Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from PIL import Image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HNH9IkZCgJWHLB/ox//3nf/5n685DYkkfgL3QpKU6Y14rGrq1teWMdpRKpdLptI2yCRul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\nRK5CYII=\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=256x256 at 0x7F67D0351DA0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = Image.open('15033649_10211078424907271_533264582_n.jpg')\n",
    "i2 = np.array(i)\n",
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import skimage.morphology as mor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Image contains values other than 0 and 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-b50cc9a81de3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mskeletonize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3.5/site-packages/skimage/morphology/_skeletonize.py\u001b[0m in \u001b[0;36mskeletonize\u001b[0;34m(image)\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Skeletonize requires a 2D array'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Image contains values other than 0 and 1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_fast_skeletonize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Image contains values other than 0 and 1"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.imshow(mor.skeletonize((i2//255)[:,:,0])//255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(list((i2//255).flatten()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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