PLOTVI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GeForce GTX TITAN (CNMeM is disabled, CuDNN 4007)\n",
      "/home/d/nolearn/local/lib/python2.7/site-packages/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the theano.tensor.signal.pool module.\n",
      "  \"downsample module has been moved to the theano.tensor.signal.pool module.\")\n"
     ]
    }
   ],
   "source": [
    "import gp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_image, input_prob, input_gold, input_rhoana, dojo_bbox = gp.Legacy.read_dojo_data() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HJYf97Cin/+5Ph1SS7Hts8sw/mX2N0XRyxzcrd/xj5bvfElUAC0G11h75WfPxwlUtefMg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\nqD7bwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ba0173550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.Util.view_labels(input_rhoana[8], [68,70], crop=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "binary = gp.Util.view_labels(input_rhoana[8], [68,70], crop=False, return_it=True)\n",
    "splitted = np.array(binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "binary[binary==1] = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b646966d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b647f8d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b645cc9d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IHay7q/cWDda+GDizmj8G2AWcMoPqf2717xzgXmD5TKm9qulPgH8ENs+kn5tnpya2EM4G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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b6464d1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(splitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9781710987371827"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(binary.astype(np.uint64), input_gold[8].astype(np.uint64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1039548495432179"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(splitted.astype(np.uint64), input_gold[8].astype(np.uint64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b644f3f50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MlGRSWrugaEDBVAgAFzcqE4yVnUxdvudzSLviX8pgg9kvauLj0CBW2d8TB0O/BxJQRyGY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\nQjCzzIVgZpkLwcyyD2hQjnxZSy03AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b64581790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(input_gold[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h/2ajY+yiteq7Rv/pYZ2MA61YilOkoFJUcksnoJpt2LNKjU4TlVBJE02zvjQ6RtyZp2Ri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\n+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4Y+1nSsV0VP2guZjpGNKRmZEGLSazHIzuZ9nkaHi78uW8X+kyV4upgKIRy4fF4anI6g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14497379d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp.Util.view((input_gold[8] + splitted).astype(np.uint64),large=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1449587410>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZLn+Zzwfk2XGeEKVQUiQUwpGXzPtOBZT3YYBUnQFSdXx8bQcxaRBdCl2gvt46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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1449737990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(input_gold[8] + binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "splitted1 = np.array(splitted)\n",
    "splitted2 = np.array(splitted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "splitted1[splitted1==1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f14484ae150>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zn/B1vJ7/fZVhWc3uW5clNT6pKKkxCJIagyCpMQiSGoMgqTEIkhqDIKkxCJKa/wGM2UwD\n+cE96AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14495b2350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(splitted1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "splitted2[splitted2==2] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1449757d50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gqp4BvgC8nsWRfQ2wLsl/Ap8B3pTkVmDfIsje9FEI9wOrkpyR5HhgA7C9hxwzCQc2/Xbg\nmm7+amDbwPiGJMcnORNYBdw3XyFfwCeB8ar62MDYgs+f5JX7z8InORG4mOkTbws+e1XdUFWnV9Vr\nmP47vaOq3gV8jgWe/QA9nYm9lOmz3xPA9X2fWT1MvtuBbwHfB54A3g2cDNzT5b4b+PGB5TcyfZZ4\nN3BJz9nXAD9k+urNLuCB7s/7FQs9P/C6Lu8u4GvAH3TjCz77Qe/jIv7/KsOiyu6ty5IaTypKaiwE\nSY2FIKmxECQ1FoKkxkKQ1FgIkhoLQVLzf+32Z6rzk+K0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14484e1e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(splitted2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47895063243286246"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(splitted1.astype(np.uint64), input_gold[8].astype(np.uint64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8530532196776562"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(splitted2.astype(np.uint64), input_gold[8].astype(np.uint64))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f144832c310>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JxO7MAc5kO4BtZ55EVLpd7fAFXfmCrqWjrUQ4c4Am7Q2/6ljCj/9OmtwvdnGl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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14484eb590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,3,1)\n",
    "imshow(input_gold[8])\n",
    "plt.subplot(1,3,2)\n",
    "imshow(splitted)\n",
    "plt.subplot(1,3,3)\n",
    "imshow(binary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint32')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_gold.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "splitted.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binary.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1039548495432179"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(splitted.astype(np.uint64).flatten(), input_gold[8].astype(np.uint64).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9781710987371827"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(binary.astype(np.uint64).flatten(), input_gold[8].astype(np.uint64).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.  2.]\n",
