L4HG/gist:439961bb6acaaeed3cd5a6dfd1337475

## gistfile1.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import urllib\n",
    "\n",
    "from matplotlib import animation, rc\n",
    "from IPython.display import HTML\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x114d9ca90>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAD8CAYAAAD6+lbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11324a668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resp = urllib.request.urlopen('http://sketchapp.me/wp-content/uploads/2015/11/1-43bjDRA6O2BvOryiUCwpNw.png')\n",
    "image = np.asarray(bytearray(resp.read()), dtype=\"uint8\")\n",
    "image = cv2.imdecode(image, cv2.IMREAD_UNCHANGED)\n",
    "# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
    "# plt.imshow(image);\n",
    "plt.imshow(image[:,:,1])"
   ]
  },
  {
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 19,
	"metadata": {},
	"outputs": [],
	"source": [
	"import os\n",
	"import cv2\n",
	"import matplotlib.pyplot as plt\n",
	"import numpy as np\n",
	"import urllib\n",
	"\n",
	"from matplotlib import animation, rc\n",
	"from IPython.display import HTML\n",
	"\n",
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 29,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.image.AxesImage at 0x114d9ca90>"
	]
	},
	"execution_count": 29,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAD8CAYAAAD6+lbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGw5JREFUeJzt3XuQXOV55/Hv0z03IXQBSegyowq4TCVLtrIYVCC1U6kU\nrLds4gr84c3iTdkkRUqVBGfB2AaNZMeVqqyFsGPAcUKiDV7jVBzbq2QNRTnlGEQqtTVcLNnYsVEI\nskMyPdIgSwiBbUkz0/3sH+ftmdZodKZ7+nIu/ftUTen06TMzzxx1//p93/Oec8zdERGRhRWSLkBE\nJM0UkiIiMRSSIiIxFJIiIjEUkiIiMRSSIiIxOhKSZvZOM3vJzA6b2Y5O/A4RkW6wds+TNLMi8C/A\nO4Ay8E3gve7+Ylt/kYhIF3SiJXkdcNjdf+juU8CXgJs78HtERDqurwM/cxgYr3tcBq6P+4YBG/Qh\nlnegFBHpdW9y8ri7r1vq93ciJBtiZtuB7QBDXMT1dmNSpYhIjj3p+/6tle/vRHd7Athc93gkrDuH\nu+919y3uvqWfwQ6UISLSuk