXYalgLbA0lhrGnSeCiL60arX5x9UYcRB6p6J4SuMMQIwGZMKg8VaQMZmymLIQVzIeqAoa\nzs1vqF0oycxGr+QsNkmqvj5pfxTlAcCt1pbG1GYVuRRDBiuLYUl5Y3awGDTbQHTMTui33OhctRC9\nODkoimd+Q9fnXqxhuEA1Bdnk42ogtbAliIHOwa1tcPyiaNkJ3VNWpTL04EP2F8xqj1BZiUhjHcLn\ncLySJ0Wlc2B4dsLuuLzT5I7Lg6jPkGsTYcRRgdIHAAdFWkPrAAlsDQ4L4qWeGHqgdnElvxA0nmE8\nOfDa5PglncuMpg+6Q7aBjyRTeKtdVknc6AFLkpp7gdBTwWovFDW31osHK4WnDUwWxkBZfA7lUqVe\n7VjNG7SgsvA2GC8VQsoHn6JzV0gtTL4l4iTFMFdWFS4nncVT7l+dKz+KpBXgrjfe5GK9RtmQwtLw\nLpAg6rQQ6I9NRgGyL97yG5YsqhKLG8i+wSFsIjiTlBPDKRqaTskbWcGyiaSiKXAYGUGEU9bQ8k11\nBJYmUHSKisayjtak1clVIATPgKVCU6MqmOLQnFvt8qTaYJRiTO71qlGlIB/oq4m5wfYkYIstu6K+\n1ZOHFi6LFoFKEHR6nDw6dC9awmqbgXVI4XkifW3ieRUqN1z3l11VyNoY0zThjWTKhb9qbSSxaKCD\nVkWP2ARw2c2aB8KQxod23iNhXMQSgs4tJ/AdSb/zxRSNor+gs2iB5uSmjcM2cX2Z014dt6+JP3R5\nICJ/WkT+CxH5TRH56yLyL71e/9dF5H8Xkb/22v7pX3asQmgOqxpXdrIKbI+K1IZ1VECqgQdNfG+x\nuOQLWo3K4ijo+sGyzmd54DkoF6QCRFCSKxplDiVonjRJShSdW3jXj0ZTYapgZVgZn0tRgfZSqTYP\nXA8ayS2TDIGmXBJkXjRdrw5aIzXI1wwR0nloR9EX4tF55o1pybTEfXfjRC9aNZLFwYaPVgxKgtA3\ntAZUY+kDW5MMo9ue+neL1eg2WRgPTWhgcmF0jE6IEzoQu9CCDwWa8cYHanuw6CySIm1y1YFbJ/RJ\n1oavig/eS6CuvchUwVSoSh7SeUiH0RAu1kimCtE2onGmcW+LRwrTFmfdGPH1QO3fykjrwL9SVX9V\nRD4D/42I/Oev//3bVfVvfO2BhK1X8gSTyYbsjKuEI4K7bS1TiRDSiNojrXJgMXm2XfP9XF7rb1mQ\ngy6+eQsCsybf1CBb4n7Q9E5XY6GMgruevPPkGYmVvZoaezq9TPEwLIRDvwU7SSkugjQ2zJWLN4LQ\nJKIT3YgMHjk45SKAEZOrTqQlrRSrO5JvyGu1PXvn8KJEcC6MQYliCNGejHXgNln03Y5OQaSYLah8\nEvaOpKMoXwRukfRsNLlYOnnUSwZDEjwIHaglltt94Ckn56qt1auTI5OMg5BCPVAbVA9wIzSImqg2\nOgsXo1Lo8vKaAIiJiKK155KIQFi4vtGy70EJJ2oTxb82/tAjbVX9dlX91dfv37IFjH/qD3UsCs/N\niVU1SopcTsvJ1YqOYGoIxUntixXn1AemuRdoKN0CEyCMZQ4SLCmMxjuNq4zIG4ckJSf3F4ljSXHk\nYpIYN57idN11a0njWMERyTAIxsYf44ExUQbWnEWh1qi4gQbik1tAI7b8nWuPjhb07Bv/5+Aavyug\nHKk8JNlI7jvixfXq0zMbIov1UiEr0KUI3YhC2RsjA0XxtjgTNAO1vXD6MDDZm9tgaKFhrwbIlhHd\n0JcUv4MtUh2XQNQRHVzYloLLhZahVQiDTKUkKL0IbWh9QesLrsWTxlGTqwqVYOlg8QC5aDgtD1Kv\nbZLwlfFHgh6IyN8P/CPAf/166c+LyH8rIv+BiPydv/T9KE0Uz8a5kjMGJ8YygSwyg0sc1SIlkNdP\nqL46Twvd0lpUG6JP+oSLgZVAbg2V5FYlTEnGcgaClJPI7uqwkYe3akw/qKtRVyPECCsiJ5SSKBon\nVp9p7YJ1oi9M9JCkAOVGySaplykjGx+6W6NXOlBQTl93VAwVI3MbdeCFCjxbI1KxSqQZS+At9lTs\nKKsA/dgwWDi9ABHGpYg1UhKXzfT65De+47n2LDw7pgEsegpWjeWyubY+OXIQDkc1KLhrcuQkONAy\nssb+zDU3aSiFRPAcrDpYdWBhjEru7PvXat/vE8EzieyYJmcFoX+MSSsin4C/CPzLVfVz4N8F/kHg\nzwC/Dfybf8D7fkNE/oqI/JU1N5SiCl+6cBfn0ieLBtVpYhxlG4JhF/KaE0J+AcYLk5sEk6Bo+Hj5\nIkjiZng6poLbE6P4oo3lB2qJVOdZu9PzJk5KgTreFe9Kl6LkDdVGsZGH0F2PpTYeBlmfuBukObWt\nKyi70JxcWjzlhRO/tFuiymVAFcqFciGSZN64ujP54KjFm04CoeK+RyoKy4vmIA3G+rtIvTjLCRZW\nFzp2Tb1s+w3MfKAStBJaCSMLjUFUh7jhIpuSWQttQciBsInwyMJNOepO2ImEU+JbI6Zb6Nht4ta3\nokEedEu6JU8trJyzJgdKaNDUcN3lkYtTwKwb8StUqn9LSSsi/ZWw/2FV/ScAVfV/VlVUVQL/PtuM\n7v8T33eY6ePGdSoWMMIweQM76aV0WSxTLlECsBx0FboKmY2FMPPv2GB43GietBDKlZE7QUyebH6N\nYNKh4BsphjmszyiOtqS7MgUQRWQhOhGduCjCE+egyYI4SLtR5XAFQ3ZCqSReoFz0V+nQGLwvf9Eb\ndxlTdCLmRkVMcdnboqDdEXvnPY3FwFi4CNZ3bTgycC1EFlG5yTt5Q3pnasPz5KMOKp+0NJokXQ5W\n7QGA6rjCu31seItgCoQ0dMDwtmtsMUARPTgzOLlhtUngV9tqY8n5WkAlN3dUtqHHjMGMgUUj2KjC\nGU+WGpc+adlwKzoGJKLF+SuUB3/ohZjsavsvAL9ZVf/W917/e6vqt19//rPAf//Lj1b05ZQJJ40n\nT9RhlPGwjmQwGFjCUzdpG2Do4k2Sqx7AJkrTOmTQpRFhpBY9BlLFks0gO3CCkw/t3OSih4MYpptH\na+WoDdraNzJa4C15vxJk7EVSOMobIk8qGy1yd916Y4UBHzQvpgnPZpjHdsaxRUahBCdKSkPqxduv\nIAR6u/OwgdXkQd/vS8NC8fagxefd/PAnJRMVoS0DJmKG+aAskAKvgVTgNTlf0JrnE1d9+W9tD7KQ\n4irlqETl4pmGmbGYKMrvVO72bxucKcCJp+K1tgBT75R/Q9SkvzDXyQDpm2RjFz97Go/eucQ5FD5c\nudnWmT37Hw/34B8H/gXgvxORv/Z67V8D/pyI/Bk2peB/Af7FX3agQigVDje+teJGvVxVFkM6iw9a\nXTxsQ1CH71X9UwwCVBtL72hvjFKWCppO9k6LYmnR80TtC+adq5RqyVvmJq2I4ylMV27dyVBUiusX\nZh1P+mp8UeEduBEkA6vi0QzLxbSgy7m9AdKYdtts/b6wS6AaqoW4cTCZ9sZdCgJuth9y+Bs3mUxX\nhnZiLq7e0EjCGtN0m2LUovUH+A0KPqUyO4QrGYtDEynDWbQWhB90gaVPAEYFnlv6bnogdTG8k23y\nczvB33YSMnhq4wjhrNiQnz+5d+GUuWHJy14OiN/QdKISlO77pqFELcomxuDqc6t8G7CUN3nS48Ab\nVHx9Kv6hk7aq/kt+f3fEr3KV+T0hxciOV5AspjW6J2FFruSgsWh0nBlQr2+lRWc2RfVBjwOpwWQh\neYJNNIRlgRbc+ULnZLbgFsUlbHpcTUIHVop3sKVcPTimIOcW25mfrFQ+6ZPFQPgCCHcV/u6lrGZc\nNKwetPzElIuIA9WL5gdNNhEnW1JVzBp0daIWKQcP9kMuu6NlWxKeQWuN46VpW9VRAqzh8qTlScNY\nPZkxkYK3GszWWCStjLTcTQa7iGro63GXN8Q2g25tCxTCGlVK54EPaH5ScjGybW4IwiWJiGEZtGws\nBQ5HwzAuKhQXUN9NDEFAtwbtSy6qNQThyEa8GHk5FlyK8OtGmAGykjI4c6DrRLkhlxLbj+QlMTkp\nhbGKsTaHIKogTqQULweSJl/mJJu6AAAgAElEQVQgIdkPasgEG6jDrYonifrnFy+gYAVuk1tdrL59\ntNyg/KL8QigOeZIx8bYIO2mhfLMaX16OjYhvJapNWgmqk8kgJUjdLCybm4zbtAgvRt42Z3cN2hoc\nKFaKiFNtovok2wIJRhXU5lloS1ITt8CBQ/qWKTXIahwUj0rWVJTtpRCpvzDUq7bLlGjbAqqrUJFQ\nHU1o8UbWZrNlDQSI6gySbsGpN5YEZCGhlBau+rKuUkIgBBLhrN0fOgQkFlqB135vp7h88JDEfgXj\ngx9F0hbguk2QE6XpnZTgtKRqUHmgeoBMOs4axhqGlXB85wvAtj1qIrjCEjhxWk28fsabQzZnxdiK\nBh4vl5dGaxvQf+bmyqobPUEZKIOs517pysEZycik7OKLXUzbjq5PthQ6CpY0LJNmIKsjuX1wn1KQ\nzlsE6MDlgeaT0knp3B0/3Y6OHscmVsvime9csJOCpMU7lmy3HWeLG3njEoCNef6MwFQRkkd2Tk38\nlTC9FhUN8QNE8TxourDKLQTljmjH8mDUfVuktgezOqC4FFaBqaMyN6JQExFoBi6CiyCVPEtIe1Al\nqNy2olov0MYspdeF6kD8j6cj9kcWUkrIdmIxnmSdmBh3geKDsBvm7FV9K67YC6RbKiofpDVKFCln\n0mjVuVXwCAGUsy6id8gPQreYrlSwANOGpL66RkGV0V7mHy67PFAMKhD7zgfX6Fm8lzBj7no8t82n\nMBFzpEA8CHW0Tm4qrLmbJP8PClyYDZzCXg7dDq8SoPONb04tHJT4dg8nXiplR6WxsqNtm34MW4xK\nQg6cC8/O+XKl0R6syG11BHuKJ8AV1zvWgvS33Zl8OfU8v3OEyRtNn6w4SLk4AhJjNcHWC01hS/RT\ntwm4vcyoUcMqCW4MfVJmhAuHGqFKXYmMg+HyIgB9XfwoRlpkW5RZCX1tL9qoSc/BkBujJiLFUQ7h\nnCgnysUgNUkVchmaQqtk1GYh3Wh0th/BiictDnpeIL49UmmQd4Liiu2JVZo8tfNFZMtksuhpjNqa\nKiORLCaBSydld4Yasul/rSNxYDUQC4TBpZvaSCvKJm+2UDmQZ2f4otVFq4uittdAOKiz9H33+XW7\ntCwxsg3ACOs0cSr6lpZ7sOSdUdvttgMuyVTo3ugVW21QtnVqIiySGQa1PX+X7C+ta3Cr3AQj+UAU\nBhdI8njhuf0qQvJlb1+EGNMLoiiH/ag65xIyJ1VGhoOelB9EPrlMCZ9YTWb7NfPyqipCHpQJ39n8\nzjQGwaUPpE6mJWIwqmOimCjSnYqDXI6KcGFo5eaEUlwNis9k3Wk1CAZLPoEOZgkTZ5mBJaGFyMXK\nRuEcJWidaJ2EFiOdVW+bjN10lwL2gdL2ubQo3UbMgfHQAesEgzaFScNlskRxMUbcWQeEKK6Gq6Gq\nHMOw3O0GgLEcTQWEXpNcE2Fi4RSKyOTIRLSjAXdLIm58q4sjYcSWnCvHVghUkvlkRifl4pMVHoVE\n0aVRIWh0wmSznNu5bY8EkA76TrHRii7Jg22K/OCia5IGJ42Thgpb7kPj0gHrExZ3HCCNG+AGl7Xt\nAfGV8aNIWgFMGi2Keyt6QG9srysOpuxW411gSqNWUavQl/yla3HUfDm62MYafDsQunyhyU6loR+8\n8YGF0sTo4pwpXNI4LPEaDI2tr6qgu9JdyQo+1OihYL67d/UEUar55ppGELL+X/bep8e2Zknv+kVE\nZq61d5333NvGdIOZIBuExAQPmDFClpCYABMkZhZC9IgPgBjwCUAMkYxkyRMQMLBADBCWPwGNBzAB\nWSD+CIMbubvvPadqr7UyI4JBrPO6B3b7veqr6/dKnVKpTpXO3qXalXutyIjn+T0Fy5NJi4OmkyTI\nrYiBIxJdDYvFORSViYih0dHohAs5QUY5KHpOTkuuXk6LRJi2odar76zBnouVD1okW74jkaQtuu4s\nVY6uuA4OCb6p57cwxA5OeSMy6NNwvXAm0eBDJ8eVBQHxA4mGuLG7MeZk3KWRZKdJuZefmazb7ngg\ndeGIwLUOiBIXVzuRtBv+qbQ07KozRrNfwXDhl7lSYKzkkGDPLGmi1yTssSjEp636ty22Xp22FReW\nwpWDxmI2Y48gPW+A8kQ9WZLQgoON51qAkOocvjHk4hEvIjdCysRomRwq6HbrT6kaNXoHu9j9E1eA\nOfRcXLpTGtLFxmLmVskuMapGzQkmhA1AOFcwwmqTaEf9Dj4JaJYcaysVlzSWNJ6rsWJionhcLHFC\nnqUV1sDy4CRJCVqWxqDFxNV55kD8qJHu7dOehTDhu1yctlB1VOrNSV5s2Uvvm8VRC18MAbI6D+7G\n1ItwaK1Kj/coJu0WxkPqdTtUaTJ4BYg2Ni4+xOj6Qud3fLTJsEH4Ivl1Y3kBS5KnLC5r0OGiV23a\nEsvyRy0D9U+c5O0uMCYNI2i5eF4fuCysG1tm6U5rkEv3TluTqXBmkOls+o6n4fITVBIEUoylnUhF\nste0yjvQST8R38n4wPUkdHKp1GjUOuaNa9W4uKEcttBvPjRvRC4GTtde8sooJkNqkpq87OCMDbOg\nSdWHO4vIC1PjsMKcNumIvLDoFcuUnU+6EbpzJVwRhEz23Ig8OeSNll53MAedddrHHIsnGQ/E33hk\nHbK6Ty4Rhs96Q2kJei4xQrK6FWnYNxH3amwCZievXhvzQwqUXDywBVla3AG06LQG3SuJKCla+g9d\nP45Nm0IT5cgH6s68JnsGV0K7+fyIMGMj9EWE1kd2xGqaf1nj7AarceTiRSD2zssGKVsdFGxAKmoU\nmDh+QpCQ76g7IwsmoXqCBcaBcSDaaTl43kTAl/XaMClMGqonWxw1mZNB6sVl6+5IrMJjqmBmuAuq\nztIXIwZQqEvJ5BHVKVgefFhtkIMqI/q96d7tKLyTNtSc6RtHTE4EDeGh76CN3d8KjpcNpXIili2W\nrXLcxlvxamUifJB6cKSimpyts0Xxz0QUi+p29FRkGT2Lr2bRytslydIyRIpXdkOPxFMxTQZKy7cq\nHlbHMNateRCcOd4J+zUzNv7J+pP1i6wfyaYVPE62mIRMohuhys6kISBlhd5yAQddjC7GZSeKcObG\nlAv1UWGA7kRLCGOnxMZJMlJ4qfJJFofUIcEkQJWPNsj4oKUzT0XmYOWDlQ/2vFgy+domG6A+CKQo\n3n7UqFSUFcEjnC4PHnNj5pOm1c5JjbLgtLJSj9hKrcXOFcIVUtMnTfKGIadCd2fJXo+h8Yid7gdy\nNcIriUetUiJNEnyje039JF6ELHq8UIF9VfSRzVbBdKkoC6wzJdFsbEvRXHhzQgaZZaN5l08sBLdv\n/ZESfLspWKD3lKv5Z7oL3YVNAoleijkc9UX2k5mOLsV4MPNgePD4VYvA/7hLKH1nwTE7W+wcVjK/\ncw0yFmsVptPkUZh7dQadDGHIOxmPasto0ttetO1UlncgcD1ZLPa4eHkFVWi+mGGV5HXVVM61sPAn\nFxIHEgcHhQ7SSK64NwiTFY3YJjsvXhJ0pQg3sfD2gaSRE0ZMVigzghDFwogUhiXihVjqpvgACcPE\nQY0tRglhsuzfPxdni9KoLAJRQbXzKQWNyUmgJhxtu+HHA6XhGpDOF4MvBmIL8u+aRzW24kTIwRIg\npUov3onbi/fIk5YXI2H15MoHl10sFn0GTZMb6EkOJ0flnKWALCnnrzZ6NHC73RcHGt8hqXyRX4Fg\n5pe5UoTLjC4XEgXc3XxibIRcNWmSgUu1w77cE5ffWMYXrVqyu+OyShZoJ02UMxTaV3Q1OjUJIpVu\nifgFKCoHzXdWq1HsIyaa8BSYd1SS+cWShrmBXiQTl6igEtu5GmxhZHZCLxJjhjIkWFrmyk0PUGV5\no4mQEswEt0W73avdncsaLkEloZabVjNxXewunFtic8BQziUFHslRvIZ+kj4weWdaMGm0RdWbOO1u\nhYrC1ReZjSkbIQ5RSo1Yi9UanoHKg1TBvYYH3hqbX7RV+uLQcpucvQJETASTixclHdXmdD9xBhsX\nwYOHf3C1AauVjsImWypz/LpNxICufqexWHFQhfqjqjJVSZyxTqYmzyU8l/C1QWdHczBbORDEhCsh\ncqPJqv5uywq7w5mWrKCukgIf7cGUwv78RD4QGdCNSx3RF6IvwgZNXmQPXAaqylBhY0KCXlJes5gQ\nC5UDNLB40dTZuejZ8HgiWgKTwjfttPRvljdCOsoH5sKKsvekJGdbBEqYkFcjxZlLEIGHLEIvZg/C\njJrBdcIfWMA0aFMgVjW/eyM1eTpsCSZBtkTNWAwwRfQmq2f9Xm6KSYXtqQxWnzQaLKVzMlwQGRV0\np8YWsAU8V3Jmvc4Rb4x0PvbgwghpBI3m8FWNvP4hJzb+oktIRoCncfaJ5aLFwHBCGtvqbAlng54V\neZTaUY7iceEMF7Cge2K50+QggCt3eibLWzXLQzljo7EKIhez3A8tC11pTmYp9yVbiVjE7xBopzMr\nligHH2aY1rh0S2h2sJpCKLhCGNMbR24cUPabpdgywqp/e1Eg5CmjoOJSGQe9wdInRuNBbY49Npad\n5U6MRUd4KTSUTSa+StoZBLtEibPTioyozxuklzzT8Gz0FEQv9B4lL1WWTYZ3rtlYWVEAtpT0mm4d\n6ph3liYtg8gnrS+QF64biVCmp4sPS3pAcy+ROYrN79A8me2FpfNSu9H7v2Yk8NKYnkgow3sJP1DI\nC8WRFrgLloNXOsKJcPIMK8CbTJzqp17SUPXq9XsiqkQ8b3vOWYcfPZF8A1UulMxWfWC48fbFWBhM\nBrNm6TFQIM3Z0xhMJBqQqHWmBDOfyLKbrwX0xiaBSjGv1hTUTvKG0j1i0bNuuZtfLJKhz0KTro5i\nZE4mSciDS95LNOTKk4A8i6XFB6c3nu4sc9SLdONXwxLMEvGD8CQ8WQod49XrCj4kQQJwhr+BBN/h\nhC4yJ70fSAbLg9MSz4J9NIQmzscSRJOIwukLD4QHPQxvi2gweLEsOPXCskFUlpjKIvTFL6CX+XFs\nWkQIMZZW8Jurgl+cttPcydUZ36zVZlQp3njPhqUxfPDBAg02WzQvOEQFOJ8lrPeO0NiiRojo1/sF\nFjpg2O2j2iE6koW7v0RwKwnjJY0rnyxZjBA6hfUJUQZe6jCDKcYjOkfk7YxWwpKtLVo2nEHzVnj4\ndNYmrE1QKRXU1Yw3OxnyTtiTFU9MwaXzyQPYoHVebav6l51HBpe+Mbzhrd0O37hpMQ+ubhw2OGwQ\nebH0pAOxOu4bnorSmRZcVL5ZJzF7FiDakl0HP7mCjKBr48MaMz8K5xR7dW+WMeNkxsmyKveubEwN\nehiJ0KWoPh/mbFmBL/wCLK9fhhv3fxeR/+mmyfzO/b0/JSJ/TUT+5v35j7SRZ0Jez/rCT2YKojsW\nzmzOahcvqpbr+K3sKqetrMT14DsB1mKuxFXoOYFAc6O1F1MuujROc5o6Sa96DlgUGHjkVVpWNgaO\nhqFhbKksTR55EPqBcPGS70i5aGciXhO2uSaP+PZ8kHKBnKhKpb+4cdhF5GRKTctWS+yy+rAXxqwc\nGvlMxoZlgl2syNvBWoF604OuG5ZCCjiG+olZmTkRhzY4ZSNk0nLwFs5bOJnKclB3VA+E5JIHLRee\npd4igkH930kjwistKKslF6HgxW6oYcbE2wS5GNYY1pCcnNZRbagosOir3vRPSvq4pKw8W/7qMxf+\nxcz885n5z99f/7vAX8/Mfxr46/fXf98lJMMujORhLxoNb/BAEO/02QidlVXlgbpW6DDlYg2q9tta\notEZ37+wi6JwbZxNyTixSGYsesC8mWFqRVe8zOvWaQeXDDy3Ysm2n5E+eZeN7VJOnjgvloLsJ02d\nU57sNF4oQ0BYpBSZXG8gRk9D5kbSMEtGQgthtawPL1Fhuhb+U74iBMTErS5GSxYthCEXsg4Iu1PT\nnTUG766srOxbkWTkgXswKbkjuiA7PQchjUs76MV3bgxbGDuijZmdk8aIA8/Oqw1OgVMMbYWZ6uJ1\nxZHqmDzyAQKnl+a5B+Xi8Mnle5Ug7YOWyTRh87pTXqtxyT/8Pu2/CvyV+99/BfjX/kEP6GpoHiwe\nTA1Ugq89kFy09qKjnE3wTL6Y8cUMsyDuoeBKBx+0fvJhJ+ROygNHWR5sUTyYXEUQdxPackYuXv6k\noei1oRmkU5DfVhC5K4ShgYiTPRhy3taVnWM1FtCycYqzNXDxGhlbcMqDF17lihQOf2hUloNVNOi3\n9oHZASFc6mz+zsMbaPDAePMal3oaZ1ucuQOD1MVLlZYX4sqwVnFMWT4X00ZTo2fyksZLGod6kWlW\nQfuT6rl+hJJ5VV1vwhlln1EV3twrx1ZAqTvFjInLTt6Uy1ccZCi9X/RewxERB3H2dJKdx2VcmbTp\nhFXMaWovqMoPXL+MTZvAfyci/4OI/Pb9vd/6ZiO/P//mH/UE1Zm9WLrhXia3tS626BVf73DdHFZs\nY7AqmsjrN0ipg9vLXsgq+C8UIv3pB4940NaFZEA3njikM7Qh4jzlC4uA5vR0LjEknc5J5+SRD1Y0\nJAXzsry8xLgIRhqswSmLZ3SuXPQlpSdYjd5e9ISWikqQ/kZmCUXW2jBNVnZWdtoS0pzhjVM2Tv2O\nIxes5J2isHzSguFpX6BlPLTVCTOGnpCL1X9ORtnFNRwk8FbMspHwACQNdGfLxsIKH6ULM7kHAo2t\nKa71xppQ4SlWToVkIdLpGnzRjqdgqZwq+Br4GswEWxeJghiDD462oYzij/l3vI/6Wxy/QPfglzFc\n+Bcy82+JyG8Cf01E/ucf8qB7g/82wHh8KsEJ3+FaUZ8jhbWCNzs5W0NJQp/YjUUCaCJcETdy52Im\n/IEaP3ElVMCpDaGrElYAj+BrM9SdGEqugd5/qOTitGpJZTRWVJNcCC6d9OwcPNjjxaYVjhwWmNbJ\ne4ngGF0by5U3Jl/mk67Byq08YbpormDKLotYSb+RQK6JYVz9YOTOll94eJVG474SfRWH1QCns9UG\nkw803kg9kbCaFGqDXJxaebYS2/feaY1iOYhPxBoNR6eCNrpdIIUAHeEsBv1m4YYkLo0mRkbxDK7m\n7HmAdmw5j1Dy5lIEBynf4XbS7uljyy+4dojE5IW5kXox/FfoXMjMv3V//l3gr1JEmb8tIv84FLwD\n+N2/x+P+LmFme5D6HSsPyljsZE4GUU3pCGLtdRWNV8HKIpmycE22qLHmHsYnVmE65dvPccRONhWQ\ni0c2xGtcOpexS5KxwQw2dvYFVjk3aL7QfBE6eZB0CTofXK2RTCIW5CTEaa4Vd7o2PnRBm3z0wdCL\nzMXgC0OyCIj2xJGbsj0Qb4g3rjCmCD1q/v8iOG1xSGHgMzZ0GZsmFwb5Uf1jHmAfhGhh4m/3spsR\nocwU0o66Jya4+fdGwpRFpJMaqE6++LeT/IskUC1T4hQj84XGgpholqmzuyLxrKAVMUSKOaGxsNyZ\ndgKB+hNtyUsrWjV1rzipvDA+MeVX1KcVkbcb84mIvAH/EkWU+a+Bv3j/t78I/Fd/9DMlSw9CG5rG\nTCsgnQkvubhSSE4uvVCpqww4bVU+wsrFUGVSztQh74ROLMBUuNL4uXSCN97VaXGWZUYPPkguKZtI\nxsVLhKnKR58czTiaEVnmxmmBptZJPpRNE4lGxI6rcbYHI7+yhRA2SS8lfxfHeZYFPF90nTVL9YmP\nwPQD0w+aKj3fEUlcCjQ3smiP4sklF26Bx6KJEq1zsIqL61mMByl/8spJrotHGA+U7Xhw6p306MUS\nMzEiNrKXwdOj8SZBqtLTYAnEJyQnYwUhG5+mI3EhzXial1g9Fy07MPHge8zThyy22MgE56puDBfc\naTpuSnhH9OfoevvB++6PWx78FvBXbx5pA/7TzPxvReS/B/4LEfm3gP8T+Nf/qCdJhIhet2+ckODI\nnZYfDIyWgfItHQVCq167io+G6yckX2xjcFwlFk+fNFM8Fib3ISgm0o2VDfWNxFELxh3xueTEbC+/\nPpPrvm2LJOEDdbjsYvdnxTLZSRNn3mopk0p7CSa2CnPvoqhvaAOL5EorVkE04MmWF36n25ieaLTv\nebNNCoH6Zidx23jCBy7wiMGpFzsP3J2rbXy3PnhlZ5iyeUfyxFvySuWzHWx3net20VfntGDI5Aot\nsZLGPWFckG+kVsCKxRuxXZgfvMYGyxg+EY/C7Vul2qC9MhhukcPn1VhMBg50Pijf2vQN3Q4yN565\nuJYg268okikz/zfgn/t7fP/vAH/hF3imWzISBZ3IJyoHmo13ORj2hnqQUaST6TfeRzrLDJOF0DlW\nIlKM2iUN8qDHwOvYQOcNX2ddrfW9BgaabFP4Ooy3uZEra/zpHbX6OZodpJAhb2mknBymuGzVtuLC\nRXnEV7DHPciIwuLT0Pgg1uCSjsbFEYNk0exkpZD9PjRluSzMzxJju8AIHrMBs+B1WSbMy75i3pnt\nQiX45I3QjZ6Kp9Aa+NqRMD7n4tJkZr0J96txtGJKHCI8PJh2EnTOCEZuuJw3SvUkVNlWIDyZHKgK\nKwxBcVlwbdAnE6XbO5KfgPL4bfLiyieQ6Hxj9iiAyHK8fynKuYzy3P3A9aOYiAmwEsjgVNh4oQTI\npOuzdJix6Bn8vGnRwKU8T3pjLDKKqOhUTFFm4eJnK8vzIDnEkVQkEqtWI/spILCfF1MnZouek2mJ\nRcOiEeVv4IqyskwGJmV97usE2zCcQz5xubHkIlDeLQh759zgZQb6okmwRVG8mZ+omLnKnaE1XD44\nteHRETOaG66JspWARZMmgnpjiiHrvrVLkdNVSyNwLlCdNP3K10xmwm4vdntxbqWfFTnoMTlwzBV1\nkCwYnWvHBHZPQgu68bIXEqNCBE2Zutiyw3gR9x0x/TOHJIckPS+O2DD9Soqw6ckIR8Rx6egahA/a\nihL0/MD1o9i0ACNX5UnlwBVcdi4Z7PMAAe/CUuXzPCteM5xzPUnZkBSSg5AT6crJYmhxAshe6ioX\ngls0LeX9X9pAgimNZqBhfA3h0gfmFFjtPmA0L6ZYWmdxgjwY4Vy2ED/Q3BE7WBZYPlirYXe/szk0\nXjwCfNwmQRG0n0zR7w+W7Zqc8okWdZDcViPYUakr5ZU7XQuQSQiNKoUWQXAxGKyQcs1aibrTN3Yr\n1dWZnznzMz0WuxSCKaShURH26KKRpHQ6MFMwecNcsEza7Lg4Eh9ceZCSzKw3YPeLpRNbTl8nfZ3F\n9SIJ39nkZLZJ5kVTxdWAVYTKOxnzh64fxaZNSdKSVudVMozHOhiyuKyUmAOhxyTY+aY92PMg3Qvf\nLlbqKHcgOHPytiZNT7Jd+F02tOJ4syQgP7ik02LV6RjomrT4uEfhddyeeRPIJYiYdNnIvPBUoDPF\ncT7IOeiz8eIg7xhNPZWVyYZxZS/XAItHu9DV+C6CLZMtE8nOiIO0F5Kzesf5wnnQddLi4qULzYZq\nw9PZM/FoTN1oARpZKYniLK3NTE6WtT+0OSpeNKURON0GVz6JNLDEeEHCyGS2D6SVuFyyM1gsedIA\nVWWvZ+OjvTE0yO4M6RVAKDspgaly+YD4RJPB8lXPzeB0qQwzffzg/fKj2LSkcEhJAC0dMA69CFeI\nhcmDi8ZL30AmTxGeInzYgYpUdFMaYhcrBBenZ3DKIH2nE0g0WhSR8GTxpFDtQjCoK+4WNX5VDC+G\nKKiyhSLacaFwRDrrYGRJ5kbqhubOCEX6wRtRGWHxoKkS8mDlwLhu1JByzmelfmO8JHlJcrRJU2Nb\njZnC0QPBmJw8suP6TvfAY/JzdS4NXsCeF49wQp2zfxSbLAT1i5OBqNNT2PRi0wth42yzBgKyCFml\nubCNMyqFvLRJnQtj5s4pHWlX0cM5OLIs9C7OI4yn/6wSeFw5PDg8aH6ismN50jRpOJc4zRtdqj/d\nRVnZa4jyA9ePwrkgCdsUotVYMgDJB5aTk4q8VxWWf4tbu1leOTjwIoMLPNZG8GLOz4RdbFLph5KV\ncPMyZdfEc/KzLGbszuIF6GwcpuDtDsaoGg+qe7AooXPmhfqF0TnDCHlHvCN+EaasudH1nQzlVL/r\n1YugsZqQUb3MbC/ShUuPclQALSBkVf9WBs+VBIsR8LNb5D6kM22yc3vVmqMt+IiTp/SqhVmkXKQ8\neMtkpiFtIauyuqZ8JXJjWND9Dc8DzHlQTARVCK/41czkjRfk4DJhYQwRJKojNlbn1MXS79AzaXYV\nyRK4Bsx4MVls8saZXrlkJhzSaR6cWxZd5hew2/worrQpAi0JkkHiakRMPJWnVRAzE1IbSq8xptRM\naL/DMRbK0YyLndWTZnem1V0aYPAWTo9KHvyUBpmcouw6cQ5aTJpOVlZz/JDGIQ3PQXfBosiGpaa9\nQ/EEDn0nrZEGmPPBTuPJU042OpaDkEJ/Lmoz7CmQFTFaSTFWJkKvXIeyHVV4yTIhs9Nk40VivvFg\nFqY0Fu+hbP7GXDA0sew0/0yoM9OZpjVQoe4s5Nvt+RIu+SA0UITrvsdITD6sk+k0PThj57QAT0wK\nAxVapdwhF+826AljOzkkaPasjyh5qOQnNBOy8YYRUp421JFc/ISaaP7Q9aPYtHJD3TQcF+PTcqxt\nZXuJycqCPqRrjXAzIZNI55KBJogerJW4BWMZ4cIZjbfckezoBSqT0EBaluJ/GZLCi05r201YkYpx\nT8OyDivIgffFV90gJ6dCk2qmG8GWFX6cvpXrol98MPHsXLfAxLRKHZHBZPE1HqW91asAruk8VyPa\nLHPhvSlaCyxBcqER7G6QwUsrM01FMFOkLQbJFhtXN1Q+MFUSYVx3Xi8wAdWTiyeHGsmGinCo0H0S\n9xXvu+WonkgMTgV1K2qkn0ypM4UZNLUKANQLHEYYQw+GHpxZONZBcqXSdPFOdVc6lV/WQnkxaPHr\nxqcVLT6rbLTMmv/nxKNyCkwqg1Yz2fKgI3SkkJsELRJbQeqFOZx61IncLq50ZlPoV4VhpFR3pQ12\nnSjOkFX1nTUiJkTDdPi1u/8AACAASURBVNL0oumFNFje0HzH22CPdSORBpobJndOrDojNjQefPLF\nJZ0NZ/Ua2ZpMRk52jDd7cUbZhZYUuukP7B1fG29ZQ5TGgnWSWlOlSxqhQTOHFZzZEElsJksXroLn\nRYtFSOdavSaMpSAvsYtNoKO5kLwQgplO93lnO8C7Nq62uOINFUDfWTaxaAwdXBl1oHVYTTGpHIn3\nPsiuHL5x+IYzabOBgkrc9qkksqycl2gdNsNvxu4PWz+ampZ4q2AMqatCJ8qfxcnBg9EmIzrv+kDu\non3z5BKYIjQtwbTlKN2tTMjBVK/MW1F6JpE7msB6MaXGxiFBn0FYYuw0DSpVt4rangOY9KWsdmDS\nOGSiKmQWu1alZDwfKH0K5KDlwURpq4TZU+rKHqY4Wm22dMzr54hupB2470hepDYEJa5F10bmiWsS\nGJqzcEpZb74vK1gmWDgagjfofhD5QDOZum6FGkxJupZIW3LxEGXlxiGNwclYwdDGpR+sgJaf0fYq\nDq4unnniJpyhbBelFZbB7hcvhF0qoDp4cIkjeeHa2FJpsRBdaEoJnbz0IWG/ZqjPWifLNtI6YslH\nGp9wtpsobSGVSZPOxmRj3i2nxuCqLgOTaedNvG6cUmniWyY1XK1BcZiQsdFC8RbkFESDRoKCewXH\nWVaCfWRUzlZTFsYhgWn1lnvCjMo67yxaq2CSaY7lG+hW3rKsVMP3PlhQA44FEvv3nqpT3mEaKopY\ncGpDnYoC1cnigXHn9EogAtNLILOjxVbgDbdFXwr5HUuDN0tUDNGB6MBCsViMlBLAt2TpB7TzdjxU\nNT28o/lgyAfiMPSdM5KQneVSaWlqvOwJFNbqAVy6cekGsgiZ6M1Eu+LEJJFsvHRgC6YuzpHFDf6B\n60e0af9k/cn6YetHUR5AdQKmHIyohncz4cNHJX1zcrkiLZj5QO+T5iXJJw6WAR4In+vQ1AJJME08\ngskbWwRqYJxoGGmTmZ1+OmdLQoFUMifaKovr+7OBXjx8gHyQFCr/ygQ6iyxV1txgnAWsWB9Er1v4\nhRIhiG0sYF+LRcNkoty92TuOSGnMrsiapA1aXoSWDNJWZ8jFXB3rF9N2LEB7ohF4bjxt4vmOxMbk\nwlLpAseq+0z79vs0wUk8JpaGOmTujAw0H9i8mMMK2EERwVODzKqL37xzinJKYtZ4+EmUZ5iVit4C\noCucMRY6B7tMXB6sKHnmvl531sNGnxfxq4wZ/WWspAp19cD1IrKRq0iDy09SNoZNZFlpDe6mf9OT\nKYaHInS+6gu4aCgWJRtUbzzXzwpxH8lEmTpxioq42kIrFIeVrfA+a4Epw+tDl+JphD9oujNcy1ka\nF6qLZww0nTgb4SfnuPW190w/eOByUud3rbabVhyqXk/2fLDng+DBnhRLK6oBKByYd7JJ1eBNERop\nUa7gI5hrLzpuBoSRciHZCblQmaQ4JjVvTALxBwdJ3HGoh3TGzSqwNvEOTU7yHrVAvb5XbgwXvrbi\nq4VOhHcQRxEkDZE7dceczZQRg2iLqxlLCgIS+uKwRjyS5oLLG/nD9+yP5UqbVJTSIL2SBo2diJOU\nToiz+MSwcurauiV248SuvE/m8CmFlVJiFE36ujjsnq8TzNbQywqR5MYrk0+pXPYio9HbB8wNb8GR\nJ9bvqHkJXuE8ZCIxuTAeErz0gfkHIQ9OTUy/WVWMi8VYG9kmEVGpkRwsGqofROysDn3BusPvNDsr\nwKywoj6NlQm9ZvibfOA5yKirc6qyevnBzM6y1vBTctbdZ2T511426S485E6G1N9H52eiTeTOcZzS\neXBxhlct74bLjstJhBCZiBmewrYGweJzbsXDvXUOnUC8Ye3uuVorTL7OOnTdRsd3+0yXg7aEaUHG\nxO35g3fLj+JKC3UwEZJsWcOC5oRVHGd6MMRx2xkUk1asaNqrLUy2imTPVlRuXaQvxDqfuLH2KTT/\nQE3wfLHUMBWmfS1x9t2UL4ep8rBBD6dH9U+3dI5IcnVUkvQOayK5M+P3aOtEpmPXqzS8S0g/SD/Z\nloOcXCagRuYbgfG4p04rBysHoPQcLBroxSUTs0IvXZGkDFYTeki1iMRIPVDe8XSu1lgcoMmSF26T\nS5U9e7l+Y7FiEfJkjYpHdanMMYkXTMMpFV1ko8dBI3Er79zwErecFd3DRyRTXzRvaE5eLfCWGANj\n4BmgL0QCfQ0snYmz3SbJDMUtynvGr1skEzB1sOSiOcz2zuZPCPhoJ913woFwlr5j93vNaXTfufSq\nWKVUCEekQzu5VuLymQfOK52nbpx+YbIjWT3aZKfRmVGahxeTXc57HFo/510rKOMhD0ZcuEsFYMTi\nXM5IitdgH8SEJo1TJtiO+sYrnebGYzWyLZYuttyY0ljtpM9q9zzbxYeWDf4Q2O4ZYVEOg5NOs0mI\nQVZDTqKTOiAUyXoNXJwRD1yDTZIrQGx8r6TSyU14PMkl/MQa04QVDctFF6lbukDzB8Mvkg9m/rTu\nejfIukm5cId9YcXGSMdQjm9ep2gM/+CUT2h/p6P8vmjZnubObCfbUmDSfgHCzI9i0yYwInBdWAr7\nOQpVr8ojHnS0YB3t5BEPDrlFAd5p+oHfaCOVIC2YHgw50CbgQgRsrT4XytVBJ59QvorwoqBxPTa6\nOhe9eFz3H7lr1WNzLtQWVyjqi7UefMRJHsnioEnUVZxOz452p+sHr/EGkkxTxp27JWuiLWkLuFuU\nF63Gm7IwbcxcNBOad6YsugR5DlQ/EAbpcuco7GxycerCY0OngXpVo/Ji052LIG/2kGVdYZEHwcmU\ni5WGqaHeQM8qlxQ8HA1F2hsQd+TVi9WM05QWnSsnvpTelDMGe1R5UNT2TwyBmco1O8/tABFOOxAa\nQyaLhv8CE7E/Tgr5PwP853/oW38W+PeBnwL/NvD/3d//9zLzj8zLFcBJ3hA0e4kpdJG5CDYygqdc\nHBUH8v0VsOtH2XKkxre48GklX1SY8RtMaTzk4OyOARKDlicibwjOF2k8Az5a4vKGx8kIwbST7WRe\n5SrNWAiDphfreqD2B8zzyci/ybgGeRXp8CUDV0c0WJbI2vBNWKt8WLoUbw1L5+hWGQ8ZlGgBlItO\nIzLZvTbxlUJjMZh8BXZzbD04WGwC7yY8rnXzH5Iek8sClY6LgexICp13JOv3WdbpLiw+6DyxWMxW\npshmg4PBjiO+CBGu0XhGsG4AClqMWbVX4UlzkE1wuwi98CxhjjFYMm9VlzG7o7HBtZAt6HxlxeAU\nw/KHRzL9cQKd/xfgzwNIyZT+b8qN+28C/1Fm/gc//Mnq4HTFhloifiJrY4oTKpzNyzAdqxIXs660\nhw72FE4dbGtx4iyE4cbqi5YfYMrTlZfdMjgxLA4O1dtW8mJ4Z0lldVXLpub8/XaI9jROvXAKoMZ8\nwwOO1z/CV/+KzQfP+ELqRdfgGp1tTvRZ0rzNfs6+OjoaEY2jlVKtu9NjY7WaIC02OotDFQ/HLXi6\nMbUsSCYX6beHbCVHa+iq0/pYjaWG8+KSz2w4EjV5skjioZz3m/CRF1PvTDGtFEtTI9LxnOwklzmP\n1Zkm7Lm4NEh/r/C9DCTr8IWVNV+jRPSW+f1IVlyQKMJOXdmLtONSf8uVnS2Fle3b+/YHrV9WefAX\ngP81M/8P+QVmyN9WSiIybvXPi5SixoRs/CQXX/PBnqtaYXrS7027pBMkD38VNM3uoI0oRoHHzlTF\nmDzcCH3nlE/sefE2O9e4ChvPLJ5CDpLO0onkRru1iS9PzBPRg09LmPl7MDfO+L94zOTwzimL4DNH\nh0/5GT4FLr+FjXeO/lOiwXehtGZ8Fy+mC65KqKPUqX5I4NpLixFBt8WpwlTY/WBEL4ZZJj0bsZwl\nDrnjOCulYufywjzqDa7GR4CtRt637ZdubHZAFpn7ci9drRWxOzJ4zODoQfPAZUflZJPBezSUmiim\nTjxbkW/0ZNFYsrPdY+lIr8TMLrSYDGnMKPKNAIhxSaPplwL//cD1y+oe/BvAf/aHvv53ROR/FJG/\n/A+CzwEkifvE5IMrN7CTMxp7vnhJKaeSSaiiGCIPRB706Hg2PnLntJ03Tyx2InbeFVQbu198qLBM\nCXljD+dscKpjGJGNoZPIjcsWZpW7EJUwy8miOWwq2PoJX/nE+/Fn+H8d3vMT1xJMHVNh3y/+UYLt\nmTyG8Pl58pN956fW2