6E5DeBK83sCjMbAG4FHu/A7xER6bi2d7fdfcbMPgB8HSgCn3P377f7\n94iIdENHxiTd/WvA1zrxs0VEukln3IiIxFBIiojEUEiKiMRQSIqIxFBIiojEUEiKiMRQSIqIxFBI\niojEUEiKiMRQSIqIxFBIiojEUEiKiMRQSIqIxFBIiojEUEiKiMRQSIqIxFBIiojEUEiKiMRQSIqI\nxFBIiojEUEiKiMRQSIqIxFBIiojEUEiKiMRQSIqIxFBIiojEUEiKiMRQSIqIxFBIiojEUEiKiMRQ\nSIqIxFBIiojEUEiKiMToS7oAkTSbvKvE9HLAwKow8omxROo4+qESM8uiGqwKw/clU0cvUkiKzHPk\nnhKVfjAHr+truUF5tMTI7u4GVHm0hBeBUI8XYXxXCfNo3dAJZ+3eZ7paUy8xd0+6BlbapX693Zh0\nGdLDyqOlKBAtrAhvC/MoHOtZla4F5cSOEtV+oLrAk3V1qYV5YU/6voPuvmWp36+QlJ42cW/USvMi\nCwfRQkLXe8OzZ+nbf7BjtdW3IGPLqbV4DayCWpfztBqS6m5LTyrvLEUtxAJQjcJlfovxgkIoTW4d\nZGR/Z+qb2FHC+2gouN2imqw6Nzxwep1R3lnCKjC8R63LVizakjSzzwHvBo65+38M6y4FvgxcDrwC\n/Jq7nzQzAx4CbgJ+CvyGu39rsSLUkpRuKO8MXWon6qq22Imy8P1Dx9vbahvfVWrLzzmnhTkDaw7N\nsOyrz7flZ2dJqy3JRqYAfR5457x1O4Cn3P1K4KnwGOBdwJXhazvw8FILE2mXiR0lxj9aF5DQckBC\n1IJzi1ptr/6P9gRbebR0zlhjK2ZbmJVoOOH4z/dRHi1xfPu29vyCHrFoSLr7PwKvzVt9M/BoWH4U\nuKVu/Rc88iyw2sw2tqtYkWYc/VCJ8s5w4APaEowXMrWCloOyvDN0sdtcp9e1mr0YuuKj7Qn1XrDU\nyeTr3f1oWJ4E1oflYWC8brtyWHceM9tuZgfM7MA0Z5dYhsjCxneVmLko6m7WDmZ02tRKmPzg0sKn\ndnTdKm0uar66sBzfVWJih8JyMS2fcePRoGbTL0F33+vuW9x9Sz+DrZYhAsDknSXGPxa6rFWigyxt\n6r4uymF6edSCbUatBWnV7tYKUO1rvt5es9SQfLXWjQ7/HgvrJ4DNdduNhHUiXVFZFhaSmtnmMLMM\njny4seAp7yzNTj/qWkDWW1ITp7csNSQfB24Ly7cBj9Wtf79FtgKn6rrlIh2XSNDM51AZWnyz2jzI\njnexF1EdSPb3p92i8yTN7K+BXwbWmlkZ+DhwH/AVM7sd+Dfg18LmXyOa/nOYaArQb3agZpF4aWgZ\nOYx/NJqnuNDZOeMfK0XDAUm1IOtYo5Poe9SiIenu773AU+dNbAzjk3e0WpRILnh0gGRiR+mc0wXH\nd5UaP7unC1zXAoulM24kX9LQ3a7n0cGR8miJoRPO6XXWlons0j36DBHpNJ+bn1h7nCppqydlFJIi\n3ZDiIFJ3O552j+RLisMorUz7LJZCUkQkhkJS8kctI2kjhaTkS9qObmdA0vM0004hKSISQyEp+aOW\nkbSRQlLyR2OS0kYKSckVja8tgfZZLIWk5Irm/Em7KSRFRGIoJCV/1H2UNlJIiojEUEiKiMRQSIqI\nxNBFdyWVJu8qMbUKCtNQmIqux4hBtQiFSpjqY3V3GKy/kK2OcDfFLdyQzMLsAGduf4Z/zef2c2E6\nurza4Eln7d5nkiy9KxSSkojam3JBYb2FMKws45zgq/bPLZ93LUQFZNPM5/bjOf8ntWWv260GlbDt\n6cuM8Y+WYvf50GvOuoezHaQKSem6iR1zt1FdSP2NqdzOv5tgrcWjiePt4Re6nURYt9jc07j/h7