O0zMXZefSPbZz5GZ4gjYWQUiXDlRqYXE7ZVxHzct3xBCFs8IkCFoxvNQX2j\nx1e+WlDyk4XEYkaj50R4sbX3Ammo8lBlT8fWhoRyaKsYKhGGD0YIM5OzNcyDJg305wRwqPCQRXbl\n7BOPALnQFWxLOWWwreJAhDguD66mzHSExgwDAhcQLTp7KxA/tF/hcEFEBvCvAP/l/a3/GPhzVOnw\n/wD/4d/ncb8tIr8jIr/j50linNlAF8ETaUm2BvICE5TBtA4CKycrJ5fAaoGqsy3nZcIhF9pOdk/M\nT951K3CH5/dKLKFYBnjVUZEbOyd7BD22Al1Iw2LHYkeAdxLk93jE3+Fz+xv8NH6Xt+MrakH6nyb3\nBy5/lq+f/kmi/7P42z+Bj3+K8/ln2Lc/xf6mPPeNrT/o7Qt/Oh3JzuoTK+AMDz+ZKrRMwg4+7GCR\nbFLWmBkXXyVuT9YJJKcJrm90rbImpaHWyF7qsT4NjZ3Z1k2LgDONSzZOrMjlVL65aGIRDFVYi1MC\nccB7TcM0WTg6kzEd1QbxRDX5asqWyWn5/RBDrbQGgyDM0Vw0Bp+igvbijnOa+iD9V9vy+peBv5GZ\nfxvg2+d7Y/4nwH/z93pQZv4l4C8BfPqN38ydwvjEHe+u6Xg8cGDoFy59o62Th3R+rt9SyA9iCdGT\nKTubfLDFg0M7nyO4bBR60yt/QCOxUFSD6M5SwVIq+DgHI4OlL0hl5Al84wQkSxWuxs8YtI8/x9fr\noOvv8/BF7n/AJwf/yR+w0WEIQ2DZ77HlYO7JmwmrfcIiIf4xvo6A6+ekr++zCpMaySZCXoP/n723\n+bksu+7znrXW3vucc+/71kd/UWSTlCWSUmzFskAgcIDEgYEkQJCJkUEGHiQeBHAG+QOSWaZBhvkL\nkkwCZJYMMglsGEYQyAhsxw4ZWSJlmlKzu9ns6u56P+495+y918pgn2oxiqQUIdloAj7Ai66+dfG+\nVbf2PXfvtX7reSw5TUaDIOjc+MwuTseJWAgR5mP0x+hcZaF0H4szG1YT1YBwjMHCBSjaaT5RVVHr\nrOFMUcdUtBfEX7LazLkXdmNENqtgWhHNeBS0POLVEGnsmii9gQclCeJjg9oVaD4sln0EfV6RKls4\nmYyGsPQL/qrx8RrXn8X24K/zU1uDVzik4/oPGMSZP/FyBgQDHyHtIkN72Y+FHDEz9TEpekGYCCaC\nTU+slvF9Ikmn9duBCG1DbOyx4dqoGCKdKYxqQpFGhGKt0VCmqpwZoDYNI2FjdFIaSRqVxLwq4p/y\nbF15Jr/LmfdJvfGYhL3+Ap/Kl7mvb3Jnt2x+y12d6EzsMUptuysiMzMd7J4id8x+Icv+uWi55QEy\nPnmwJqV7kNwRH2zDnhTteUx66IVmK7EF0To9Gie5B4bVp2wXqirExHUaGYTACIy9yfACy8hhYAsp\ngiuGpE7IU5YedB1zYFZ1GCRrJnwI7agLKTpNRjSzy3A1JO+ICqLD4l5So9rMQQGhGGhA8UzuI1a6\nMdN/Bs3on+pOKyIn4N8F/tOfevi/FpHfYJRf/9kf+r0/8lIAD7acwBdS1DHunTeiB6BsWgZOqFUa\nA8BbzNl97CmjJVTbeCengFYgrexuLOqsnLjKRhbDQznFGJGeG9SkA15Mxn0ME3oLIo/TdnFl1w1Z\nv8YnaceuX6O1CxYvOdVKTL9PkYkWO7cXoZ2unLTT94YUsPyEJMHejWQTEr/Agytz+mfsLYjjwFea\nsEujqbBEJsUYp9nTjPkF7zqmfL0gMdBJXnxkIThhPji34pUuMLPTJDO3Tg3Djq5TseE1Uy943jg1\no1oadWZNaN3GNoxgShXz4BpGxIlk65FbHs5iojD1wNN4g90hlINhEBTWJpyisgqIjskQNaOi7LVh\nrkzq6L8ooXNEXIA3/9Bj/9HP/H2IYWRpncZONad74K7coOxWSW2iRaIW50kdNJItJhLD0J11B2J0\n0Zh5po1dhhbp6kqWsb+qVEQKe4zhwYtspMhYbGyiQ5oXjUUa98cAZdJHSld8+pi3L3DPCxBn94U2\nC8YbNN3RXbibC+InooOlBVWl+xOqCX264e0606fv87UNtu48MiDIAOo76IzaOgzenDG7UPyBjrGn\nFfrEoo3dyxHWES6pYW3CYwGrmEDViRpBlisR0zgbvPrnjk4/pge0Ja4wcPwx7qirddZYmKzSvFB1\nY2rGnu5wFlrb6SJIKGZXqjPQpVY4hRKHoDqnTmvGRQc3YU8Zic7UEqJQUsdj3JnjZ6AmfiE6YoLg\nsg7flVasZ0QYgAoPaGOcxehYjOA2gPTAWPGkQ0EkjqfGmSsXT2gt7LIz3N+DcRtupNaQVHmIM0/o\nhDuPupB6Jwf0tNOqonrsnvppdKvqwg8lWFyItmHlgdsm7POKofQcFKl4vmchs+WZUyTCHikp4wrr\nfEbXX+ZHGYp/yL5voysG7JrHmLXPTGF0uZJVB/tKx57TdGMVwWTECjdRZtfBZbCd6DKYsl7IjI9t\nwrmpwuVgC7hsJO+gE+E7PQmpB9iVqyxMJjQ/DmF0qDNMfvDBoKdBnRTAIxM2OAZ7jEHF0PFJGBF0\neeTUn7DlwQoeZb7RIOpqBwK18vo72i/Iog2GZj03xaRQBYoYUleyBp1OIOQQ6PvnfNoWMtq9DMvh\nqk5qY1Q746BDT9TrArYiZJo4yaD3iVvrwy92hHVQ5ZHO1M4YK+W4A/bUyKuS0z/ll+8Kj/IRmxlb\nPONlmjF7EytXbnSiTkHhGY8nIeWF+7lg8hzSSp5/kWl1OH2Hr17h4p/yqcrnH41elZoXct9oh5Ph\n0hSVK10KExf2uCE7IKO2vEYiQpls5yIZkQ11hd65FmGhsRHcZ8VlTAdo78QxYGiSiCO5Vs3I0bm2\niVN31jKSaQljk84plE0GdG7qjMkEN0QFJ9Mjk2UbQZnxyhE6U51DYzUNkpAMoUhoZx5VsJHVeM3r\nC7FoIVgiUFEeo1MOYIeEswFJE6VXrmJYNMQG4MxjaJpaCE062cfUam9lKEQ5EeqkecUDJJTSGlWF\nW3F2CVpLlKioOlfpFM+I7DhG7aPgPUvlo6kzvfwWL8tL5OLM/co03fEkP9JMWVTY88y5z+h0QZOS\ntHPqhf125zkzafuET+db9PIX+LEpZ/kB837HRcdiWmT4Y7sOXliJjkvjmpSnrmxxYkYgXajN2NVJ\nvtGjcImE+o4iNAsmgRobrRdMBhDv1QewuNJ17Jc9MtmGJ/jkw/Qu+sBuQ9WqkYGdaVeaNmYy1S80\nm3EPJrnifcCdZ68spXGp4yxgCo4Qso6uXJqInoAL3ZSEEb6hh+70da8vyKKF2mwwrrzSNCHRUR1U\n7rWM8tUcDfrCdtxpxTZS76A3dDfOfuHBwGTlos7c9zFA1wYvtSAj3B1B1YRHBTV6d/aYWOJC1THR\nQKqUNt79G4nn64bqD9FeabLRU6b6t1i10tKbmDo5rmxTQ2Nm6cI9X6LNO7l+lRdPM9luKV5J8/d5\ntzr9+iNepolyEANXm/ConLxzDuWaILXCm3Xjok7SdgjubmiMoLcchXxD2MQG2aZnuhs9zaQDBVV7\nMOXx90mR2HQj7zdc88YUgmqlh5FiVFZqqpQeDBXfgHaoJy5i2H5iF2FJI6q5aUV9mCD3ekKPPG14\nGVO/3cnm7A263uMqZHe6g0sMfMDPG2EmELI2+jGCnSJRPeF2RUodkGKE2gurGeeDzy9euJYg+UqJ\nwksSrsO4OMeMS6OykA+GQLRgT4mJzh4b7gnsSoigVJpMBEoKaMw0O/6Ra+Mxn/D1a1zUyfERU79j\nKT9gis4UG+43hHa8QSmV7ELK8KQb281HPO1G2Mp1OmEvf4kPOBPMNPmEQzZG6RtFE2sMiXMPQ/TC\ntS9MTdhtyEm0Kz0NH1dIh95xlBKGRpBshOq1N9wSxYNNDo0SsGkwdeNxWgdhsitNATcmOSYZvFJd\nSTJRDzEKadgXNTVmAqfgZUOboNZJPsDLD23cNWfZR3CchRWYBMzHNHUTR1TpopQOruW118sXYtEq\nQxjX+0ySOk7Tek/EhPRMs32wT3vDUsbX8eLHtFP6AnHBRTAxtAeEICKDAGhBk8o5FDSIGHIQ7Qt7\nuXDeF7J0rgbWO2EV9zzSYP7K7fDIs8sJ9LdZ+ol7f+QxGd7ewNNK2SZ02sZik+CxP6PmROlfYU2P\nzPUN7qeM2UzazpzK/83XL5kL7/OZADG2Bw860aIOxZJkSq9UzdQcY2E6hNTBw6qFnoVAUV1BpiF1\n9oT3iqsND8IeXLOQWIcfjOEcg4yq0zwoOrK2rtCbUKzRJZOtsgXM3nBtNGZcg8w4kLk7zY3FjdBG\n04wGLHa4KqKx9cpJOgllM2OAUzvNCtmHegB19OeNexCA7oKlMdu/JSORWfpKbUbuQeTGpOPl9ul4\n8WMazQdR6iG06JIwGSdXZUJiHSh8hy5CyI7G0HCe6pkgWBNobKgWnGHTVuzzwcZoz/h47nD353mM\nR5CN2/3CJp8w1+H4Stqo+YbzZvT0E272wl42nu0zcS4s7mh8mX0qrHe/wot5oq9K219+noqatw7F\n2LQz1dESTakydx/Z17Tj3kki9PSIxUxTQfsTguuY08LYLR9ClcaWhckHhcZ/SnvkcsjrwiBfmFph\njysi0EPp4VgLkjotyqFpFUQG6ywcUGOOysWEuc9oumPVhfKK4xCdksF3R0RR74goXU6jWkElSIgo\n1n/OtgeIo0mo+kDqE6fW6EmpPg+PV2RKV7pVtO9c0yippG6oXDHPzNrYETQ2JDJYp1R4lBPNr2Qx\nrI+9rCAUM9QvWAq2PmNSWDGWvuN9BZ9oadwBIz3w9sXp9k+ZW7BfoU6NviUek+NqbLVgsbDNFfF3\nIDu5fY37SejbV2jTAzk/Y6mJMv0uTx82lnifHxmsryAa5szRWHZhSxuKEnVCIjC9ok0IUzyC5IO6\nmDqgTmuG2SDV5jbsiJGfkNhoyZEG7ZUVMV1Rd5oLk9+ztWWUsnQcYNUnIrdBrhFBbCVaIVLQPaBD\nhBEGuzgqlRZKFmOJlf0oreXe0Z6G5ikcfBqVmN4hnIvo4Eo42M/Qxv1CLFoJGUjzrlSrlBgsrkgr\n6okkEyEvCTeqFOZjD4he6O3MXjo0ZYpOkwWPjRQFMYXeUCt0DTJ9YEHTMJetFCaUmQFqS56Jw4Wl\njPwtQO+ZH1uiXH6JT+iU8gO0Fmy6w0IRXnLWTC87yRNmH5Orsp2U5yJs0njmO2tMSJm5bt/kQ3Mm\nU9ZoNH0x/jp9o0pGp9FEUHkEDaoozW8wvdKIsc05HA+IID7G3nsM2gs21Endr4TPzLGza6By4Pjd\nhq5UVrAFR5GodBnDj4++MdcTNWVUH4kwrgrFR9dso3MT4DFTpRFtRqQhYWzjBwCw6UiaXaIMYIrt\nNOy4eydm1hGnlMTPEmj9QixaR3hQKDpU7eozku9RH63MR3ng5OOZoTLw14AnBd1IbUJ1pTGR+ooA\n0eBiDXRI42bX4Y+1hsWGkrlI4NFoMbGpktMjzdNINmRB63hzmG08WTspfRd1ZXXhGoLUN2BxTJ/y\nkGH2wO0G35/RbgL8XR5DiXibVXeqvsly7cT0Pb5yMaS/z8uuXF6l9nWiyj29TSR3NBJeHOnCQMcV\n4B44Q694nJjEB82mQ1bFw2juNCnMfmETQ/QR/OYP6s5HFiPRuUgmHwb4JIWHFlgRujdUnAZ0TeRm\nmDtiQWnGQ1HULyRRQgONib13VBk4ekCZcIyTBBFGk8B8J6KQUWpMNMlodJL8nG0PBMii9JbQVFF9\nSW8TWRpVlUmcxgIHaOMV2EFZES80gtQnkExwoacFZQSsswZ7zUgUROuYerBC74bFKMbD6C4ZaWR2\no1J856rjzTHthbtkTA9/iRdcmfx9Tq3TTy84h4ElkJlI5xEZLL/HrZ+4lsLZTuxZWIBlT2w3GXn4\nFX5iw8p0kUfO7T0AWrlSuo1qho0pjFYzqo0sbbwp5RZx8JRIfeMqTuqFmoOlbuQubFbJUeg+Wr7X\nuCXFznZkNjQaJrCrkrtwCuWBfoj7+pDotRMuxsSZnUCksSbj3JVmjdSH3CX56Fx2HyDT5Pr5uM2W\nX1LiNAAjNMyNLkZERew6EKg45gnV128ufGFGyP/l9S+v172+EHdaBLSD2yNnT2zi46CgRvaOe6LR\nyaokcepxB4w40aVzasZqV9waIomsnd4rUx8DeHM0lCv9aNVKF5pMNFZmT2xp+HhDFro/kmPGJWFH\nNzK08tZlA/2dkUGVe+6XG1r/Or40JD1jDqHElX0prP5NYupk+xKX5HS55ZOASb/KfF1Z8u9SotP5\nJ3yUbpF62BNbZqONU3cMrq1mxXvBu2F5hZhHSTDqgDJ70FLjtAZ7UbrtyHZil04WYRPBeATPQxgN\nbNlY6sIpJho7u2VCjFNcRzbXE6hSzYcIygHRgWGVUQlwnOzCVWJMOKjQmOhxD6+mpX1i1UBpqFW8\njxr4bor0TNIBIzG9kH4GofMXYtFKBF0rNyGsEYRNwBAEO0GXxplMZajjOTTryQMPp+qKiI+6rjyy\ntzKyC2RaXMESEQ0JGRDf5JzjyoxRxUET4o7FsM1UvYILZseJdlN+PC0s9Ve5jwtZP+Bcd+rpPW57\nJggkF1p6wpMa6Pm7/AIzrQikN6Bcea73ZHEuy5s83H+Djw9L4t42Sv4AgN4qKhMmnU02Ui9E6xRR\nemp4nxCpRBhLn7nXK4nB9NrSNsp1vZAVVIaGyRg5Ws9BbaN+Ou1C2AOXmDEVhI0c8OgnQnYmVxKO\n1MLOhaJKM6d0tazUYwAAIABJREFU4arCJMOlFjocwmp+DPooSRJxOIUjgnNTdmUg9GWlaqbjqCea\nOovu1JrYft6aC4EeE6CJ3o1Ud0SUzWLwoWC0WimICqc63sm7BhTwumNhzNzR+xjJuZpw7juJwrZn\nWjaUTtWVJrdjEN03AiX3lWaJzSdUNryN+a1DcUPN97xz/0jI75BIPNDZk9H3t/nkaVD4Ms/9AfFP\necxBvXyb339bUfkKb8ojaTVePH2bSd7FamJe/glvXpSp/zY/kYz4uAO6Z0I2ehdmXWgyOkwproCO\nOGAXCitVjDOMOTYRFjKpwWYD0DwMlMoczlVnJmnI0VxAGruNQ34XZ24ZF2fhEYuCauVBKujIzU6e\n8C3Y1FlUR+ZYbHBybUj5UtfREey3pCP8Ymzcc8PEjkkb9fgWpKSgK/TCbjNrDk7+L9ZC/qe+BKeL\nsjXjrA040SJItWG6003oaky1smliOz7muipRlQnDRKn5hLRAxJk3Zc87iZkoj0D5nFF77o9sokw6\nI76x+475TC+XoQc1ITx/rhmlvcGLYsz33+AuGjleUPyBurzgzYfg+kRo5cwWT5mqMz/7Lu/umX3a\n0PKUOmWeX3fWN94j5RPy2Td5b4E37zIX7tmPCaWZHQW8wGNA9h2LmXuZye7kgJRWVtLnWQGa8MyH\nXE/EkSZka+y6kOs9Lc1o1IGFOlJeNRVyOxoIydmlM3khdCWkcB9K9hPd6pF8q8w+HMOtGdkqcox9\nB0bENMqMArut6PFm38mcZYjxNlWSg6igPYiUmVrj4rdMuo0pi9e8vhCLNhB6mzmxU6UgspMRdlOq\nnCnxkkrHpzKo2Xacgt2xSMMLRoPVqFpBjKyZ6MZuQzVa6OwiEDPBdsg+tgFxjsQuQQ5FVBHviAjL\nkcDfdOXNrZH5XaCweedqE3Z5zidPlBbPeeKZnD/FZUYff53ff64surAEnC5n7p91JL5O2jL59jt8\n84PnkP8xq5550sab8FEXPCqlzVR3zGa6vXJSQI+F1gUIegjqiaw7exi2dzQP8IfHRJHKZVqwBsnH\nXTBkSJPnWLliZN2Inul5+GnDFqQvFF9pciVLQnxHw+ip420QeLYIJm3UPg8dqz7SWBDfcDl0Vgz/\nQrOBp1/6oKm7BFdRcleultB44GLGXF9/T/tay/sYBf9IRL7zU4+9ISL/q4h87/jv8+NxEZH/RkS+\nf4yRf/v//wcEIVALhHfWfMXN8RCCK8Q8qDIOoesRUumDq2qVTXYaGdGG4SgTW9oPL8MIiHQSe1SS\nVbpMhO6op5G+Zyc5VHc6K0imsnFR4aJCqmc+PN3wQfwGn8aXR6zOV9LpI87+kqd8QO6f0aNwrp00\nfZdf4Ls890/JQDs9clM/4/n6HpQX7Je/yHvLL/JSfp0tndm5YeeG8I0dJcTJ2cAzfc9MnujimNzR\nZSeL414+Z+yKNmIebd8iO85O74F5I4UNrgSJFistVqokigtJM0ZH/TzKYE0R3UYHzBJbh2rnETbq\nUBA2VVwNJ5PlQi/jk1AVukycPSAKRBklRR9jTJtUJGwIr9WQFMOVQeNZ7SOU/me5aIH/Fvj3/tBj\n/wXwtyLiW8DfOv4fxnTut46vv8kYKf8TLwkw6wNzLo253bCFDKugL7huFJSkUGOhRaZFPkoOgrGT\n7QqaiDThOmiLIo9YLJyksmpgpug+KhASmcxO9g5xRqyRLBFmOI+I2FC9u+H5nq9e73jDvsOt/ngY\nIvPMGm9yzxM2bsf817XymISH9kt8GN/kY3+HPZy6PeGSnvFJ+RVkewfO3+EXt9/iKd/lzfaCYHyp\ndRYEUqfKRpMdLRWXlVUz3RTjxBoF0k7EQgqoMRFtIKWaZlrIiHbS2OnDicBOsolk04D02WVwu0Ip\ncUUQmgkZI0emdJgO53Bmx1lQdZLu5D5hXdnjhLkg3QZ71wdI2W3Hbadrx/vpCEQVGo2oRvJGBKg6\nPTKXlODP+iAWEX9XRP7cH3r4rwF/9fj1fwf8HeA/Px7/7yMigN8UkWci8uWI+OCP/f6MaGD4sBuq\nb1hSyJUiwd6nkRttMKlSX6XcxYbys4NHsOvoa0ueEPIAIdNwn1GGRKSWIcwwjJZlgCTikBnnRm5G\njzOzXGkHQGJrT/h4FqZ9ZuUFYh9yio7I/WgCdwcpNJuZt53y7Ld5uys7v4r2W6blQ56J0uP71Ju3\n4NNv8f2lsHyWeYyPCPns+Me4jInj5kwmiG7sZG68cGJEFZMMasvVZ6p1JCrG+aDiXOmiKBMXETSE\nkivaMqs0yivOQ090yTS7oHFDi0ams/vYQkVkpj7QoaO1PKMIj3LB+lNOdgURSlciK77HWPyMyeV0\n5JBbKGHBRZRzrzRfwHaCoLkOjnDuzG2U3V73+tPsab/0aiFGxAci8s7x+LvA7//U8947Hvt/LVoR\n+ZuMOzFluaFxSDlkI0TGuIfYgdgMriHDA5vz54FhqQ3RQNKAduao7DlzUyuEcbVAYmVLyk29Gdqn\nWHFdSLFRuzLRWdPY97VWqOJY7FQX6mFcWeKOL20rEb9F0olru2HPgcsziu1MZlgfE64Py0y5/jI/\nesNZ2nPyvBPbl3j/xjjpL7NcnHz7D/nVD2aq/Z98auXzwMzehCrKEoXWr4Qu5F5HFqorLkKzhvdA\n7UryRLOEtA2zRvTTKCtFo6QGkanhIwAfbQDpgG6JFI7EmS6N5HU8pzdcB/3FpeGy4AZEJ1FJcktm\n5epBWMbY6C1RpFGZsBRoC2ovx8/pYMrc91HVKVfcT5ToCJU1K7Y5zTL6M9ht/nkcxP6oHfX/Zz74\np2Ed5zfeCW3ClgpzdKoKFyr4kL9tbWJhp1tB/BPUD/aVJx5Nse6USHymwlQdQdkUpi643JLqlRSd\nPRI5ZaQ1UkxglSo3WOt0bZSoOELXTiLRGXeMvgufTM/R66/T+kt6/oibNsptiUSP51TdiOjcPl6Q\nt77Hs83R4tDO1PKSt+934mkQp1u2l9/m96bGdC+8jAdO8SPgGLwksKhM4qw+/GmRdqJBJGHyPLy0\ndUXMmXzoqy5e2O2BMxPZd5pPZAE9klRVZswG6G5qJ67aiWjM0mmSUOmEJXoPso5PpyVW7sljXxyK\n4SRPYJnehhdMBFoUFKdVMOmkMspX5gvqsOmJYlc2SYhxoJOcuTXEZpoL9voVrz/Vov3xq4/9A9Dx\n0fH4e8DXfup5XwXe/5O+kUSwJ+W2wUubmLmnqTFpp7liciHtUFOCunA5oL3PUkK6IxQu0skCJ4dV\nRsBks4LbldzPtF4RAq2Ki3KV4+6lGyWc2ieGs7sxk9jhD+b3i/P2y0eafY+7tCP1DR6zE/YmCx2T\nnRRK7Y37m0Rcf4XLG3CrTzHNWD3zcrnQ+RpvXXdO5+/yrVVw/iGLLJ/LjKNXmmaqXpFYKDh7KLrn\nkfWtbcDySEwOTqXaASDpIBScPOKL0pHWcSBkCAKRo+iv6xAs98A8IwYdpYZyq5VLz0OK4ja+Y2pU\nlKUqXTtbLEx2h8Soqxs7mTzcr2L0Ot7sWWENxWxlUyeFEmxU2nDhakdbEKmypT/j6sEfc/3PwN84\nfv03gP/ppx7/j48qwr8OvPyT9rPjEqwH23TlLJcRWulBeEaaQyr4pMPyQmKK8bXih6OqMYlgu9J1\nYDs1lCzK3G8RNsgXVDbuJNMRUjQ0dlI3woUsK6RRPO8kmjfuzbg3I3rik3nh0/g1evsyro/c9DuK\nXshliO4eObFq4+n1ytPT/8VX/beY/YGZK+fyMU/aHU/S71NvXnLdv8H37S/wvvxlPtF3+YyFz1hw\nPSE9IG7GMKJA1pGsWm0nrLJaIknnzm5ZZaGzoxF0jVEV0Cs6YD3HJISSIhM64zHhMUFfiIDumS7D\nDhlimAtXUUR2rC9IFkSvw5vmzq5Kl0KRC01PGKCyEZrZaXgY3jhcDTPmcghehClAolA8cFWKQ40E\n6kTLlPr61YPXutOKyP/AOHS9JSLvAf8l8F8B/6OI/CfA7wH/4fH0/wX494HvAxcGr/ZPvoKhp29O\nFWh9wzgPHZAmYq+0MPzwD6TQY6mD2CP42Av2kmlxhzKjDFrf0mFNDfcJ7c7NYV+5uLF0GzBmqXR9\nTvFHhpLjilLI7ag3yoUnnvH4Hhdzoj3lIU9MPGH1xmQXbuoLZH/K3ZMVHr6Nv1ExveHssNen/ORc\nqe1d3rwzOP2Qb9w7Kf4x7/XM5eDT6joIL6lvQw4niYjRVFh84yrTMJWbMfsFI2iR2BEmfeSmPqHL\nRDVlkY0wI/fKDsN+eQhJJApaC8HQhKpnkt+DZJoHKYSVjjUlZGJpgceJrg+4TgiC9swGaDgaTmWm\nUpml0g4l02aNIJFpZJ2ofWfDmJloMspx9B20EPb68rvXrR789T/mt/7tP+K5Afxnr/0nAJBApLMz\nGARJF8JHfXXu+zH/1ChMhD4gfjQXaqKWYE/G0jJJOjUVtE+IOK5ObRWNgiK4OF2F4oUuPnSXlukU\nkE95iMKtV1whycQ+Is2sPONTzYR8g8QLsE+5daH3Kypn2prY/ITZZzx7FPqX/gG3tmD9l2jlOW53\nvHu/cj/9M/LtDXr35/jhnCkx8yifQPwAgD1X8p4Ra4QF6hABhrPJDdF3sDGHhgnNg66d3DvRbtll\nZ0tC8o5pQZqx+QnUMW10HwaZwfIVJHY8JvoxMeI2csYEzBHsOm4QrQduO4mM1xjg6LjHxch0qiTC\nt8Go6Mako1mya0L74Qb2l7icKGoHtysQX9F0C3El6s9ZR2yEYjLWV1xPhDRMK/SJpp3Qjhydlqkv\n+HGu0yws4XQ3VHa6DqBHobJ2Q33HEvQQkgU1jOyV1ZRTHy3d8ERhZdfgJjYwZeqFsDvKcao3+TFv\nb8ba32PtirXMNRVqM6xfmHxnjsZ9+wXunu+k9TfYzx1bnvGkG7nN/PjJQq3f4tkalCc/5Bd//IQ0\n/yYvLsblmKmqkelpY3dhCmHHkdRprSM98KRkOVCgNAijigyHQt4Qd1KcKFzZfJgqIim9Z6QbYePn\nhDeaTmQSPTo5JhrO7MYVw+lECNZ2wiZEFELoNJII9bjjhx1TwH4Ze+mDP/vT6TiLoFdB5EyyTO6d\na3akBU0zxCcoJ+JnYMx8IRatIrhVNIISjYdIpNaJ3HEy1juqlVBhFWf2V3abIHQmEbgUxOMQhTSK\nCFWfUOU60JfRxyFLApi5SKA4KjtrN5LFaErECJHszLQDvzRdv86PMjy5Jja/x/0Ftr0k2S1ZhAdJ\nZD0x5Q95ZxXazT/gSz5z8V/DSgbbePeh8elbv8MzM9rjL/Oj04nl4d/gU/2YsBEC130HJkI31lcg\nZxfcElkzLRoSThehkshaOddExynV2MTIaaf1eRxyfMK7cKLyqIn589d7gJyNQEToGkg4LyMPCYgp\nEkLEwhROT47RUM/sKsy+s6pResXoNJ0xGdWZIiv7q2lcWYmDWxs4mwehkLZM00HCiT5BHu3f172+\nEIsWBO2DpOeSWFipxaAfMcSUhlNAAhEdgF/AdCJipTMYWmOMBPpBHNR0j7cTki4kTyOhIBPI3QjZ\nmLL184jH+VggWTtNMm/Ezv0+7kxp/pBv3il39ju0dkv4jnRnDZDiuGXCofrbfDhXZP83kduVrIVz\nTXjP/ODmytr+FWprLKcP+eoLJeW/Ty6d6zb2mhe5QXylBOypUb3jUegedKmEjLvovVZyX9jVDt6A\nAIqK0KujJsP2HcehUoUiBn7saclI6DAoupIdmlVKBLMItQUXKUwaVAJtjljH/QnZHtkkBh8CHapQ\nV9Z8pYcjdSZ0VENKS2PPrcN07t6HATJtI5cQHLHRzPT60zZfjEUbEjiVTKZJQwhSK8xxx6YnandW\na0ibCL1iDCyS+OWofyQigiYT++qcU2OXBF2RdCX7QlMfKqWoLK1QNePdMXscvfk+cc3O1k5AZ7VM\neqVK2n6Rj7XSt8Go9RbsJsx5Hy6FFIN2mDu3e+f6/Dd5hxnd/1UeCkh+4MlD5Y30j7i9CeTu1/iw\n3GAvCz/pL0npe+N18DuqzBTtmASzDyHHY054NGbv3GcnNxlO3l6ZNON6patBGwBkiUaXgT5KDkbi\nGh30D+5m4YMujj7QPZF7cJdG3nhPQXHBvaJkLDteJ3a75wmwAVOsXMkI6zgwt4nEhZYy6dhWhQRZ\nHiHO7N5BguwQMugHrgPmdwHW/GdcPfjnfsWQZlQJSusQGcoDW1uGdlMSvUPkTrTC9TgFT57JVDQq\nVQOJzmnecJRcK2sqZFcqV/Y+FkPyAWsvrdNSZ/PgpiWuOcbotV8wVaIH07ENEfkBT64J67/HBaH3\nE1tROl+BGep0otSMto2P31yZ9r/CT548UArMdSevibsnDvXP83IXnr3xKc9eXMj571AQ9uvYo99b\nRhh702Ds4dc0Ji+qNprAVJWrFs59pUmgDfoM0aH5ma4Dq1STEj2PQ5P7wOQfIfCu426rsZKlUG2n\nevCkBx092Fw7RZV6OG+HKbJSBSY3VEfIG2uEz0MIokMqGMeBj3TFY6JFQzXInnEJtBe6XshdWA1K\nh8G2fL3ri7FoAZNAo9NyIvXj8KAVPSZMJwpXv5JCkaOkYg7IcLKyz2A77gupwT51hJ0gM5GoEtAy\nrn2M7BwxxSmC1RJz6+xpZ0o67sJReTzK2PPlq7ywCcwxf4lOlaeh1Nsf82aauZYbZFrYOrz7WHl8\n5+/xRjnh669xVWVdPuHpoyBf+995a3qGP3yD909vM1/+LT7iEZ1+GwDfL1jYaGpYoeMkCVofePo9\nlOIrJ4HII95nqbPXQtcJkwtEQjlR9hVRARwVZa6Z/cisqnSCO1Do+0zkMyYXVhn7YAM0jIE0gSqN\nUhMht+w8slE4kw/fpdDCEeIY0/mDIcWNCYsrGaOpcw2YENSgYhCN4kKo/9F91D/m+kIs2iDoYhR3\nrgKqV3JLhGV6r1QTwis3BKsGr5onoePQFJ7RdEFjlFnWrEyx492YZOcqxuKNGgtiRvWxBRFxmhiJ\nC5mCsNCikeo+SH4H/S+m3+ata6D1jpeitHiDuxunxDe4W4Q0O/PVSDzy4+eJ3v8Kua7MpweSK+n6\nlM/ebuj2ryFLY77Z+MrHHzP3/w3JwvZ4TGL0CjnY8kTUQctpbix6IfqETo7rjLkPw2HAfR6m83Pb\nuGiiRKPZhlrQBFpTkhYyL5EY26pNdhYyHo19HmrXtS2YVkKD0ne6gfaCyIbUBWQj24Xqyg1XPBZm\nRict0kYcW7Q9/uBj3nrmpieuZrQmFB1ngUGlPALkXrimPhgOr3l9IaZxFTjVRlND+4a2gtu4S1Qz\nbgNmMe7F2NPMBeeCk3TFQqjW6DHRmDFZ0YiR99QRKiecbomzjehc9qBpIcJwVypGSEd8G7leFdSV\nSzIuyajb1/mMr3OXblAxTvkFb+333E7f40Y+4tlF+UwzFy28ebfyXP8PnubfxfpTKme2m423t43n\np+/wVv4h0+NbvPfky/zY/yov2q+zp+fjywrXuKUTdIFsibNuzG2c+ytBi86mRmdnaGZPFDZqGqCO\nVfP4hGmN5BuzQUvDohjjO7OEgcWollYju2FyTwlFmnIxYdN2yNyNNu2go4M1Pr1OPEZhx+m2EXoi\nRz/IlAmLIWCBe14aCG0Mm9IxTVyjDIKlGCJDHTv//N1poWtGw0mRqXoCWbHWxqyT65A6qgKX0YoE\nQiYsNkokdkYPXP1EtsqYlDGUyporsxeclSnsmHxIdN2Y2j4kHJ7J1mkxqg9NnKfHRIGk3yFiIuoL\nPtOZvn+Vh6eVZXsbZ+Y6f8qyNmRf+MnbEzt/mU12bsvKqTr2+IyX71xp+1/i8abyxpP3+fpHMzn9\nbbCF6qMj5gQxoGP0vFFrI0lwxelaOfeRPJM9IbYAzmQX3Au7dVrLTJrGbFdqEJUaivWh+rUj0uk1\njYkISWhurN6hG5vsR002UboQ4oMd0TIYbFXJx1zYUhuuTq7KVWOcR3pHsZHuAjwKuQ+2wmVSlr2w\n5p1Iw+gu4iCK8AlWp9deL1+QRStsVKwXVht7nLklmu5jr8Y+iH6haJ8/L0Nvh0vsHJ2TOu2gU7cw\nelqRtpD9SrQzYWMqAKk0z5SoWBvA4hMT1YKmK/hCEWPzxDqN6FG6/4s81MquiWn7DM2/x2kfW5Li\nt7T7Z2waSP6Ir91d2N/5uxR7m9i+SnfD8wu+8jKxvv33ueXE/vgrfHBa0Pt/hw/6zpm/B0Azp2kl\n94T5hKSxiGpvmDgiBZoTtlKaYSmzx2EL9wQF9h2yB90SWBqGHBnxxFeDmp4SSTZqv+UUL8d8GaMi\nkXtGy0bdEwOYlIje2eKWlD4bw4ghWGlohTChiCOS2dXJvUEbLK9iFzYd2qbMjKTAezCr0plwX4/9\n7HkgS1/z+kIsWjkUSyMAU0mt45JZYuYuKk07LjC745E/n6t3hJwyEZUICBFaDqbW0DbChRcrnONK\n78P/Ck6L0dJVT8wtuJZG98LUJ7J3Lhlm6RxzgJTlHxHXieKfchXBrm9zdxOkemaVE6l8MDgN13d5\n70uN3b/NW5edc36A1pjau7z/9h30b3PpzpunT/jaizOX9Le5lZk1hkrpui5cSqNLJ2KiHuzWqjOJ\nK213siWadJpB9jE2nmV4K6jDa6s6/A2dioqPiKY5yCtx9IZXWKKBBdIKAxo1xu6jJybuaCLQFrYi\nnOKeFmXcravhKTGJUaOxS+fcdRwcNRGvQvp+ItmR1KPSbQd/yv/D3rvF2pZm912/Mb7LnHOtvc+t\nqrqrqrv6Enf70saOQyyMUGQiIRSEQAiJB55A8EIkeOPJggdElAfE7QWJB0SEkECAFAkhnghCGCQU\ncrUdx47b1Vd3d1266tQ5e6/LnN9lDB6+WZWOY1Mnph1VI8/SUZ29zl577bXWt+Yc3xj//+/fvNLl\nTGYEDaoNnu6LHp+IRQsj5zZJp6uAd6oqxZTEPPTwUuniTLqx7mC4ucO2KS2DW6HlCWk22KkeWGNj\nsdHGqcGZatjft3vMb/BwQmxmcmUVQ1zYNIJD64kt7cbG+z/BiecQGn290PIHvNSNqnXk2dYHYIo/\neIdXLoLEXyHLDOstLjf0/Davnivn21/lNVm4236Sb9wkwvN/mrf1zBP9VWBsqtyV3JUYzuMyro7Y\nxuYKQUjhQu6J6olnEli04DbjNDwEamsEJmq5oiGRBK4SRw7DDoYTlCyVczACN0S94OVADo1r2Dj0\nyibznhx5JrlQbEQ6CYbETDbDRDFvdM80Hz1YC6NHDCM0UL0Nf58rPUzM2li1sbSFIgXEMems9iNW\nHgigllhjJ/dM14j4xmRKlxOzFTozaonNFQ1jMZ1dSdoJfUxlcjcuPgDNHiBZoEslMtP7hutGs6FZ\nFb8iljFtbJ7JKKaCiqN2wWXi4ShpKfk3kcuVtjZOCrU/4dnUiP4qJULOZ3JLUF7lg0PH/FWWdSZm\nZ1HHyiPeelkQ/3msFJZXnvLj34uU6ZdZfOLaxwPNbbTpalTMFQ8gbCzhQLFBnamWUIEeRoej2agx\nvRtqjSYzoo1kGRMd+gUc40rcF1PsgTWObFrpK8UE12EBjxYoquP/wcj1SPDRTaDpXnKMzsYkgopw\nY2AR6AmkjX0DI52RGHCPbFKIPgYO45xs1BhZmrPhpPRHLK8/Ov5/fHwizrQgqK7kPgzgZpUUGJAJ\niUSfcOlD+B1HmQCjm1BwVNvY6UokYGQaZ0uklvdnOFpjzcdZvJuTEbopNWQe+IVLCFAXethQz0SP\nPMvjchqvP8G7eoH0LW7KiZjf4WFXtug8DA+wcOReHxPljpumbPoujy0g7WXUZ8rtszF0uPmr3MwP\n0ac/xTduDbn7Rd6XO7L8BjBGmbEr4gbUwWrwSG1O0o5pIIjTLOOcyS1SY8T72AiZB1wMeiMGMBOq\ndDINmGg7grO4YZKJtoJnJI/XL9g0okv7NIiIrKCJ7gtdhgKuy9DGIisnNw6eUQmYGY5j3kf4NXBI\nlc0SVVcWi2DGRTNLG6SgyQotVWK5HSmRL3h8QhYtrJ6IRCarlNBxc0wCQcG9UTAswNSd8z7ym9zJ\n2ikWSAS8d5oYzTJZMhoq19A5tKEj6G1j8pki1+EXC0ZXeO4LSy+INCazEbMXn3M8j2b8lP8m/aJY\nKdyHiK+v885LzqKf5joZfXrAwSq5RC6LovURdyky+UzICm3hOy/P6PbjbAdlevQ9XnvrhuT/O3MN\n1D7G0t5HgLXR2GQMUQKRFgSvE8RKs+EwTm3Gw4YZRCuoKGoTGgT3hjH6sJIctjxoMftYuqkNdzMT\ngRVpMy6VRtsnYY2NhcUWuvU9L0wIrFSd0DZ4Ykc3qhiinRY60hpmR5b9pHLvncRE8jjKmR7IcqF7\nJkqlekYbiA5h/osen4hFO9K3I8TCaonQA0U7KTjZjVUjwXwkbEdIH/ok1fEameI2tLIEikRmrYTd\nPxWassmKl4QEReWC+cxBK61PTH6h6YJ6wKViAtGdVhfu0niTw/WnObHi8TukcsGPb/H5TXm+dG4l\ns0qAdESDsjSnpWccPFMz3MjE+Wbj9fOZ9eXf4okeudz9FL8zR/IHv8jTcEfPXxtPZz0NuLBndKQ4\n06SREHrYCBYIHhCpZIt0HxM/kQjUfSEmhBn6YNC2tlBlw3oYpEUgSaQZNNkwUYKdqTpjMmJA3VcW\ncZyJEAdIwztIyCzmKLBJoLnQiaS2klqmpMDkHffRKlx8ZFho74MfIYrWGQ3CZo5SQUfFbfZD1NOK\nyF8A/jngXXf/R/bb/kPgnwcK8DXgX3P3Zzsb4TeB39rv/pfd/c9+7GMAkUaxNhRFKLlnmhVKiFgH\nVKjacV9Iu/St9w1PQqsPcS0UFSYfXiXFKXVjJqFktjg2ZOZOkEzzSouVbAtujosDwtUXkj4nKrx6\nGi/+efotwj3QL9wHx8pn+fbNSuqfp2kh+EscXbDpvRE12j7LdTrRw2PW1Ont07z34Ar+s9QaiDff\n4bW3ZpY3kRIjAAAgAElEQVT8y6grd3YezycMw2Kz8VzIkLrQ1Ak2xN+rGIiwRiPKwiqV7DYC7UJh\nKG0nGoroIG+HPSmcuMeM7mPsSuZGOluYyNIxy7ReRwBJaAQ5j5hXiZAYc3MC3WQk0mgkW6HlQCgT\noY/U97zn3Jqt4AENxuqRCaPFEQoS6ZSkTL3SJBPth9vy+q+A/wz4r3/gtr8E/JK7NxH5D4BfYoA6\nAL7m7j/3wr8Bg9ng0Vh6ZLNBBTevhFRIXZGgBDLi0Pi7MZbiC7UPavbilbULkYbJBO5MKnvyopAl\nUsKF7hPRC0WPLHrPWTrJRjhxJaBeER7QrfPurpoOd1/mrB3C15nKhk7f4bYeKYffJvTHpKSENLPZ\nSyTu0fQWc6sQhbxO6IO3ebJ22uHXuE0P0btX+dbtgeV7f4r39DlBhzRR2saVbQxU7EIg7xSdQvLI\nqp3IBK6oBAqF1DqqgRYW1E5ES7R4wcjMltlE6OKECMaHqTMbPQgHm2hmBJQYK80bPU64bwQSoQsS\nhG4jailowdvIMo9S9hisTKzg4YT1IRrf4j5JbLdkGmYjvLl1JXrB1Oitk7YbCELxTuo/xBTy34su\n4+7/yw98+ZeBf+mFH/H3OASILeC54W1viMSIloWWzni7xWNHdAQ7t72lEmijZZL3EajEHZ0+5tzm\nMzE01BMmHW03zDqa93Bma5nbVqgpsBFxX4nR0LpAOnE8jfLgJvwmIhPWN85Zob1Bu23Ay0RGJNKt\nNUL+Hm1z1vYKnmGuj5F4Qc4v8/TlK779NEwNDiuvv38ipL9CInG/t7w2TmBHomw0SSN31gpBM26D\nNfvRhkcGVFklUK2ickE902Il1YeUWLlKRXtAUsfaCOoAiHFQeRpGDnBxodsR3XMY1CeMMlwjdBYp\nXD1gLeNxo4thZRpsW13pdsQ8ssTGpvKRBNJ94xKcqUfMhdkLWxjM3ZAzVjuJPlp2/uKV6g+jpv3X\ngf/+B77+ooj8TeAO+Hfd/f/8ve70uwkzJg2vGZUTuDIGOILbkBy6B0wHM3beF+2mnaKJsddWRBv0\nhrmhmpCwQsmornSdcbN9lJhIdiWpsGmgiZK0EGwYKDcRtN5y2uX01w9+lme+odOb3GwXfHmf3IaP\nKtVIBGwGOT1E3QnynNuyoulKtk9Rb9/j5XPm9PLfYJKH+OULvHXI3D77Bb6rT7mRrwNQ7Zakz8f0\nyjPClWOL3AdBdMa8cJArpgu1Ca6Fq2RuexouhyRYP2J6YhEZdqRg5DbT4wq78OashZAc7wE3J2kl\nMvTMBiTpiESqO1MXLmRUynAkSCDVCdSQuHGRwCxXqisbI/2cfSKWHdygRyXibApiCQ2CmNAjrCZI\n/3sF6h93/H9atCLy7wAN+G/2m94CPufu74vInwT+RxH5aXe/+933/XsIM49f8R4zA/I9D6iGGO1D\ni4wGsm90yVQdHnqA1Bxix4gjqbtmVJUmkU6jtcytdEwjvje3mxnIWOjJO+zxmF2cVTpHj5RQaNq5\nOe2JjfnX0OtCr1e2ONP6K7TpgtojlAsXP/Lg2vD8HrImenmNy8Gw7TVuH54J2yPev3Hq9mV8bnC8\n51PvQVz+bz59P9P2l0dEWX0wy4RG14ktVbRXkIpLg34cGH42xBeiOVteMRZyWempDtqigXumxyve\n1zF8YHQpEgeonarOqiP10boQgwxUvhQajgajc9jDoAtVFlIZrcahBYksdaLGO7IkTDKhZWyHN7cA\nXSq5G8EH7Hr1wmPguscRiCi1OZF/CC0vEflXGRu0f2q3jePuG8ONgbv/dRH5GvDjwF/7uJ+XW2EL\ngzxNcHozPAWMYTnpPTPLSEKxHVekOeIG6gNmFmVsL6IviDjsJcMWIGzTfrZJdKlIUrQtiJwwnxE2\nYnAKjW5wsybu0xgtrpd/nA/iB0j+Gg8uBvM7LH0ipndJbWLmKS3M+PMjVeFB+DbHErGbleS3PA2N\n19eGzb/BjTn9/md592Hj+Dv/BO/Gp6Q4ak1tFeVKZUG0IiK4CzLO5WiPNBkhyCZp5DPqivdOAjQE\npjbOmBoiUhuh6lDR9YmadhsMhcmF1KDNhm5xjwmNqBeCDKyqWaXKZfB/LY0oUIkgG5uOx7JwxXwm\nWcTZbfkf1qcRsjjWlRbvkf6ESQvPeiepfeQq1iis/oesPRCRf4ax8fon3f3yA7e/Ajx19y4if4yB\n+/z6C/xENoFsE4WB80EyVpRZHcOxMDZsRuO6h8BP5gQMR2iuZAGTAU8LHkhauTIhXAlxBIGYVLIq\nqwsqJ4I5HupAALWG+8xRKuvsHO/3DUX+m3DNWC1cUqb5yziNST7NNTpZA3NvtPiMo0XO6+e5PoHu\nD3mCcPAT55To2x9HZiE9OvP6UyEs/xvp7oa7uncPPGDidFbc+viABtCQhgJLLmAZ7wFJjtczcCBI\nokRHLDPpuET3buRUduynkC3Q21hMRaCmjrSJtI341UihdyUhbD0wiWE2E73jOtRc3uo4K4sh/RHd\nLxRV1NY93OFKZwjtAWhKnxLqEe8N8wsJQYNB2Oj9iHUI6mT/IWoPfh+6zC8BE/CXZCjOP2xt/SLw\n74tIAzrwZ9396cc9hjP6fkEqYhV0IbgxhZUaHPNAjZ2+BSwlUh21ZlWnunLAuAhYMJxBB3dpiE3D\nI9WgSII+FrqrsbSII6xxeJREyt4mqoMu0xvnZbw88vRnuJdGCd/i4bUhN9/mwC1VZx404zo9pmik\n98eEUrCHv8NLXSgEag6ozDysV8rDX+E2P0Aur/M7Dx4ib/1ptnYl5l8D4Lk36BOTCCJnuhqrLdy0\nE5qUc78hxAu5TUhTLGQsjPSZue7lE5BlpYVEdyG50fojLF4/cjGjiljA2VB2Y6k1VEcfOMVGtQix\ns7Uh7HYKRyI1OOKOyDMkjpjTqPPgczERPVD2h5kJWOskqbgOeaTVSEsrJsuw5rihvdLTD7Gm/X3o\nMv/l7/O9fxH4iy/86PuhwAK4Ky6KAdqFk2bmPsI8QkloH2mLsgPoZnWuHmmtQBbUhM0rQSCMzBkm\n6yPayRUBLArFnVlGHov3AOp0mcY0jhFRr35Cr2MXfJy+hm+OtisfHBvePs9605D4adoMc7qD68Kh\nf59rCvTLl3j+xClT5klLCIm72yN9+wVkKeiDd/jS241Nfpn35EAvuzLHhKKd6NBt4qKd23biGjOR\nyBwqjYlr6tzWQPGItsYWw8hIoJHERuKMVHqbkdCQcB4FlY4G/uQrURaqAJYJVmkxoxa4uuPdQY25\nFoxM0UquwkX5KENX6ISWqaEishJrpIti0fgoqkKMQx1DnsZIoUxSkJ6YOlR3zkGJRMIfdnnwwz5c\nBPFEC2eCdUJNZHU8NLxnYnJaL7usMLJ96IJrleQdT5B8RtpG8humdObETOyd855IXgUWW9kY8/si\nC4ELk+zsIWtcdULEye7QJ9Z5NOPX+y/zlAbTd1i2O8KD7/OkK3fL28TpBqmP6AnO/oSlbvSHv81j\ny9T+ecJU8XTDw/aU6+P/i0fhAen+x/jmw0D87p/iPbln0W8CUEsZoA5XUugczHFdmKRQIoTWMI/k\n3WofJNF89HR7GATE6M7KRmgP0LCNxWoZlQuy0xk7RxoFbCbFwlVkLDQbtnP1ha5X3B6Ar4hnLHaC\nwyaN2SoWRn1ND3s6uSDasBbI+0lTzSi501ShKYJRJVJjp7RAikY0oepQOrzo8YlYtGJOc8dJiAei\nBs66MnmiCYQemfpKDRlx+2i2bQGqOXMfuk3Ux6ZLIlODHhpqEWUjW6NKJqBIndF0HV5SubJ5JMoI\nIbEIlY2ZzsP7vQyZ/zafumSsrHyQlH59g/bylcAXCLoRDndMF8HsSpmUrXyJtnQWfY1sQi6J08MZ\n236Od5eF+fZtPv2OE+Vv4anhfXQPajRCD0QTXH10ALShBFJtVEtMvlvp3UEuSJ0gBpI7LW3QHDcF\nLYhBlsYWBZdCqA8AaKIj2DrBiFgO+8LcUJuJciFbp+uVYAYegYZJJmFUU4ILrsLch1u6BiMTkZBY\n94ml7Ck6gyBUwEfWmJeZSSqbNZYOFiLZzy+8Xj4Ri9bFSXHFrNHTgc1WZhv4866OW6PGTJaB1wnb\nPiacnDHdDpiEMdHqutuoOxddWLSykRkjjOH+DKHQLWLNYEmgbaS4yAr1SAyRYon1Znw40rOf4v1w\nxfNbpHrmePwmN1uCm4maj8TTp3iulZiF5SToo7/D6+vMaYFDuKHOjziWit68yVEdTl/kuzeK3H2F\na3mKhBHJlO1K64WqhWBpBM/awOsXVTQEktxTywQhEsw4Z+VBX2lpIjLKi8UTGwFkpJq7NVRvOcfR\ndTmYAkKSDfMJtU5TH3pZ6XgKaB3Oj+KBoBvVhBwMcwNZcF0RMj02il859EwJDt4Yp2BI0rhoH7po\ny0Ste7Zxw4KTW8KlDyeGPHzh9fKJWLQqvqONDmxWyX5AfKUHQaxRw0DYe+vU6NTduzWU3kJvHR/Z\n48R+oduQviWvdDIWC60cIDRCvCDdaT6T4pniCelxJOwwk2NhswliI9dhOJT52zxcK1Ial9BZ18/T\nbg33N3iwFfr0Ng/uhVY/4DqBP//jvPNaQPTJgBl35XRM2OULlOQcjs946YPAEv86z2Vi8xMAd6Ui\n6vQopNowH52J4kNvAEbrCxYHcCSGRO5XSpjIbaUQUTJd2751GgARzaMPO+8i8GuvLGJoEzRsIEfU\nVoQB76vbkU2vdO2EHTYHNhZxT5BXtAec0TvOHmghoL6RUExGjX7xw/hgSGDRSnFlpmGaqTWQwkjR\nlNZwvb7wevlELFpHkJ44a2XqE9471xwGxksVJdL8wkUj8QdqJgFKFSb3MVFjwD5SAdeO4qjAUgFt\nmCvdEkUDyaBLAhZm2SiioIFSA3knkG+6X07PP8W9X4jxW0yrIA+/zeNroM8bh/Ay7fKEc3DC1pm2\nhr/0a7x6fcD5eGEKN1S95eX7yrPHX2Xuitx9mfdjIdVfYG1vfdSMl9C4hk7sBhTwRPFOlgrWadww\n07l4R8J4zXIYmE2RCfFA1Q49ETBCdDwsxLbStFP2VMRbZrobWwQ8Isa41KtzcoNQUEnk7gg+SgvJ\nI3tMBlKpuWKsZA9s4kyyEdW49o7KWFZzu4dwpOs69ibiXEMmmZNko0mgSGcm4PIjRk3EwTEWi0Rv\nbNKZmo/pljvmjSbCASXhw8bBqPcsJkIbKPTYEyePhDis0+5wsCumSpOOCqQ+AvIGMzDRuY5cMVlB\n6khM9ITEjeN5b3kdfp2pTFj9gBJnTufPUV5xmn2R164Fy9/j9i7SS6EmYz39NL/zqcIUX6arMG2J\nt5cFbZ/jJgnl5hmvP3U8/jU+iIatYxp0lUBoFe+ChUDUjSuKWMJd9uC4wcFqZkRWYhfMDigbSmfy\nCaSO0Ou+EiRylcjcndudwVn6StUHzL5S6Yj7QPoTwHfDZ7hQSaisdBKqIzvNZLAPgmSaLXiHFMB6\nwT2SdKHv+uAeDyNo0IQiuidQClWc2TNNEtIvtJCQ9iNGTXR23KcbFxk0Pg0b3SNJhd47wmiLnUlM\n7IV+HyqoFSGZsqmz2Ea1A1M8c4EREmfrWKA+erTuRhKj7sypxvCmVYMQoSqkEnh/2p0Lz7/Cxa/D\nLl1Wlttv8OmzcJkbU5zh/hXOZlj+Psd1xR79Nq/0xNoOHKNQg3PsmTq/yZIfUNbP860bmC4/w11v\nNEZQyMJ7iCUusQyEpvhACu39VaHRLdIREhCCjvImN2rNTN5pGDAIkFUmBhmp01Rotova6bgGuim+\nR5YGGTX1JoGgle4ZUWOTI7IDTqrOhNZpZFx13+g63hXTBbUz0s/k3cNXXAneqK6oKBad7kZ2waTv\nyHpF3In8iInAxQUTGJJiQUKh9WmM91SGY9wnVi1kK8j+BNc4eFYWjGIJDY3ijcmveA8EDbSBm0OJ\nhLBikunEfaAgHMwwCVw0MGthE0G84SifKuPT3+bfIJ2h9TuuIbKeXufdVxvVv0i8BJb56xzunXpW\n7m8eYuVLvP1QEG6hQEy3lIcXtvKzPI9GevQOX3xrovKrTBpou8rruURSOHFD5BwgtyHk2XQGg8iV\n3m+IEcyVK5GlD3dt086VzlKEa3aidSYK2jNbzKRu+N7fHuytp8CCckYlcXVhInDjyjMiuXc8CrM4\ntetI72kHRDLiJ0KHEAMKgxjOFVSpKGGPMxWveHhE6oXmBiTonR4HBSz0FQ+JiUFgfNHjE7FoASRU\nxCYMgTYRAnTZhm9M4m5D6UhiZFIBNGixM3WncyX0ie7QJCJamP2KkChBkQ5iMhZ1cDwooV8oYojP\nHKxylUSwirVAx/gg78C2D36Wi75PT0IoV6bpOzy+U/SRYceHXMrrXAGfv8vD64n+yld5YhNFPk3I\nT0jLMx5flOeP3+RRmNmev8FXD/Cp08/zFnfkOLoHvZ7RmFltQDNMnS6d7IUtJDY7MEen08kqsLed\n3C471TBwyop0ISps0smxkjFcBXTUtNriKJ8QekyEJizOgPRZJ+ZO1GFbkh4ocRuBzrIRQh31sicu\n3phIeBKoA4s/eaTtzgXDcV850lApbP2WeZ/DJR/PTV1YJZP//tSu3/f4RCxaE1ht+MO6CCFPlO4E\nm+hSCd2oacWqsviIDoIdn+6BzRiNdKlEySgrRuQaJsSGLqAibExoXFk9khBMhZIax3plCzPZN5CM\n506g8PAyHictv0K8AnXjoo2yvcr3Xiqk/pO8vBY4fIfDdcZK5nyTqe3HKVFJ8ognVK7lMevhhK6f\n44NwJs3f4rNPZ1z/Fp8JypnT/joIva8kmeg1kuOFTfZRpzrpw5A5FMd3dH3FwxDIVxGiGVkmvBdE\n5gH18EjDkN1RsElC1JnY2KqyiaGayR3WMFpgYGR3Wti47c4mExo7tWa6ZqKcRqRVqORm0GeSO8Qr\nk4+TykUikxsXT/SoBBukHHelqTBVZdVMkJW1/4ihPgXIagQbZw7rZ+BmEEvayMA9ilBUWDFsh2ik\nmlHv9GhEX5hbwmKhmJCk4xaG114bDoRgA5fulSaRDIQme1ZD21Mj75GeEYmc5rFo87Of4Puh8SB+\nnbQ1ZPmAB2ug3X5tBHrcv0YJUG7e5/G6UW7e5FVLVH8DNDPLQqyOz7+O6hF//lneWW5ZLpVrLXQd\nmiLZBdnJCimsdAkE7fQ+o1bpfRnwopBoUtHmSGyjU61DTORMNFvpKSEmZJxNG7FHPsziUHHowqZ5\n6Me80MSpqaO2YWp4n2lxQ/tQgOGVeTW2qLg3CBPuQ5AvVmihUnTi2JePog6DdtitPaNtNnEOhbTO\nYIWSI3OvFIukf4D0u0/GohWQLlwSJIfGQpYr3TOWjGBOrQ4aSL1T2/i1VRpFneidTYQqV44lcU1K\nlLG5c6l0zWBGrUdyOAMB9Qhpgy3QQ6RHiK1AvyHEEU58s7+Odvwmn73ClcY2B7y/wd3hHm9f5GD3\nzNNzpEG+wgeHx7Trz2O3F1KcOLqjcqVypJ+/yCM54YcTb9x/nxK/SgqRsmfJdt+QLqC+t66EVDMS\nCkE6WwL1O+CwAzcqqgHpjvaOqbB4wQmIVVSH780Zf8+2j7/FSZ7ZbKXFQGiB2PvIRxBDbBAnN4wa\nKsHi0EOEsedw74hFwnglRxypKFMbqTzXnR+c5czWb9HUiA1cE7E2aryi7ohA4Tj8Z/bi/PpPxKI1\nho72UEedlUUwmalciRZYEWIMQ4nvM7Zzn7rOBNswSeCOBaGERnJHbOxKVY1miWSNrCtFRlJY7Y4W\nJasjFHobbwBxRENlN54fhlwuPvsCp0mYtndY+pnr4V1uJEH+LktQVj5FjQXNG1NtPHz0V3nUI6v/\nGEFmrMOx3lEf/gbiB6w85tvpFW7vAu+0lSwj0DLVOgyNJEQT3c5IEGJoXCRwpNJ9GpMna0xkvI1a\nvwuo5DHRQofBsSlrEJIlsCGGBzApuA4b+EAfDVlkanAGJu2cRZi6IWIItzQZG+CAoDqs+piQ1Icp\n0YwYh6X8w9p5sxlRI3bHQqR4QVWIMjH7hdUOg5zYBQk/YuWBOhRpTMxcGaWC+5i5dxMCinonSN53\npB/698twkdZECitIZ/OxqSiaCL5iMo+2DAeq12GTCYLKRpVIU4MmBM1cpbNII/dMicprp3EGvMvv\nkreEh0FZdHmd9SC0MFJkCBcOpWOh0bJxqV/iPiRumPEKs0yckpKvr3ONlZgbL5+fo9Nv8pnZqedx\nPT1HWGodwct6xaJAixSH4IGKIzoSwn1PqOkqpFbYNDG3So0BwVnqkU37bkm3oeLq++tmY7F2CSQb\n+RHjvytZDpi30a8WIbUJ0fdp6UiwmdBWWorMdQwRVu9kb3hUzIe93G10D7ArlYi6kGScVKyB64Hq\ngseVWG6ooUF7cZXXH2GR/uj4kTs+EWfasUmK9CKQjOCBzQJz6NTgoE5skbJH+oRd5eV9FAohnBGb\nEQIlRjbrqLfhdrCIy2Vgf6TRm47UbL3iIjh9AId9I8mBLSQqKzcW+eCwRz+dvsg5bFh6l5va6fMz\nFrmhS6apU/tCCWdSS8zXQr95kyemVH6MhYRIZCqwPXiHgxVYP8PzB5Hw9s9w1+7Q9D4AYhuiBVrB\nXIdJMDakzYgUukRCr6w6c5ArHSVYpGgkimIogqBmnPVK6FBEmDQgbKQw3m53Q9TJlmmMsXHsbUxW\n2oWaQC3ipBGmogPJSau4KKkJqLAxND2rRLLoKEFcPuqjIxPJZWQVS0e7I1wxhvs2GBQNZK9DvviC\nxydi0SKDz1/SyIGNXWmp7kKKGWuNqwbEOp3AzX6ZK7ERa6AxgoexSKiB1AM9N6amXGUjiIEF3AOo\nMGujdhvtl+DMRLYYEDLYygNPrKLcbuNxtvQNjiuEXtimwMbrrBJZ8vtMNhPCe9TLaJZfFsX7V3gv\nbBifQRRiKnhIeH2F87yic+PBXSVPv044KyqDT3tvGbRRc6e7MNlKN0N0NLmqX0eANY1NAqUJs2wk\nAfWIMThk2iqzCk0ScwOJAbOBUwUwF6oVciwEcUIPrHFBO8QYSTYs6qZK7CMNvnoacklVilRm7cSa\naKEwayZ2hrUHw3b8UgxXLjqTmtHNcM2kdkDdKJKZLKNypveHIC8uTfzY5S0if0FE3hWRX/+B2/49\nEfmuiPzK/uef/YF/+yUReVNEfktE/swL/RYuhA7LJjRrnETQDpURbhxEaWYkn1n2hnuXjluiudMC\nrD5xkSNHP1EzdIymI5w49UASA6304NRumIZhce4zzTNYpHMhmmMmpCY8T43nqaH2CqfwGd6fhVQ3\nHoZv82B+i1xf5iRQLxFpK2ad5VI4pl/ljfI1btN3uZmeciCBHfHpbQ5+z3SNXG6U8+XLnPobPLdX\neG6vgIwoeusZWmWzMTJFbVA2ZYxPBcH8QNSKSKL58pGQOrjT00x1cHWqToh3jq60MJhbEeOoQiSw\n9UQJw9nRdtSpSQX6UIqp0DrkFkgIIpVkieKJoivdIlI3LhhNVtQjO4Sc1Y/cdMPEaJ4wudJUwAa0\no0slutJjJfwDxNv8QQkzAP+pu/9HP3iDiHwF+JeBnwZeB/5XEflxd/9/HywLnInIfCFYGKLwKMQ2\nIVKJO6+q60Z3+bvMaBcsCu7G7ErlxErGeidKRixD2LgQOIiCT2TOeHDcj7hA1IrRCWZEG5MixCAV\nju1D3e47PLkk6uZcpohvXyaoMefvsZwzq9yRN3C55bJ0juef4M0jHMrneJpnHqc7NJy5WW9H+XFb\nefy80aavMl0Siz8H4FlPeFRSP3OVhO5Yo6YRtYL6jGmhe2XaYSQIJIXerwgLhY63CfGNjNPiBTXh\n/Zh5sMfTu2ycxYlMHAQuGK6F2SY2M0JUdLvF8olswxCqPuAd6oUmw7Ubbab0QJNAcyfwgCCKf5gh\noY3WMyJGCENLq2HEOSd31He1mHdUX1x78LFnWnf/P4CPNSfux78A/Hfuvrn7N4A3gX/sY+/lY3at\nKqgrIZ5JzVHZ6AQ2n6nSsNZxkfHkLYAL6kbyRNXRdDdRZgqrRVY9s/aIGhQXzCu0W5oHqhtOpfRR\nW/UQ2DyhYSO5EVriwvhTty/wQXyFa7phuSRi/CpZ32Krj7iXW7zE0eiXd5ivF9r8VT5nX+dB/G1e\nit9k6bes/hKXqUB5n+m58TxNXOoXufYv8FSe8FSeYCGDbXRfSDAWncJcR1951SvJnRDANCGiaA/Q\nO223u6sLSB+0SZzclKsJN005ETgRqGo4iWyBq8k4IUga0QHS0Z5o8QyV8QGXQlMfUOp2RLUQe6Dv\noSE1CEdxAk5uZWQ7uBA8U0JDHA7WKWqwdaI52gJXGWLwSKP8Q9Ie/Fsi8q8wmAb/trt/AHyGgUn6\n8PjOftvfd/xuwkyMQquK0LA+UsXxhSRO7E5jIsQLxRJtb3nFVsGVGioPG5ykE+LK5jM3XFk1Dapg\nVzY1eo+YXJglAgWzQIqdYhNiisqG+ESXBhhT33Wu6V0eXxPev8/9EqB+hRpW8vxdlrPStwsril5e\nZb3t2PoTXB+tRP9HidXoD95jqZFwCmwZzrJxPF2Yw2/TY0Cuo7V2tUCX4eSYgYs6AWjBSb2RNHKJ\nw7hoDlkKBaEFYappFwitw5JtkU0D2ipBE6obh51MWPWWYGeKGOpCXDs9Rqo21JcR2qGKmdFbHu5c\nbXRmMgXpPkyVsqJkAhWVjiCc5jGpA0gWsVCxkLnawsHKaK9ZRNNGNKULzOKY/eF7xP5z4M8xNv5/\nDviPGXik36sw+T2VED9ImLl5/IoX63RRgnaSQfGAaSRSiUExvSC+kMk0H5efphMWNhZTziHSVTAX\ngsAVWPpwQNXkJDOmMFhho6mug6fge/ToHhYdOlRJSDDKzkw9XD7D07BB2JguG3r7JiEG6vXT3AuE\n8D1SqYTwfY7VsJvGZ9ZIe2lmvj1S6hd5vwem+B4PTpUpN549dK7f/0lO3CNxJ8y0M26RKh2h0ki7\nnUTESx4AACAASURBVAhWnYnWmBgdARGhIWhM3PqFc5xwCm6DMbCJ0kl0hayN9cMwESD3E1dVGpBT\nZI2QOlRmIh2XjvUFlUIUpXdIMqNhJOR