Or\ns/+fpDFJ6arJu0pRSzDmRlhuc1/zH9evk+6Yv/8X+v+4IIvu75NlCknpqpmLUJe4h7hFXfnJD2Y3\nKBWS0jWv/l7UzdZ9nnvPzLKkK1g6haR0zfQKoKquci9ygyMfyWZrUiEpXVEeDa1IdbV7Vv2shCxR\nSEp3hCOoakX2Ljc48uHstSYVktJxk3eWonl4akX2vOpA0hU0TyEpHTd9cdIVSFpksSehkJSOmri3\ndO4pgz3Awul8Vp37KlSir9l1PbQ/5pu4N1tdbp1xIx1V7aNnArJQic4O6vsJXPanYw19z8wN1/Kj\nqwepDC5wimVOeTHpCpqjkJTOqr8IRc7ULgZRmIZNn2osFOfr23+QjfvnHh/7QImpFSFIcvrh4gbH\n7ihx2Z8sbZ91m0JSOi6vAVk8Cxv/qL1v9Ms+G/28k7+xjZ9stNyG5dTKpCtonEJSOmZiR7bGnhpV\nmIHhPZ1tBV3y+We4hOhc95ll+euKZ+nvyVCpkjVeyM8piObRmOMlL1c7HpD1Njw4xsjuMQqVxbfN\nmqzMmVRLUjpi4t74y6FlSaECl/7zDMu++nxiNQzfFwVz7cylPHTBs3IGjlqS0hF5mTxuHgVUkgFZ\nb2T3GIVpogNiGd+/XoiO7qedQlI6IktjThdiDgOnkq7ifMN7xihOheGMjAflsWsHky5hUTl4KUtq\nZfwNPHCK1E5T2XT/WHR7i4wHZRa63IuGpJltNrOnzexFM/u+md0Z1l9qZt8ws5fDv5eE9WZmnzGz\nw2b2XTO7ptN/hKTLxI5S5j9+B19Pb0DWjOyeC8qsysL0sEZ27wzwIXe/CtgK3GFmVwE7gKfc/Urg\nqfAY4F3AleFrO/Bw26uWVMv6eGRxKv0BWVMLSjIQNgsxh+PbtyVdRqxFQ9Ldj7r7t8Lym8AhYBi4\nGXg0bPYocEtYvhn4gkeeBVab2ca2Vy7pVbstaQYVKrDpk9kIyJqhE57pqVbTK9Kd8E011M3scuBt\nwHPAenc/Gp6aBNaH5WFgvO7bymHd/J+13cwOmNmBac42Wbak2cjusehNm+7X/nkKlbmpNlmydu8z\nrHlxJukymmYOa16caftZS+3WcEia2cXA3wB3ufsb9c+5e9NtB3ff6+5b3H1LP+k/wiXNme0GZsiK\nf89uc2zZV5/P1AEcc+j/MamZWhWnoZA0s36igPwrd//bsPrVWjc6/HssrJ8ANtd9+0hYJz1mZHe6\nWwj1itOw8ovPJl1GazIUkgOnYP1nsvH6aOTotgGPAIfc/dN1Tz0O3BaWbwMeq1v//nCUeytwqq5b\nLj2mkJFe4Kb7s/GGjZOFYQ5z6P9Jdg6MQWMtybcD7wNuMLMXwtdNwH3AO8zsZeA/h8cAXwN+CBwG\n/hfwu+0vW7JieM9Yz19kNglp3d9Wjc5Hz5JFz9129//HhT+bblxgewfuaLEuyZGR3WPR1WwuSue8\nuDxdPOLMuiqDJwqp3M9WzeaBsQxPQ5Us2fBgeg/kFKaTrqB93nr3s6mcDlSoZGuMup5CUrpmeE+4\n5FfKWjlpn4LSLC+QqgtgZLUFWaOQlK4avm+MzX84hnk63sR56mrXbP6fY9E9zlPw7i5OZ7cFWZOC\n3Si9aOQTY6m4OINl5Oh7sywFZz0Vp/Mxa0AhKYkpTCd/FZuBN1LQnO2EcFvbpBRm8hGQoJCUBA3v\niS4gm9gtRi06pS+Pah88SXwAmXf+HkDdpJCURA3vGYu6vEm0enLaiIRkxyMvHs/XjlVISuKSutxX\nGqfKtE0Yk+z2fMn+H8Pqv8xX61whKamQ9esips2aQ90/ItX/U9jwUH662TUKSUmN2XOPpWXdvrqO\nVWHDA/kLSFBISoqUd5a62z3Mcat1/GPdvae1F8NtO3JIISmpML6rlIrJz3nR9VNAHao5DUq9LCVx\n5Z3hxmFdPiia61AO8yS7PQWo2gcT9+YrKPP8MpEMmG1BJjEW6XD6lusS+MVdksDRbatm4zaxzVBI\nSmLKO0vRuGCC0+re2Lzo1QKzKaHxVg//n+Wd+WlNKiQlERM7StGZNgnPO64MJfv7OyrBA1Nu+QlK\nhaR0XXm0FHXJUjDdJ29dQ4Bjd5RmW3RJcouGU3702+m+r/ZiFJLSVeWdUQsyLRfgdYPj27P9Jp5v\nahWpup7kmTWW6X2skJSumQ3Iarpu4zC1KkXFtFGa9vHpdZbZo94KSemK8uhcCzJNb16AykDSFbTP\n0bvDbIGUtCLrVfuy2WpXSErHlUdLeF86AzJvZpYlXUEMi1qUWaOQlI7KSkBmsYUz3/Ht29J9qmWY\n4J61s3IUktJRWQhIgDNrU15gA86ssdlLpKWWR93uIx/JTlAqJKVjyjtLmQhIiE5RnPxgdt64C0ns\nCu9NsipUBrMTlApJabsjHy4x/rForl4WAhIAh+nl8OrvZeONO9/4rlK6W5B1anM4K4PZmHCukJS2\nqw6QioniTXOYXpF0Ec0rj5ay+U72bLTgs7hrJQuy0oKcxwvZOrBw+pbrkrtASDs4rPrXdN/XVyEp\n7Zfgnfpa5tm63NeJq/oy+4EE0fhkt6+i3iyFpLRd7TYMmRmPnM+jc7qP3JPuoCyPpnfieKOy8EGq\nkBRZgFWjM3HSegR2fFc6rqLUqoFT6f8DcnoxPUmcA0UyO1bmNjdV5fX3bUvNbVIPf3orgycKiV+H\ns13WPZyO/RpHLUnpiJHdY1i6x+MXVZuq8ubPGOXREjM3XJtoPeXREoOvFaIuag4CsjiddAWNUUhK\n52R1THI+jyZqT24djKbbJKC8M5zemeWx3nmKZ5KuoDHqbkvHLD/i/Hg4J+9on7tx2PiuEoUKDN/X\n+ftMl3fWHZzJUUBaFdZ/Jhv36VZLUjrmks8/k69XmEdvboimCXXybJHyaInxj2b/6PWFZOGodo1a\nktJRWTl3u1Gzf4vP3Z7AqtGbvtWW5Rv/fSunrgifKmE81BK442E3dKMV3i4KSekoN3JzJPZCvBj9\nebMtS4fCNGz6VHwQTNwbTeNxqwvD2r4K+yuPAVlIya07GrVoSJrZEPCPwGDYfp+7f9zMrgC+BKwB\nDgLvc/cpMxsEvgBcC5wA/pu7v9Kh+iXlqgNRYOS1RQSc3+IrQKUYLjrBuc9ZffjVt0pr3eocf5jU\n9P846Qqa08iI0VngBnf/T8DVwDvNbCuwB3jA3d8KnARuD9vfDpwM6x8I20mP+pmPj0Vd7jyNTS7g\nnA+AeWFXaymeNw5Xv10PhGPNZZ/NTlcbGghJj9Syvz98OXADsC+sfxS4JSzfHB4Tnr/RzPLahpAG\n1E5T7GW1y8Zl6vJxHZC1rjY0eOzRzIpm9gJwDPgG8APgdXevTRcuA8NheRgYBwjPnyLqkksvC5fu\nl9624t+z92nZUEi6e8XdrwZGgOuAn2v1F5vZdjM7YGYHpjnb6o+TlBvZPdZTXUo5X6ECK7/4bNJl\nNK2pkSJ3fx14GtgGrDaz2oGfEWAiLE8AmwHC86uIDuDM/1l73X2Lu2/pZ3CJ5UuWmFqTPW3wtWx+\nSi4akma2zsxWh+VlwDuAQ0Rh+Z6w2W3AY2H58fCY8Px+d8/m3pG2GvmExiZ7VaECa/em/2IWC2lk\nnuRG4FEzKxKF6lfc/QkzexH4kpn9IfBt4JGw/SPAX5rZYeA14NYO1C1Z5USvIn1s9pQsjkXWWBoa\neSvtUr/ebky6DOmS8mg+roUojRk8ley0nyd930F337LU78/57DVJI00J6h2FSvbmRc6n0xIlGV53\nIKd+4nUh52fn5FDcB15WD9bUU0hKIkZ2L711MXPDtfx0Qz/VojGzDKoDNnsDr5nl5PbKOZ00eDK6\nr8/gKac45ZxeW6B4xll9eIq+/QeTLi9RCknJnL79B1l5gefKO0sKyCaZw2V/eu6H1oX2by/SmKRI\nj9PQRjyFpOSP3vRN0UG0eApJyRW1ipqX9ys0tUq7R3IlL3cS7KYs3UohCQpJyRW1JKXdFJKSK2oV\nLYH2WSyFpIhIDIWk5Iq629JuCknJFXW3pd0UkiI9Th8s8RSSki+6+nnTNEQRTyEp+aI3fNPUkoyn\nkJR80Rte2kwhKfmilmTT1N2Op5AU6RJdSCKbFJKSLyntbg+eqrttRcpaboVK0hWkm0JSpIPMYeg1\nn73Py8juMUyhlCkKScmVwkzSFcwpzET3Gl/38Ln3mx7ZPRbVaSk5sqxhgFgKScmVah+p6M6aw/Ce\nC9/HZ3hPFJS1G58lKgX7K80UkpIraWhJWhWGji+efMP3jVGYBi8mG5Q6oBRPISm5sumTY1joyibB\nqrDhubOs3fvM4hsTWpRTyU7DWX5UKRlHISm5M7J7jOJU91tnVoWhE970LViH94xRnKbrwV6YgY3P\nnGXlF5/t7i/OGN1SVnJp0/3ReGB5tNTxe7jUbhnRyr3EN90/xuRdJaYvpuPTmKwKfadhw4NLr7eX\nKCQl12rB1cmwtGo0vtiqDQ+OceTDJSpDdCQorQp9Z2DDAwrHZigkpSfUwnLyrhIzy8LBkmprY4Hm\n0P8mrP/j9oXOpk+NMXlniZnl4S6GLYRlbbjBKvFH2iWeQlJ6Sq2LOXFvaFm2EERWaW9A1mx4aIxj\nHyhxdhXROOUS6jOH4lnY+EcKx1YpJKUn1besjnykRGWw8ZaleedbZ5d9dozj27dxZq011drVeGP7\nKSSl5236ZBQoR+4pUS3Gd8XNwxhkF7qva/c+w+lbruPEf+iLHU9tx4EjuTBzT3q6P6y0S/16uzHp\nMkRmHb27RHUgOoOnPjCL03NHzrupPFo6J7ytGn0VZtSlXsyTvu+gu29Z6verJSmygI2fngueYx8o\nMbUiWk4iICFqJZZHSxSmo4M70j0KSZFF1K7gkzR1p5OhM25ERGIoJEVEYigkRURiNBySZlY0s2+b\n2RPh8RVm9pyZHTazL5vZQFg/GB4fDs9f3pnSRUQ6r5mW5J3AobrHe4AH3P2twEng9rD+duBkWP9A\n2E5EJJMaCkkzGwF+BfiL8NiAG4B9YZNHgVvC8s3hMeH5G8P2IiKZ02hL8kHgHubuhrEGeN3da9eB\nLgPDYXkYGAcIz58K25/DzLab2QEzOzDN2SWWLyLSWYuGpJm9Gzjm7s1dSXQR7r7X3be4+5Z+Btv5\no0VE2qaRyeRvB37VzG4ChoCVwEPAajPrC63FEWAibD8BbAbKZtYHrAJOtL1yEZEuWLQl6e6j7j7i\n7pcDtwL73f3XgaeB94TNbgMeC8uPh8eE5/d7Gk4QFxFZglbmSd4L3G1mh4nGHB8J6x8B1oT1dwM7\nWitRRCQ5TZ277e7/APxDWP4hcN0C25wB/msbahMRSZzOuBERiaGQFBGJoZAUEYmhkBQRiaGQFBGJ\noZAUEYmhkBQRiaGQFBGJoZAUEYmhkBQRiaGQFBGJoZAUEYmhkBQRiaGQFBGJoZAUEYmhkBQRiaGQ\nFBGJoZAUEYmhkBQRiaGQFBGJoZAUEYmhkBQRiaGQFBGJoZAUEYmhkBQRiWHunnQNmNmbwEtJ17FE\na4HjSRexRFmtPat1g2pPws+6+4qlfnNfOytpwUvuviXpIpbCzA6o9u7Kat2g2pNgZgda+X51t0VE\nYigkRURipCUk9yZdQAtUe/dltW5Q7Uloqe5UHLgREUmrtLQkRURSKfGQNLN3mtlLZnbYzHYkXc98