0lCTQpaHubAj3lsjeEU0sezVYqVxVWXpjo6PdyD6zhk5H\nyCGgTbmEysT9Cy++P9Cidfd3Pvy7iPwXwP+8f/kd4I0f+NbPAt/72B8ow+hxdGOThIdKtEyQK9GE\nzQbi09Rxzh/tgoM3Qh3YSXwohgJO9cRiSpdhH5l6pHgbUe3a2GzYd5SJUgXJdQg+ZGLTE5NO443a\nq6e6vMO8CmrPuU6GrT9J8o1+fJvjKWB1o2igtoe0g3Kuf4L+6ILzJ3ncK+nmbZ48g3i9cjdHZHIe\nnAJ9/jrHy5GtjUW70jAd08HQItIMTaByxbrgmqgSkT5aWB4auHCRmaVBDXWAldWHpmJX124eyBax\nMIQ5DcUtMGlF6oGBHNgIPpCguQ8y+0bcXb4Zp5O60QUIg3pYiUScKEq2zkUbc19gJwBVGocudH9I\n7iuGUuLKjQecmeKK6wgtLWF94fX3ByXMvObub+1f/ovAh52F/wn4b0XkP2FsxL4M/JWP/Xk4HjeK\nRXBBekTFqdYpMjHFK6EFvMNsB7b9CZ6zkRusLmQXcGcTJ0mnhIhwotmMSiUrFEtswZg8sGogAFGN\nrc3DSk5laoFL6MwSPgKp6fqY0yzEAnop5Js30SnQr29Q7DzQRf0OeI9PXSPPX/4bfGkNrC/foDc3\n6N0f4/tdCfHXeFgulADr4QZ9/nm+H0feFkBt7SN0fdURjKzecBvCPZdKwFG5x3Um7rzYhlFDI3kY\n0aIa6ElQg57hQYVVO2J7gAcZ4URvD0aquxldZ2K/cHLhccy7V08R6xRG3Xv1QKKCg0phYaIzrD0r\nQuCWaGfOH9ptUJoqwe6RkDCc2BKWnLULiQvBIyU44cUHYn9gwsyfFpGfY1z6vwn8GwDu/rdF5H8A\nfoMBpvs3P7ZzMO7H3Gdwp2jHcGZ1pAdcFDzh4jiNa/QhdgG0jaBjt04XH2HNPRNcyNpZ+yMkrLjD\nZgLSCKa0GGjecW+jvtVB/FOUGJVkM55WvOyonvltXnqaqLxFX+BZ+QyTZOL0VXKdaLJRVEjls3z3\nyYlw/4t84/VCm77C55shj/4OT96r+Dlwvp1Ab5mviZC+zsuuXDjvr4MASrFOlAnVTrUBcJMwYqPM\nILAQvA/2rhbEM7EP7H7QcVmOHboGlqKjbWXbSHEE8EDoByyMXDXBmWsDSTyRyGqVhZkiBi3RgjP7\niUUWhIDXRtED1Y3ORq7CA9lYLbBKYPdPUhwivpvUfQifImwihHjC28I1GbFW4ou7bX64hJn9+/88\n8Odf/FcAEwUKNWTMAlmMU48cSZxCIbUDLmWIadgxJ0DoK30aUOBM5upjI6eMLDJSJ5K4eqZ6Z6GN\nlk2FJdYRgtfhVtZRv/o4o8VwGa2cvIfs3X2ap0chvV+Z1v+Hvbf51S3bzrt+Y4w518e799nnVNWt\n63JdX9tJjBWFD0HSoEFII0II0aQBopcmfwQt+umnQzuiEYkGEh1oQCSERFBAQAR2bCy4+OJ7q+qc\nsz/ed6015xiDxlynfGMZ+cSxrCopS9pSnbf2ed+z155rfozxPL/nhXX9GSaNuH3GUyjVv6Hmgdz/\nhC+vheMv/Hd8sT2wSeW4/wze/iYvosjDb/Hpy1cgK0/3K/buN3ir+7d7zdoaWa6YVCiDWfYQlRdV\n0BsW66g/i7PpwZqG54yWTuB0CtYbVS40C2rutOwgK+YPlBO75s4InJN9zJORqG20NLokKjNH3lBd\neM5k4YUuK60ktQuTKCVjmEdF0ZzYygS+0ySYfSBF03aObCwxs2lysWQJoWtnaStNDzQV1aD9UygK\nvhMdMckzLNg7dp5aVw72mLiEsMsLJRc0x/daGSqilIr3lZJXDmfki0nSTKmRrF54MUdFuEtHCVxX\nRHciy/DfJ2xUQoWSjR5CTaUSlGM8HLL+jC8fO11+ynub4PkV+1QofMUdQvNKsxXaj/jq8412+5v0\nz5+5xL/EcoVX83/Pcp3YnmZu92847j/h4dbZ6z/mk90gRp32/TJREzTncVA0pcfGRNC9ECSIYF5Z\nSYoHUUA8qbmCOGGVRmdKJVhGJmK+kHqwxXmql4VujrWgTUpqRXwZQSAlCRoSK/jOa5ItF1QK096J\n4rxYZ4pBYySUrcDiQZHKJXf8bOPOHQ6b2cuwkR59JvUFcmIjMbnDdCdd/zBH4yOu78SghUTNkSzD\n5sEInUy9QV5QLbRwFBCrHGcbVwo06cxtxuqOx4ghMuuQMzuJhJNaSdMxB+dGhIwufQWXA01DUcKh\nmHIgzF15Pkkptn/G29P1K/s7VL6iHIDccc2k2MZrEfbLz/j8Wtle/32+2F5xjZmcXnN7/Kvcjg1/\n9Y+5j43LceWbV4W7r/8SX2fn7hSbz74RKXiCZXJQmH3hIOi2s4Sz28DBE44XpcoV8ZX9jGJtkcwU\ndnFSDzoVjYUpBbHh+lUZpTSdGuqCSTJ54WmdTl3BB5z8wibjgKbivJzBKSVnDoFZDjQbt4F24BBj\nypk481kfI08IXadQCGlIXkh3LCecF/ALysHHH8O+K4NWIHIhouEMkFxo0GNBsiEcCIpYIfPAciw/\n0TsqnSjK0QtqO6kL0pNunQyjiuK8EAwf0u4XzIJow1MltdOYWFpi+QEAN8g2r86HP5bf50c/n7ny\n2zTu2I6JlIMZx2fY5Qu4gMgd7x4Uuf1r/N6XO8V+nU+OYF7+Z7jdU7++4+WTV7Q6sd6Saf4pn+xC\nnrPMLiuZDcMRgfnYaSU4JLkcTquFGhNuhsaonvTuYEmmUMLxMyNMOSg5E6pk9FHbzg9Y+YZ4oecA\nmAiV59kJ30EKKtC1EQZbGAsMAZPDIY5aMIvTAw6MB9nIvqIMWvjEOAt4gp/bjQJsBCINygVvDiyo\nd8J06Go/8vpODFpJwQlUxmFg14lVDg4RUh1JQ1Bg4CfzFFcoUFUB5zDjntFObMzAjqijGXRXQpMt\nFak3Mo25VlLHdsAy6NKHN1+URmXqL7yvZ17Z8495Owe23zj6lUlehgt4eeZiBR5eeKgLvQSXgNun\n/4DP9mmIWO7f0L76y2xtYn/4R9Tt7HRdJrbjh3R94SjnHr0BcoAvqMNE58C5d+g5UfpGZDChNHP2\nLhSd2UhmMcQFKUHkC+nrUFRlJc2QFPTk+naBakKl4wgZZ0NA7uiyMfmYOfHKZDuiybqvZHlmwJec\n8Imihrlxk4m17ghOl8Lcxs8zaSEl6BxslKFvwKlxxXVi9sImHc+Zyb9ne9qQxLyNm0swxcGuidqC\nMZHuIELJoLdCnG5crRt7JJETKzuOgBqR+7Cip40liyHbS9uhLyTjFxcRKAvGQPeA4Kf57qjTcFwC\n9e4nfPJVIeWn7CVhv2e7M9J+TPvBRMyfMROofcVeFXn3V/m/Xxut/mV+9b3x2cM/5NWh7F9PvL9f\nsTlZto7wHqWwngekJ1+oekMk6OU94WfJSSpWHScgwbNTMslJOIAljM5BlopGpRRDtUMMY2EPY5aN\njHOmZaLkjSFimyAHtK7WRsfYAe1KsU74QsYze3VKX0AhwkhJGg0xZZbbkB0W6Luwnc4FA/xkMBRG\nCVEkQSvqwaFthKGko/p984ilDixO2nDSmiEhQ3fJyBUzEfLsjfc6tgfqg7ySNiLiJQsRjVkGyc+l\nIcxnQuGVEsaLNtaAlkKc5a7alTXhed6RPuEulNqw83Py+Uve3sPSBWGnv3nmIZLrD2/8atl4uX+F\n1jf41WhbIz79X/krh/HYg+nzN7Rv/hXe9Z3t9W/z5vGJTR64LpV+m8j2Hu0DojHklSsvolRfRiFf\nNmav+ElEb6qUU+Zn6VgqkJhXtDS6KNmCvTpTq8QE3pNnqywyDkiWIzssacy6sYsQuuK5UXQGOpPe\n2PJhZF3kKybZOWQmtVM7IzFHIHLoi9SEvdlYRezc7riCHcw6s8sGLFg/jaYnin/OPmxS8j3ziEGc\nFO9OukC5UeSOyceT3GVDsqDh7DywnICzzWTECGWgVJoEKSAZA8ScRjNn7p2QQhPl0gZDQBLmsBHt\nnhcO3bF9Za+BhQ9Y2mkBycvP+Pyx0OXnbPeCbV/w9gul2L/Az18frJcr63OwyXuwK/3tX+cf/UYn\n6l/hx2+FdfkfuDwZD9+sfHVvVHvGrhcu8gc0Frq8G3chZiTesWJI2vgFi7GXGyUOhDsu3vAy0fqB\n1ED3++HXKhsSRpGG6uChiTrZL+OwBuiHdHAZK41FQbpRzhq4o8OZG8qzVSxuHGJoHjgjEkD6DdcL\n9AktDT2EZheIgzBBxKnnmSPL2NR5btSccTZcBgnnpkFqsImhbtjHKxO/G4NWGKflIkYLYwl4Iqms\nLGyAgRquBXjGT5SkpZzCjcKhHc2BxSxeOHShyjPiwkGlaCJH4jazyAdROWwYq+wjS9aEi8NeHOsL\nLmenav+cfYHpOkF7Zn/1jl/blPzk/wBe49dPeEehvBRyV8oX/w1/8fmO26rEl6+4/exf5ufHlXr3\nO6yH47nwfAnqu0/YSF7lmGnTz/13FqIcaBaG9mRCsqI4ZKIRA9bSFrReKS5DraajW2XhiMz04hjP\nZBjNjarjITRNQjfmnGgiuHSmnIiTIjNLoXNFzZAw3Buzzkg5yGPFbCc10VD22hEODjFwQ7QM3CcQ\noXiuSLnSnNFeFmHXTvjEnIVwxat/y2T4mOs7MWhDoIShCV6drTivbtBroTXnsMpD33mxuzH4yunG\nNZhbjoyxgYWiRBmhxHob4uaeIErGTsjMZFd2JtYMmhVKdFT2IUJxZbeDjJWUF8rZeevze+6fBLWf\nsS0Fnj/h934ZfuC/gjTnwv/Fek36NXleIL/5G/z2Z41y+WV+/JUwyT/gMy30n/8S/vqFqDBdN6K+\nY73OdHsLgHSGhceF6u1Ml2xIF6Lsg1NrUJowJagehA93sumV7ivNApWxdE/nfd0QmBSL06ruE5ML\nVxHMbtSsw3WgCrycD7ySqZgNt/MzlSkUrFGi4hipydAvToQ6yjB3tngFwJxOlIYeDyDPSBSu0pli\nomDcaNxZkF6J79+eFg6EWm9cPPEDjjps4WL3rHnwYhNTdnoOrA9AcOCi1JShfE8Q6YQNlKeGoezc\nubKbUWVki6n4aDEalIS9VmqreN0HZl13SKOd4QG6v+FlPigvP6JvT9TpLV+8U/oDqFaO/inXw8n4\nitcvN9qX/xW//nzPfmdc33yOf/UbtO3K8ernPETim/HV5Z716XO6BHaMumaPDQtn4kaPChnEySOo\nvtLkhTxWRBquRvZApGMK1i+UspM5hPOTGD13DmYshR436APHn3odie5xwTNxFNeGxYqVMZs3AXOn\nARnCqht7OnOv7JZDMZcVzUEZD0B1IlpSTwCdR8HyoNmOiBDI8IyFYNPOvBu3mnjszLF89Hj5bgxa\nQBQOH1kLexpzFlyvtNQRktYKh0xYfSJi1AGnHC1c150pko2JGaUQPBvs2ZjznizPmHQsKiGCyDwE\nHi60BHyAJ9xn1Eb5q1G4nKndWZ4pz52sv4+3mb098G4CjwvL1lD7CXd7cu0z7x4m5PHf5bd+eDCX\nz/j07c6s/yc+3ZjefcHLK8cn4X4LTP+fEf7mH7Jkh3AkbXS9dulImUgxjtxZ8kJKoblTdDBpD+lj\npTKl58IU73EukEGxV3S5oaqIrNxOY6NqwTO45EGXV2fwnCAxVpyLT3R3msVZEx/VhdkEwaiyU31s\nrdxOOiLCJHn+HsfDPstY3VyCNRp7FtCVVXbSB7kxYijz3L5vUGURFnV2r3gIUzj7dB0pLdmRqMzW\nhvLdPwRLwMSK547nRGcB2ej5iMqFiWAO5dAnPCqT6uC/WmCHMkvSS2P2HAX8WxK206lnCovy8qEY\n3+7Yp0em24/ZY2ctXzPtgttGX++5XH/AO22U+sjn725cf/W/5MfP9/Tyr/P8ZqG//RE8vfC0PvJF\n6zhveLsm6/sv6N7Z6+N5H5LiB+TOSxrTSPhl6sZURuJvYUfEOGJFxakUpmxcpbG6EHbBpPOSCw/e\nSZkIS4gDOQ+WVkFC2TE0Gk1g6SOP2EPxspPNSSojaH6iitEz2MsTF19pMtjrRBmH4QgaGwt3pxAH\nCgdP6VwOYRfhUiqHOD0h+gTMg19rnaLft5k28xR29NNuI0goSzp7Thx2JfoFJLFciLOHjnSS16i9\nYxMfpr54TWhjahPX6kwkz9UxH4mIpUOvjmkjgF4GUqjUOyIn5mjcZKG4sp4SSC/fcLcJffp/ITvH\n9YFYhGifDCzp9FPWo3Ltd/z+60J5/Hf4rTcvrA+Vz5+UpfwuT9PE66dPef4kaeuVV16R+lP2beK+\njc95JiE2DhFqvbL2iavduFFZecDzBjoNO1Is37J7B6S403QEc+xlGsRwCzR35tMq7uUPLeQmw45T\nZGzNvBS6BCF9JLDoPZqNpQfdAueEJsf9oPSIcrgzyzOFCbzittJyQxgDcJdCZuB1w/LCjU5NJ6LS\ndGfOIXVqQGzfM+1BipzJqMto2UZjUR3GQzrGQpcDQ8YNs7FsHzmT5YmZSu2C2shnmLvRdexXe164\nzyueM1KDdsYHBdBbUEsBlIMbpvdExpjtiuJnB0l94bHOlBfj6B0r34CD1rdoL+xxx56w6gt3Lwo/\n+i/4YV6I57/O073Qv/pNdPua7f4dl0jqfs83y8xyfMnNvuFyloiUZFej9EYWeLZKjeUUzzyNA6Zv\nUEeiwh6JiI9tDhV0p/EGj9tA+SvMVHaE1BhBIIBmpYkzmdJDCAs8nY5QI8gcoc7OC1FXjnCsKHIk\nxca+ueWNWZRkpulKkSsVo/iFdj7shnDvSWhA3Wh9RVVIGzlxaYFLsESH8meY2PjncQnJRDJlo2G4\nJKTynBOLQMp70hZwRYqMzhdQo9O6gGyE3WFhXLIROYTRLgzrh8/0qqh38mwI70dhEWcPmMXBHWNj\nq4KJMnVHz8xaarBuL+T6ByxtJHf7pLR2waog8sjshvQ7vn7t9Od/m+0HDbtf+cEeMP8W3t9Qrm+4\nvQ7k8pbPHl+xxU/4pAsvZ2HdgJIDiOFpTOxkC7CR12ty4KoUhiHQ+kToxG6w9CSkoHZFOag5kRJE\nS7SA9pmqZ30769kBdDwnSiRHjuhmz8JhK8ozS+gIH1HHjh2X1xBjm5JyYfJklxtL77gFmfOwiDO2\nVdaDawmmXIk+PHjpRlQfFPFjwoxRmfCPL9T+cyzSP7++d9efFtbxn/0CqOP3ROR/Ol//dRG5/cL/\n+zsf9a9IAZZRCtFEs9LpPCC4BZL3CMlWOuadxYPFA6FQUbqARBtZYmmkJUWVKSuHOWmJpNOsUGPk\nuS5lQxEqyhaG5CvcAkvoXWkm7HnHnnd0Zo7yCvxLtvIFUSrzHtTpkXt9wfINVKXfP/HFnvzKD/5r\nfq3+L/yoBU9399ziC/aY4NXPhhD75TO+uQjX/IL38QblDuUO8ctYemuhZuAyoarATopBVqrMHDGN\nMp8KE8Mf10WQeMajYQNoyNyBsnLk0FV0z/ElxsU6O3CUsYUtVCQcwXCu7CpsCkrHUnAdHa09CoGP\n2nZxtgqRTmcd8VO2k+YDeF1vTFLZz9+rYBgF6WPbs8gBKGbTt7LJj7n+VLCOzPwPPvy3iPxt4P0v\nfP/vZOa/+tH/gg/vEw3XivsONiFyx5F93P0T7bO44lX54OBJdkIKi090Co2k6ET4E52ZsfUfGQu7\nNIxCK8mUL6Tc4XVDvXJPAbviMuMBiyUHG/Xc0x5qLOkwv2UKoR6V/VWS+SWP5UZdgm2/sPZf5u3n\nB4V/g1YNefiGy9NX3MnX7Nrpxy/hc8fmRz7doPs7PCf83AOGJykbtcn4efJGFifcmDzZpBEZzKYc\nR2EusOEsmbyUwTirOSHtNEci4DulCC+hLGc2LpK87MKqMnQACbdS+aQVxJw1bNjIYyJCoCaRwiLB\nxgEiVJ+4FdCYsJLU7rQymMDzaSF/4Y4lZyZ9QaRScxteMGt4lnFQY+PO776VTX7M9TF2m/9WRH79\njx1oI4L83wf+5kd/4h/7RsmzBlUb02FYBCkjEmgl2KqNVqUEpXfkvPl5wtZ6CJRkcSH0SjNlTiFi\nJUgmubJQod9IXUfBnGQ6RlDcVYTZxx6wSBA5g9zzIZm4uLLNBbl+iRjE+nOmaLT1G8wKs9yxrp1S\nv+bBKzz8byw205/+El1ufBMP2NHQ9ZGiBa4z76aZJQu9bMQ2HsJd+iC0ECAwRSF7QcQIGgsjA01i\nQ4vQMtE8BSrZIC4UdYomzzoz5c6RsPTCUq9IH/dtkp3dKk8UPo2NR1u4xFsaFzxG2o1EYZMJrc9U\nB7UcnycT1IbuB5ZlDKCWbAUKRu9O1w8kG6fRSIzIA5fKXBsq6/C+IWxp3HSnevvo4fLPehD7N4E/\nyMzf/oXX/oKI/EPgEfiPM/Pv/4nvkrBkGRzZGlwjmRNmTVoalU6EYlMS9G+dmy6VNZxrqYgGmcaB\ngu6kDyJ2SmXLmXs9eC5vSG5UEeYo9Hn4xPRU5ncOjrynZsOKDjMVUBBeHUFMX9FaoasRthLxGTF3\ndB5GQ81f492nO11/yA96oS+/y903nWiFyQqHX4YXan7Her0R9pbaTskhUHsMsBzOlErTDbE6ksXP\nGjUJbsNpO+VMk5F6uBJkB1cnxIY6TgyRzjU7l2a8nKoU8WSyAUJ+snkcguSBpsnag4OZiQ3UTxlo\nwzrs5TJSgXa4FcWjU2ImZph8G909VeoJ3mgEW1mZ5UYkqDjZhWY3alRaCWr3kcurf36Bzv8h8Hd/\n4c8/BX41M78Wkb8G/Oci8i9m5uMf/Yt/lDBjCcVt6DXVmULZZSdFaSKsNRF3mqzfIkGmOLiWUedd\nfOOmr1hjx0PY88KsB6+b0kpy7Suv2HmvUFsQ9Ubt94g+j+ScUGYKrbyQsrCLs54ajs2VYxLut8/I\nmpR4z9ENWb7iCzpP+Us8FCfqT6jNiD3pKPHNA++yM/WNvb+Q1gid0OvKsxX0CjVulHN7sMmC5RN1\nr2w64o7mNHY5IIagWrNDjH3/YQ2N5KaKiXMh8dwp8oYdYc9k0TPyKpP6IX8t59H7j0EMD0AkqG4c\nVkCfuHFHzZ0aCbLSaWh0CpWQhB5UhZBtmCQtmVyIvHHIWT2gsnzQUIhj0jgQzGcO3QlG63iVK1e9\n++hB96cetCJSgH8P+GsfXsvMHUbJNTP/RxH5HeA3Geikf+L6JwkzP0wR5zChhjNnctWhMMri4NAt\nCKmo27dxmcVmlt4RhV0LtTfUFrofoBsktLpzy0IRYSeZJWimI12bZ7o3igpI4RBlDYVsIxzvDBlW\neeFV77S6U29O5kGtTl4/5w/ukkssXHugMdFDmbtx+I6Vt6w35Upllnu6ljGzzt9wv12IfI+G0k++\nwhZORdC5cWlOMIr1SseYsVA228lmRPFBU8yOSB0uCRdE39BkY8px8KEbokHXhTwxT9UGyO9iDDSS\nj6HQilPiisTKysGhxlEhJVjjhV3ueQlYRVl6gg3b+hzOlsZYk4IPw+8pOyoTszp7JAWh5DSSdqaZ\ntTl7ESLmYdr8yOufZab9t4D/PTN/8uEFEfkc+CYzXUT+IgPW8bt/0huFJL0bYm0IVfJgsh33gTAy\nRj4CpSNRkLOHbrlx1GDtFXykMY5B6KRULA7IZFIwcW4EFUMleZHkrgsiF3YaDzJu/OjC24nJP+l/\nORy+0h+4VSX6I9NxoPYzLq2gvaFzp5YJyYlWE3lu9PmBSWDNK1MGsCBqhD+AJZveD1ZD/1CnVSSU\nrtA1MDloulDk4NCDSQzJZNbCljvqxiQr1nO0WKtScmfPCRUo1lEfgnqNs9MF7HWmRqFbH/v1abTO\nrStNC7MkLwQqObpp2XmUz7jojW4HoW148fZkmwqeSo0g1HEt5KmOmzjoJ+XGTDkYXc5GQY8d7SOD\nrOloWX/s9aeCdWTmf8pAev7dP/LtfwP4T0SkAw78R5n5JxIXJaEIJ+mkUlg4zNGRxTbyZWXkhVGC\nGmM5bbmMcIvSEQPLcubCFmo2DpmxcFL6mBGy4NlBjIeApoqKcI+z9QmzzqYFw1lixvv+4R5QZAb7\nhtIqaTecDWkzDSPXpOQdz8fEMhdKn2CpQPKSNzTf0FanxB2hO14emW8zh1yxDEqeZHNpRNEBzshh\nza5x4JKYj4hRMeHZnCkXJAeOqImTBbRVRK9MCPPpTO6etNJQmbicg7bGUF6F5whu7jGYt6YsFDIH\n14wQunc2M5Z8RGIdbd8YWzKdZtBOptBksCYK0PNDDKyQJVFJDqncxchzCxdMZ45pZ+aGxsT1z9K5\n8P8D6yAz/9Yf89rfA/7eR3/6eQlj+Vcf+VhrTEgzJJRJX4i8o5dglR3vQj2xO906rd9RFCKfEN3w\nuIOYODCMwBWqC1GCFg55x8xOpzAD7+0YMkALhLsRVZJCt/6tR0zN8Nwg72lzkE+vaFapecM6HL0x\n+46VmblVqI9IL1h/xTJVNrsxbQ0qRDVK+4QmL1hbTkbA2B5EdqQbmx5kQPUka2LZMC6EXMEvhB5n\nkMpO04kmRnS4k0cO7riJj/puTlB26skoaGWsHBoFyUaNiV73Ua0OAw+qBVuWUStXY9UbrkKLD8mO\njb0uVF7IqBDKrFcyZjwWKI16NuWbLZgeuAjiG4cWUoTCCz3nYVO3yn5OSh97fSfauAF0jFICUDZ8\n5AtoGb3ts42JG0ShnzRDz6DICz2TiXv6MYTIxkYzQyhIdg4mzF9GkqDsNFFKJpbJGk54Z7ZCzxeO\n0lj7TPNgPiWQoTsHxsI2mGLzM/XWOOQ67Nz+imsxSgatHfT8lEkmjlI4EKb+Cfv6RNRKbUG3R2p3\nKo3qsJ/GRsuJpknN4KiG9EppQbPkqp0iD6TuLF5oOE1eMSFIbigLT9KpkrwKYRNAGsUnoOElO3up\nawAAIABJREFUx0oEbObMbqSBxz2qOyIbYcOKYwb4QfcLOe20HLlsLoMVNuWN92KogSUc7Z5ahilS\n5DSOAUYQccHiyr0Guy8YjZuOQkiksYgMok7++ZW8/kwuAe56w8RwCmiDSCIPQiamY6CARAWxs1YI\niCvogUiQMZN6jGwBbawe7AZ3AZFXuq4gN2CiiBBhHHnQc6HoKH11VUoUohe0BO00AkqM2PqnXNFJ\n8WfY1FjsZ5Triq/PTBGordSEKt9gbjC9ofhMKb9P+BuKdN4ulfn2CkXweE/DGPQNgIrkNuSRTekl\nEU2yT0zsVHkZVTiZmEWY/aBRiKLsBIhR2HF9RchGYZC3RXbEZ/wUZ5tfuGpn9aDaDUvnlmNl6lSK\nDK1rlWSnUmVD427UamNDVJiiYrvRSyDmI8jZbogX8mSGXUO5RCAVbu1TYMO1UWJgS1Mn+tGodecP\n8e5/8vWdGLQp0M4wi6Kdoo1rW+naEXFCnSWF2wmjk3MwlXQsO/SZIteRdiiVKSuuwZQbtwVsn6h0\nMi+DaJ0HWRTpSs9O9XsozyNeNJ0sA/ZR+4fDgSJZmA3iuFHnJ3SD6Ui2yxPLzbjNFXKl1aD6Z4js\nYy87J5NfsKlxLVB8J6dnylYRu+KhmIxlu6VT82AjaSwsvXNkUDPoImQYakq4jFBkS0I2uqxUCyZg\nz4rLhtKwDBorxIzWwE8LuUVnCsFVSU96WSna8JZUDTapLMjg3ebEcro9nm2n9olmOcQ7ZWdmJWS4\nRDKC0B3tY4VaQxAbJS/jiZaCnUEnUQT1xjGXUw75PZMmSo4lrklhkcLeJ6pshHRCDHPlxtAaFIL5\ntKe82MRN7dTAQhUFu3F4xUoQeY/0Z5oyAja8oX7BtHMTwQUuMhGl06Wz5EHTIXxuPan5IQ3GUDso\nacOnditsc4OeyH7jpleWtuM1uexOvzcunoR+TTkmuOhw10YSLJRD2a3AJvRwXMfS2HsdOHdvBDu7\nKjUY6TwZwNh3Jo2pTNyyUAOKNOiCm1DdUTM6lQMhc8fOwBTJsUfPctD0jtpvHDW5tBshgZdCZCI0\n3Bp73LOE0OwgorCEkzI4i4qTOhOuJE56RetBMKGn8bRoDLhgD5ooExuZF7wI4s6hhvXTBp/ft0HL\noPgFlVuAlUb3ioWhIqOoHgOFmRo8nsr4NZ1JnF3uqRykOUI9DXqB5saRd6g4pHBEonZQxSlupDmk\ns4tx6RdEBjQi0lmAVj64fhNQugiWidSDJRtiz+hkWKvcFNbjDU9vkkk+47oGnV/i1eocU+Uuwcsz\npTkhTyBG5TaW1f7hPiQqw5c1pQ6+VTIOMA5SRvJMtkrpiZYrbkAXSinDAs6McuUVwgakTojvIIMH\nBkAWrG+Ukugh7GXBfMysJYxiO7pPpB2oBE4FOQiXkaUbDdVKZGLmRFcOvTFHQWWi1RMQGEFkEEWp\nPrgWXhoSNv4uASao62igfOT13ZAmSnLTCyodkQEWvs/ELdFsoDuhF7BRPplCmUJPMriMqNFINgw4\nqNKQGBCQRTaaH1h0ZgwHJFa6CdoHammikAibOu3MwOpizK7MrieKc+bGcFdkec1R78E+p9sP8NVY\n5I7t9SP3/UDWRz6Nxuv1ChVMC89lRdsDKeD5wFUWWiyoCkcZX+YygqfVyBj83bE1cYoF6s50LBxT\nHVzeNBafaWZAQiRZNprC5kbNjnJwzEqkkrmMrxg14J7j4a4d1HYidZwdMtiKsIgTNiKqkGSfAonE\naeDBlDPdIXUe9n+9YfHIFI0pGqaDR1F9hJy0NEKcjE6zHCq9SA77hlm+ZzNtpnIJp6kjNtCZToJA\n0wLIeZgIdk3WU5CxycjNIvqIcCKJqHQqiIPA7MsQ3aiT2ln7Qi9XIoYV107vuZedaor25KBSSeYc\nM9MtFy7SKK3QNTnqzutmtPnGUmFrD8i6Y/Ir9Ncb2/xjHu/A6j2r7TxPUPYbR92w206JZG3Q2Kl5\nfAuJHoBkGXldCqZj37drYWSjCF6GNbyZUjzYNKkOoUFDuAxYEZXOZsrFFZpTS3KcbVyTjuYEHqgq\nSrD3CasH6pVDxv5400p0o0lnyWTtK10OyFccjIHvMFLOCWqMB2g6VV5d+sA6GRjzaf1viM1IJiEd\npyP+hr1+/KD9Tsy0gnOY4lnQVqge45cWO5MlEhDZKHmQGVyzcM1CpdNzxtUZB9YRrDa8To1KZye5\nKcyhzEw86Y3SJpYoHDmzKfQc/jLpY4kW7bRycDXhasKhSc+OmyBaWfqEF1B9QHTGLsI8Xbh/eGSe\nC/frV9R6Q+b3p0LNcRPspjifIExccybTeGTBe8F7wWKELxv7mNmk0f2emSC8oCFI30e8aiajT82I\nXT2bFF0McuIwyHbhxpiFNynMRzIfibCMGY/CnnBwkDa0rUiQTIMDgWO6oQLORMsdvKJyo9JpOtwW\n0wkGdEZy+WGVwyqeipZxVtAYSCvJB1wT0pgzcJSlNNb2PZtpAxAaVXRIkG0a9mO/kNJRCYyCMTGl\n0M+MgmBB9IW1V+Jc1g6pI6pdhe4ztRyoQzdlDrhDEXMOTcwMjoLogSC4KFdRLg5rW7iWcUASGoc4\nkw8xjzNTDHo4xzSxdsMvdUSiXgqqC8LKLZ+Y4udoGpc2jc7UreG8o8YdrldeOTzK2DsvImhWbq0i\n9aDkDiV4kUqRnYafxqTkri90bkgN9phR31CFdMPyYGdC9B0qM+bJgRLn5vmQZE6lScd0GjVwGSxf\nkaTmPPgSMrqMXQPNOPegO5nDyYzLEHXnTulGSmcrirbzzCGjotByQhhRpSqK+anKizGj99aw750b\nFxmtw1zYXZmzs8nEHU6E0WWiRAd1EmU6ETpjb7bSSxI9OM58MZUAH2kvJYSUMhhV6XQt40BlPmJB\nS9Jz5sjhbFpsDPZdlXJqPMU6mgu9dg43FgT3JPSeNTZiCg7buFgHd0Q/Q+OZyzah3JFny9S6cGTg\n3FFwSlt4lGTl9Pyb0ppi5TaSd1hRDVI64mByT8hZbbDAGdLDJQ+yxLkiLBQM44ZFhaw03Ui5UvsY\nGCLGFg21hlNH7NJYpHAPJm8j1jQaTS5MfrBZoaRgUfE00I2ulfQNuGOSFw4RJofjNJ7ueeBWqd0o\nEjTpZDO8dgodxziyMuXgLHzs9Z0YtABdklm/JvI1IY07DRqFap2I+LYhUDIJuQDQ6g2LK+kLac4a\nC4d2LuE8TzkcACZDlBIFy9HZihh2EYkLuzyjOUIzehZKHEP8nTs1hl5JA5qMwSyy49lZUsbJWAuW\ngrRK08EAK9JxSzw2EqP0Hct9iAD7TMlnph2uenwLzACIuCJ1FOrFT+u3vrDkTGEFrjidudVB19GK\ndkelsPWFUg8KB4rQvdK8IFNnDiHcaOdD3fLGKit7CKKO5IF0MLnQdaMU5YATRB04y7mK2ZnmrsNO\nnkaYknSuuqABMxtxNkvEhBodPcVKl1AOG0ZHjwDdKeFk2Yn+56en/TO5hGRKZeeC4bhUjlBq3saS\nguHRTydqI+bxJJcc0fGCkq70eqO20fGaElpVOoUlRkeoZR0xQQmzrNwiULkfcGZP+ogJwfyAaeKI\nsyPGRMpO6QXV4IVCzcohV175TNMrRRWNQQRsEWgJJCbUbrg4YNAU9xfEK/tp5dllCLph0B67L4gp\nbhstHT1p2c81WHbhoivuwVYZ98WMzGBSBtOsy1CkiRMlWWIjWUmb6afQSDVx38AGNXGUr4LFhgg9\npDO7UWVGsoHdxmrnioqSsXEwMfsLR16oBJLCxI3GwqrjALuJk3lPlx3LSuYNt4mMQCxZcuFmo4bs\n+vHag+/EQYwckjxJR2XECS1sSAz112xtdE08aCrkHuMrGldJNlUyFqZecW08qhBZqd5Zc6dQ2cwJ\nPbip0LSw5QwFhixYCTGqOod0us5IdEwdUx+F+T5qkC7KWoxusPjMtSSNC+qF6DNHwCZC64lH4A7z\nvnHbb0R/RrqgcYA8EbpRe3CwnakDd0zSsKhUHxgL70FQWI+G6sSRYx85h7HiwCA+pg2eg6iT5wF1\nJmlW2c59p6aiqRTqiPskyTPeXs15ySRzorTBUdv04GoHt5wYsPId95FoM4dzkzdYHuPnySubTdCF\nZy08a2Fqd4S2QcWMRjOj6IEYVDm4MmSgN4Fi3zPtwcj/DTyEKE4FzMvYV0YQfnaTrCMyGg7AEG03\nQXih6wO79uFbMogWpA5/l8tIFdS8YOkjm8xHPPxM44ghEhEvVJ0pcWPPC8tZjD90R6zjPagyshyu\nKvRyZfaZhqK2IbGjMjO1G1veUziomRwM/9QhI3fgnqT1AclL66wfVHnSh9QxX9hrhXAmEQRhK0mJ\n4by9lYKWytEHSPqwHD+X7hgFxZjyOg5tqSOeSvwPyeayonnFGct7LU94f0DTcQ48K6iP1SoH6l7z\nhCgzulxSDywb4TLeR2SEAGpnOU4P30lC9FwoeR1hfFRKbqRPTDVBDjQn6p8cN/ft9Z2YaTNzdGkY\n9JMSO1cJkp0dRWWjlYnQgysV9457R3zmKMNqLfLIdNYYno865IthHKWQup/hcIJI0gnSZhqFAyN1\no/ugtVQc1FnkhT6E/6QOxVXlwPtEk4a2hucd5okJHDGTqTzjhBfWfjBFEH3H0sEbSyRLNojOOE8P\nYfiH5sKLdZDA0rj3xwHbUM6o+qBwxYrgckCMrt0djvZBIyxZCRQNpbkNSWMGls41DNPANLjKE6ky\nsm9jEMM1Hin+niWSwxLRQsmGyUTxyu206rcSbATRDc0goiJFMTFmd5I8Q5wbyAjR9uwjYYgKMZF+\n4VYD8SvHUdAO+k+RufCdGLQKoMn/1975vOjWZXf9s9ba+5ynnrrdHaNGJAZR6IEZqTQSyEgEiU7i\nxIEDDRJwkoGCk2D+AUcOhCAEDEaQBAeKGTgwBEEcKLaidkKraUW06cZW0O57q55zzt5rLQfrvM1L\n877pevW+Xfc29YXiee6p56na5z67ztl7re+PSVUSZiZIqxKYTra+MgX2eMXqb+hro6+tDIa1Wou7\nLNhUVJLPcSM8atMwbkwaXTfSnOP0mJk5WMWxFEKvrJbsGkQ4M1fCoLvT3UFf0zNQyuo9fcXYsXlQ\nVeb/g+UGqfSjlgSZJewbevAGR6wmOglHNuSDTVA4EndI3LHmytAJKmTcfXvDY9xoGcUrpkpPSziv\nNRkiNHtkJ8HryqrRkG5s0qpl2ur/MloQLbC848CqyEpw00duXJh2zxaBnmTRLe9Y0yrmI6Fl8srh\nPp3digksKkgI6Yn6KEmRQlcQTVY9WNWZNpCstazbAZE4KykPhDi7vnf29UB6laqyMU0gYBNYZ5Lz\noHVlkeRgZZ51wNSNiPuSOJ+9mciNqXYWsl+BvOYyjJtVr3vkSmdy1UlEZ8sDzkCRFoMpnQ7MVOL4\nwN5H2RtYfpamAIPMjoly6EGLC1nuWYy4cGkBnnVF5jRGDmOzgXqS1vEckFdcnH723TOVNSqYU6iQ\naaECmE0MjUATDq4csbFmltmc37PGxoEh6kwTLkN4UEEM2mGk7OjZKh1ZEvUZJT0K/xxXHpC8Z1NH\ndaCZpO34ELyXh4Jk4yYgtKJitoNdBxlJyCvcBhf34vYA2Y0jBElF4qhOGDuWyWorywg27sGkCPpP\nxFMcZn5ERP6piHxZRH5TRP7yefwHReTXROS3zsffcR4XEfmbIvIVEfn3IvJHnzyaF7zgCXjK8mAC\nfzUz/xDwY8DPiMiPAj8L/Hpmfh749fPfAH+KEjR+npKI/63v+hskEXbSLyxR+nhj1G2g5SnrCJyl\nZMg6OXTiGkTcQB85Wq0/lXuar4xoGP8bzXayvhquN16VGwJ7eKVALo0EcjqmK80Ghw4WL5bXaMI0\nWI4zDokEqUQW9eC8RqIZlfdgA4+DQw6MAZkcAa+BHoOjH1g8VGRUDIaCu+FuDBKRcZJJYCdpMrgT\nx702ZInh+UhyYbWFV77RYmPTBrazhmFZHr4WE2Jl9onaJLMs6Zs2NLeyyw+l5wMawciSfU9vVRP3\nwDVZQwAjUqBaGpg4PjqBYSiZDxhX3lhya8GtBTEb976U2rkbM7I6j31lojyI4n1j9/HEqVj4rq/M\nzK9n5r85n78Gvgz8MPCTwC+dL/sl4M+cz38S+LtZ+BfAD4jI7/1tfwdlp9l0cnPBsrFbFe9lCtrB\nUtlSOKJhaVga7XhF5oKgXJkYMPNg9lnCu1iJ9FqnOax+zx4HGcLVOx3hmINLTlYSHxMdoJnc1Llk\nffV0Dq014ZqBeLng7LbRvFUwNHqGR2mtO7USaoIbmpMlnUdW7mbDWXAtHkUfeb5XSZKM8jZwW7jK\nYFPhtZRl/Tjbqqs2hMlmyR6d0al822wcFmTsZbVkxY1tKWSuhFbXa6ajdKIvmMJNk8PKwzZw1Dib\nMq3W1dGY7jQRehrDDEO4hPCZEVgcrDQ0k+7Lt9lx3eDxtBG994aZl1zJFyIGrSdkp2Fk+5Soiac9\n0h8B/iXwezLz61ATG/ih82U/DPz3D73tq+exj0cWyWWksGhwZOdOJot2Ip3yl+ykDFQmGZ2MTlgx\nt2IutCEkCigt6+oYacVOysBNGDpwqUmy60B05yIwMivqsymjGYQiIlVnjcCoHFgOykwtjZzKooHZ\nQaoTAUtUWF0XKpgkYPVA5Y6QpGepgjvOwUIz51gPVDZUNlYGg5Wgo1lZaJcw1GeF+klyy1lu6ebY\nvLFQkps+N4wijbt0IlcyBBcjUwkNmneadw4LOon7A4lyf7ZmL+1AFFoI/cznlUymHpgIcxo9V9Sr\nxrr3waCxyV1tpFJQKX+1kMaWE8tJZuNRJ2KVRiR+0GjMnATKRY3r3p88D588aUXkFaW0/Ssf5Rjz\n4Zd+1LT8iJ/3l0TkiyLyRT9upHZUOwMQHhguBG/w1thxXCsnq4+GxQ2LG+JOPyNKH1uFMwdFubvg\nRN95bEqI0CNJWRC7o+ssPoKAhoLCwcIg2KOaGquDWEesE7KzidJ1opwF/u60EA5PIpXok9EmmZOp\nwZAFk7qZTm7IaWmnvhGxlg1TW7B5V0EdaFEKRWmeDE0ylSM3GolF0PyORnkMlItO4+ggMUlbqlqg\njUUODD+NkQGxKoPJI0MeufdkE6FxR8ZeV2kZ9NlAIMPZEIYmQ4xpyuIT0SgTPJx7pNy/BdYMWipD\nJ4mhUjFZYSsSDWuNFEfnXbncqIMcpeSN4MGT8YHX1RPwpEkrIp2asH8vM//Befh/fHDbPx+/cR7/\nKvAjH3r77wO+9p0/MzN/ITO/kJlfsPVSaYRxnIyixtBgjwWbyQo0uWPhkbRkmDBMUOlYCntTbChO\ntRJdlcHBFuWpMJnFQ90hfJQRmyZ+1oZhYFIW93cYWPBGKnMXnMFn6N7wqbR8A3owsjPzniugUY2H\n8IYvSZ+djDNgmQtmQkO5Z2fnjpQbmY7MYvav4azhkB3YEeDqE1UH1oqJMsj+rSJ/mzF0oalCNnpc\nWR1WDh4SXF8RKIcsuASVqx4kjaTxqFTr++QSkM5nfOFROncjS1EL4BdcjMswBr06lggtlCMcOeve\nZRAaqF9QXUuQmgcHG2kJ8UBn4cgddKAsZQXgjUcxsiXjEwQ6P6V6IMDfBr6cmX/jQ9/6VeCnzuc/\nBfyjDx3/C2cV4ceAb36wjPhYpBBjYiTGQczOnZ85NacJR+iO8QpcMQETGDIJ3bnGxi51O3yUveQn\nLCx0Si0F63SiFen6W+YwB7V4C3JcibN/7r6wxiNrKD0Peh6EvyYk6gPQxpBWCoNwHrVh3mgsWCoS\npeq9xmAPo+nOOgcTYUpD2ix2GsGIFZedIZ0hnTUOcCFk500oMxc6B9epZCSTC5oJcyFDEDdyLAwp\njVt6JZNrDtaY3LlzCUdsr5IdK8aKptHDWeQNwooR7FZLndkOXCeLG5olR2riuNUdSSVP+b0TKrg4\nTVYOaYATthEahAaXWBiMqqVnsthgl3vUKuH8aMKikxDl+paVCz8O/HngSx+YJwN/DfjrwN8XkZ8G\n/hvwZ8/v/WPgTwNfAR6Bv/iUgSwmhECi+JIssmHZcDrJoGVyS+diZVkEFSy8nLqtVWrDI7IyZZb5\nhE3CG5HC1J02rwjBokmIoOEctheNy5Vd5bQLvWeN8e2M1/oDuWNxJ+2xHA0DekLMijl9jNow9uHM\nNph64abKNTZuprQ0RipLbEVocaPrGyxX2nmV2aVokl2zxsYguKfbRrhx5ZFNFNdKSRy5oG1n8VNA\nqIGmsg9haWvxa3VWm1qyWrnA4srOZOGOIMrsIxZWgkeBiykiG8shGJ1dO9cYbKolsLSDJqf/lgjw\ngHrD2s70Tp7TquUkvSMxUb1yC8cUdhRUWX0SuSDLxjGffqV9isPMP+ej16kAf+IjXp/Azzx5BFQU\nUXJg3khNAsGlM0gWys/r6JUA6D7R8/6w2s4YxtHgMp3RBIlGV+dGqRk09KQmLmUgPK1SxtW4FG++\nPjhVjAnmxFgYq9NP3m5LMHkgLJkKSwRphkblzw4uKAcxigXVMplj4doHMxcsBeYEPSsNETRpZHd6\nDB5Om8t1XugIM1bMJksqkwMdTjblm9H4jCwMDw5VVhzNxLvTBrzujc/Oyb7AzIb6AFkQv5XH7Wl0\nJ+0gUjhkZ4pyyUq9nAxWhykDC+XWF1rutGxsUs2PqnNc8XzA8hW7OiPvSyMyV1QndjZLwgVaEBjO\nxppGxCA0aW6oLHWHkIVD32LJ63sBSYFcOcw48r7635ksWjvMPUC2CqeTlhxSHaz0O5aWmBj5QdwQ\nO7vAIq0qDTZIE2buhK8c6yATpsOMC4+RZJSyt6Vzs+BNO9ARxFkCOlIZKDOFdViRoKdj2fHozAxM\nWpnHSfk3II7kROU0Gj4t9NMSa+B2kPPCwcI1J9ecuNSGs51qDad4wKnJnsm9GTcZROvVlmVClhp4\nNujeGMBdlNGIChzi9cciWp2pVIgL5ivmSveiBZpudRFdN3pUB851MNrBdkrwFykHII9B95WhDgnp\nE5UbYV48gxItkd1L1pODiy/cmqHW6XMpuqY+oh0uI1g/ganyOzFpoeQbF4eebxh90BPUd9ZMrnKw\nYqSMojBOQ2als8xwJDecMmCe8YrmsM8gRydMuU5ockHawToE7OCK0sK5yERobNExOutsNElEDjI3\nMjeuUYrcHp1NBDsVu5sk2m6ICmS5/4k+Ip5Ifzg9AhxVw+ikwcUh88IlnZsI5IbPFZ8rmRUkNx2O\nWDBtaC+T5HsJmIaS9LPAL3LFddZGczbWGah2Zip3CDeBZZ55wTGYXJhcyHOziewVlzSLI2x9cou7\nWlppsMaOzivL4bwaO6EVlmfqHC2ZOrj4HU02gjuEpPl67k0SibXs9PXCawsuvhHZucgo4jp3iCuh\nwuhvuXrwaUME7o4FTwFTlqGEHogcFbyWxpQDt0eOMC66c9Ed1VElGm90EcgLqo8cOE0HoxkyJw84\n5IHOCTSmlw+Y6lG2RCLMy2CGQSRNGkcsZN6Tec8uFZiRmXSUQSAqdHbsuEIeuDprHmR0rB0IHVjo\nDBbfiUg8Gi4N5MaQhc9GEjmQviN9p2ey0TAJ1N6Qs7HnhgAuwtEHpkJLhymoDHReCBrWBsFR0VLp\nzJl8jo7oJBSkKWmPpD3SUxACySuX0OpAqhOHcZnKZpN9CIe+whj4Khx65Qil5UKbV4Ly8g3Z2PSC\nymCNKNkPxlF9MvCEObiXSWQHJkdFPZblR3o1Y94/aiIVZd/e4Cm8JiEvjLgSLoROht6xpGDWKIVR\nQ6PRxwWRyaDWUkYvuYmWM8ueTiPZU2in8QeygsBDdNZ0lvgW1xGoTCZVFquJs6G2EZwTQnc2m2XB\nlJM9lzJ6U6UU0I5EcpMiuoS+wVl53YTmB2aDqRtJiQsPuSG2MsZgjAH6mvsojlXkK8QOyLXSanxh\njjizu0D6Azqj5DUtywWnBeaD1I4ZbLIz44LKxFHWeWGdl9oIRclvdlOGKJdUWl7wOFhmo5sgc9Io\nT1zkkftIRB4Z+qaM53qS7UBt50Kxk8mdtKzSJEFY7UEmG6bjXDpomYLERhR7GpGnCxvfjUkroHkQ\ncWWNg8upDA12QNEUTHagE7HjUkl/TrC3G1MX1JULg5QKx2teevxVLkVCxiq2CGfxDXGl5yBTGVpJ\nkRobhuD0ctoOgRBUDM1OynkX8HuczqI7yABJhgLacRTjIEeyTAGFJRcww47AvKNirN7x1vFQVBuq\nDbGFmBAM1nkjI1liclgw7ZvcG9y87JEmzq7BEEFmklFBdlMbHoORCrOjupWzeTwiGqWClUGkoLmD\nQ5+Je6CWLBhDGjEgY/JGBc3BtI4zarOrnUsa65Ho6FgIhywsUgT7dQjrqKjYzJXKvPlccTwiWKSj\nOjl0wXSyTBB/utxG8hN4KH1aEJH/CTwA/+u5x/IW8bv4/jmf79W5/P7M/N3f7UXvxKQFEJEvZuYX\nnnscbwvfT+fzrp3LO7E8eMELPgleJu0L3ju8S5P2F557AG8Z30/n806dyzuzpn3BC56Kd+lK+4IX\nPAnPPmlF5CdE5D+eQsif/e7vePcgIv9VRL4kIv9WRL54HvtI4ee7CBH5RRH5hoj8xoeOvbPC1Wed\ntCJiwM9TYsgfBf7cKZp8H/HHM/MPf6g09HHCz3cRfwf4ie849vaEq28Zz32l/WPAVzLzv2TmAfwK\nJYz8fsDHCT/fOWTmPwO+M1nzrQlX3zaee9J+chHku4kE/omI/OszXR0+Xvj5vuDtCVffMp7bYeZJ\nIsj3AD+emV8TkR8Cfk1E/sNzD+hTxLN/Zs99pX2SCPJdR2Z+7Xz8BvAPqWXPxwk/3xf8fwlXP008\n96T9V8DnReQPiMhCJZv/6jOP6RNBRO5FyspbRO6BPwn8Bh8v/Hxf8PaEq28bmfmsX5QI8j8B/xn4\nuecez//D+P8g8O/Or9/84ByA30ntun/rfPzB5x7rb3MOvwx8nYpD/yrw0x83fmp58PMa0bTyAAAA\nSUlEQVTn5/Ul4Avf6/G+dMRe8N7huZcHL3jBJ8bLpH3Be4eXSfuC9w4vk/YF7x1eJu0L3ju8TNoX\nvHd4mbQveO/wMmlf8N7h/wInIPDuKW1SygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27085eff748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cropped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27085fb7400>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAK0AAAD8CAYAAAAFfSQRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nG8bkVRCnLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27086520e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.ndimage.interpolation import rotate as rot2\n",
    "\n",
    "plt.imshow(rot2(cropped, 180))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 49,  79, 103],\n",
       "        [ 52,  81, 104],\n",
       "        [ 53,  83, 103],\n",
       "        ..., \n",
       "        [ 89, 149, 188],\n",
       "        [ 97, 137, 171],\n",
       "        [ 84, 132, 173]],\n",
       "\n",
       "       [[ 56,  81,  98],\n",
       "        [ 52,  81, 106],\n",
       "        [ 53,  81, 105],\n",
       "        ..., \n",
       "        [ 86, 148, 187],\n",
       "        [ 81, 139, 176],\n",
       "        [ 76, 131, 173]],\n",
       "\n",
       "       [[ 52,  83,  98],\n",
       "        [ 56,  83,  97],\n",
       "        [ 50,  83, 101],\n",
       "        ..., \n",
       "        [ 76, 148, 183],\n",
       "        [ 90, 135, 166],\n",
       "        [ 77, 132, 167]],\n",
       "\n",
       "       ..., \n",
       "       [[ 50,  79,  97],\n",
       "        [ 44,  76,  99],\n",
       "        [ 51,  76,  94],\n",
       "        ..., \n",
       "        [ 58,  95, 131],\n",
       "        [ 59, 100, 123],\n",
       "        [ 56, 101, 125]],\n",
       "\n",
       "       [[ 49,  76,  93],\n",
       "        [ 53,  73,  99],\n",
       "        [ 51,  75,  93],\n",
       "        ..., \n",
       "        [ 50,  96, 122],\n",
       "        [ 58,  95, 124],\n",
       "        [ 59,  99, 128]],\n",
       "\n",
       "       [[ 45,  80,  94],\n",
       "        [ 52,  73,  95],\n",
       "        [ 50,  75,  97],\n",
       "        ..., \n",
       "        [ 57,  95, 121],\n",
       "        [ 57,  94, 135],\n",
       "        [ 63, 100, 122]]], dtype=uint8)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rotate(cropped, 0, preserve_range=True).astype('uint8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'samples_AnthopleuraPAN49@_filament_image0005.tif'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(p).replace('\\\\', '_').replace('/', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(241, 343)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_extent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27087ffec50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVIAAAD8CAYAAAAltCzFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLFiw4IRYJtIFCxYsOCG+A5Ki+fQ/qrAgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x270855c8588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27086928358>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAC7CAYAAABrY1U1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27086ebf128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cropped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Jeremy\\Anaconda3\\lib\\site-packages\\skimage\\feature\\_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15\n",
      "  'be changed to `L2-Hys` in v0.15', skimage_deprecation)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1024,)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gray = rgb2gray(cropped)\n",
    "\n",
    "hog(gray, orientations=8, pixels_per_cell=(16, 16), cells_per_block=(1, 1)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27085eec198>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAADWCAYAAADfCUmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naES6cVkObIwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x270855b12e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.imshow(rgb2gray(cropped))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "16*16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(270, 137, 3)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cropped.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x27086538c88>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKEAAAD8CAYAAAAfQcSfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x270865e6940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "w = random.randint(50,300)\n",
    "l = random.randint(50,300)\n",
    "\n",
    "x = random.randint(0, im.shape[1] - w)\n",
    "y = random.randint(0, im.shape[0] - l)\n",
    "\n",
    "plt.imshow(im[y:y+l,x:x+w])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1024, 1360, 3)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33036557"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.randint(0,100000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_iou(bb1, bb2):\n",
    "    \"\"\"\n",
    "    Calculate the Intersection over Union (IoU) of two bounding boxes.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    bb1 : dict\n",
    "        Keys: {'x1', 'x2', 'y1', 'y2'}\n",
    "        The (x1, y1) position is at the top left corner,\n",
    "        the (x2, y2) position is at the bottom right corner\n",
    "    bb2 : dict\n",
    "        Keys: {'x1', 'x2', 'y1', 'y2'}\n",
    "        The (x, y) position is at the top left corner,\n",
    "        the (x2, y2) position is at the bottom right corner\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    float\n",
    "        in [0, 1]\n",
    "    \"\"\"\n",
    "    assert bb1['x1'] < bb1['x2']\n",
    "    assert bb1['y1'] < bb1['y2']\n",
    "    assert bb2['x1'] < bb2['x2']\n",
    "    assert bb2['y1'] < bb2['y2']\n",
    "\n",
    "    # determine the coordinates of the intersection rectangle\n",
    "    x_left = max(bb1['x1'], bb2['x1'])\n",
    "    y_top = max(bb1['y1'], bb2['y1'])\n",
    "    x_right = min(bb1['x2'], bb2['x2'])\n",
    "    y_bottom = min(bb1['y2'], bb2['y2'])\n",
    "\n",
    "    if x_right < x_left or y_bottom < y_top:\n",
    "        return 0.0\n",
    "\n",
    "    # The intersection of two axis-aligned bounding boxes is always an\n",
    "    # axis-aligned bounding box\n",
    "    intersection_area = (x_right - x_left) * (y_bottom - y_top)\n",
    "\n",
    "    # compute the area of both AABBs\n",
    "    bb1_area = (bb1['x2'] - bb1['x1']) * (bb1['y2'] - bb1['y1'])\n",
    "    bb2_area = (bb2['x2'] - bb2['x1']) * (bb2['y2'] - bb2['y1'])\n",
    "\n",
    "    # compute the intersection over union by taking the intersection\n",
    "    # area and dividing it by the sum of prediction + ground-truth\n",
    "    # areas - the interesection area\n",
    "    iou = intersection_area / float(bb1_area + bb2_area - intersection_area)\n",
    "    assert iou >= 0.0\n",
    "    assert iou <= 1.0\n",
    "    return iou\n",
    "\n",
    "get_iou(\n",
    "    {'x1': 1, 'x2': 4, 'y1': 0, 'y2': 4},\n",
    "    {'x1': 1, 'x2': 4, 'y1': 5, 'y2': 9}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}