\nZvY5MztmZt+rW3epmX3DzF4O/14S1puZfSb8Ld81s2sSrHuzmT1tZi+a2ffN7M4M1T5kZs+b2XdC\n7X8Q1l9hZs+FGr9sZgNh/WB4fDg8f3lStYd6imb2bTN7ImN1v2Jm/2RmL9SOCGfh9RLqWW1m+8zs\nn83skJlta1vt7p7YF1AEfgC8BRgAvgNclWRNC9T4S8A1wPfq1t0P7AjLO4A9Yfkm4O8AA7YCzyVY\n90bgmrC8AvgX4KqM1G7AxWG5H3gu1PQV4Naw/s+A3wnLvwv8WVi+Ffhywq+Zu4EvAk+Ex1mp+xVg\n7bx1qX+9hHoeBX4rLA8Aq9tVe2J/VCh2G/D1usejwGiSNV2gzsvnheRLwMawvJFonifAnwPvXWi7\npL+Ax4B3ZK124CLgW8D1RBOZ++a/doCvA9vCcl/YzhKqdwR4CrgBeCK8EVNfd6hhoZBM/esFWAX8\n6/x9167ak+5uDwPjdY/LYV3arXf3o2F5ElgfllP594Ru3NuIWmSZqD10WV8AjgHfIOpxvO7uMwvU\nN1t7eP4UsKa7Fc96ELgHqIbHa8hG3QAO/L2ZHTSz7WFdFl4vVwA/Av53GOb4CzNbTptqTzokM8+j\nj6LUThEws4uBvwHucvc36p9Lc+3uXnH3q4laZtcBP5dwSYsys3cDx9z9YNK1LNEvuvs1wLuAO8zs\nl+qfTPHrpY9oSOxhd38b8BOi7vWsVmpPOiQngM11j0fCurR71cw2AoR/j4X1qfp7zKyfKCD/yt3/\nNqzORO017v468DRRN3W1mdVOpa2vb7b28Pwq4ESXSwV4O/CrZvYK8CWiLvdDpL9uANx9Ivx7DPi/\nRB9OWXi9lIGyuz8XHu8jCs221J50SH4TuDIc/RsgGrx+POGaGvE4cFtYvo1ovK+2/v3h6NlW4FRd\nc7+rzMyAR4BD7v7puqeyUPs6M1sdlpcRjaUeIgrL94TN5tde+5veA+wPLYeucvdRdx9x98uJXsv7\n3f3XSXndAGa23MxW1JaB/wJ8jwy8Xtx9Ehg3s58Nq24EXqRdtSc1SFw3aHoT0ZHXHwC7kq5ngfr+\nGjgKTBN9Yt1ONG70FPAy8CRwadjWgD8Jf8s/AVsSrPsXiboX3wVeCF83ZaT2XwC+HWr/HvD7Yf1b\ngOeBw8D/AQbD+qHw+HB4/i0peN38MnNHt1Nfd6jxO+Hr+7X3YhZeL6Geq4ED4TXzVeCSdtWuM25E\nRGIk3d0WEUk1haSISAyFpIhIDIWkiEgMhaSISAyFpIhIDIWkiEgMhaSISIz/D5CXmJbfqFt8AAAA\nAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x11324a668>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"resp = urllib.request.urlopen('http://sketchapp.me/wp-content/uploads/2015/11/1-43bjDRA6O2BvOryiUCwpNw.png')\n",
	"image = np.asarray(bytearray(resp.read()), dtype=\"uint8\")\n",
	"image = cv2.imdecode(image, cv2.IMREAD_UNCHANGED)\n",
	"# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
	"# plt.imshow(image);\n",
	"plt.imshow(image[:,:,1])"
	]
	},
	{
	"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": "3.6.1"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}