      "[ 0.  1.  2.]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24\n",
      " 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49\n",
      " 50 51 52 53 54 55 56]\n"
     ]
    }
   ],
   "source": [
    "print np.unique(binary)\n",
    "print np.unique(splitted)\n",
    "print np.unique(input_gold[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0 41 43 44 45 50 51 52 53 55]\n"
     ]
    }
   ],
   "source": [
    "test = input_gold[8]*(binary>0)\n",
    "print np.unique(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b64160c10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q2dk0/ng8R49kj4h+4GLgi03DPZH9qDoKYQGwr+n2M9VYt5ubmSPQeNIBc6vxYx/PMF3y\neCJigMaezkPAvF7IX+1y7wT2Azsycxc9kh24BbgGaL501yvZAU8qtqKrr9dGxMnAPcBV1Z7CsXm7\nMn9mvpSZ59LYq3lfRCylB7JHxCXASLV3drxv9+u67M3qKIRh4PSm2/3VWLcbiYh5ABExHzhQjQ8D\npzUtV/vjiYgZNMrgzszcXA33TH6AzHweuA94D72RfQmwPCK+B3wF+GBE3Ans74HsRR2F8G1gYUS8\nNSJmAauALTXkGE8wuum3AJdX85cBm5vGV0XErIg4A1gIPDJVIV/BHcCuzLy1aazr80fEm4+ehY+I\nE4ELaJx46/rsmXl9Zp6emW+j8Tu9PTM/CnyNLs8+Sk1nYi+kcfZ7ELiu7jOrY+S7G/gB8CLwfeAK\nYA6wrcq9FXhj0/JraZwl3g0sqzn7EuAIjas3O4HvVP+/39Tt+YFzqrw7gX8HPl2Nd332Yx7H+/nZ\nVYaeyu5LlyUVnlSUVFgIkgoLQVJhIUgqLARJhYUgqbAQJBUWgqTi/wFnhcnBPLV4FgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b643c8210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 43, 52\n",
    "\n",
    "gt = np.zeros_like(test)\n",
    "gt[test==43] = 1\n",
    "gt[test==52] = 2\n",
    "\n",
    "imshow(gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6931450975260867"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(binary.astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8416733242191282"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(splitted.astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b6475af10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DvvEb6mbcVqWqKiNGHA7Alp/5kOFg5StPquDJ/9l/zmrBz1K78OX7ql1O5Yx5HfDaqVY7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\nB4KZJQ4EM0scCGaWOBDMLHEgmFny/2l5xCUtmURwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b64593c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(binary+gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b641ca7d0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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53AImjqztlkFXrRp2KDeuOg/WvF50KVY1B0LNHTp8FUsX3tim7ZmNB3LQmmcqX0lsYsrh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\nwT97AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b643be1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(splitted+gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test = binary.copy()\n",
    "test[gt==1] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b640acc50>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PIZhZ4kIws8SFYGaJC8HMEheCmSUuBDNLXAhmlrgQzCxxIZhZ4kIws8SFYGaJC8HMkpwL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\nF4KZJS4EM0v+DyPaXVrTp1qcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b64200090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi(test.astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f6b64061b10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AMrTnHaBXLoQBa887QBna8w5Qhva8A5ShPe8AvfIhg5klLgQzS3L+Za9mlpe6+u3PZlZ/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\nSMBiZgaDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b6406da10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = test==5\n",
    "imshow(mask+binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.Util.vi((mask+binary).astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import mahotas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b640d21d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c1d1f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0fcc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0ac0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bfd35d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5befcad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c1e6050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bdd5510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5beb1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bfaeb50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bde9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c25d6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c136a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c08ff10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0e3450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bcf1b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bca5e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bbd9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5baf6f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Si6RNkibkkzplaZC0RtJGSRskXZuN9/r8kgZL+qWk9Vn+m+sle0meAZKekdSUPa+b7ABE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\nF4KZJS4EM0tcCGaW/B9tiCNQLL6ecwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bbb25d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PRMT9EfH+anzM54+ISRHx/YjYVOW/qlOyN+SZEBF3R8Sa6nnHZAcgM/frg4ES+jFwAnAw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\nSYWFIKmwECQV/w9CsBqWcaR99wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bd49310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b6419e550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bd3e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bdbba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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PIEkbJDVnj+smOwAR0a8TxRJ6ETgJGAI8C5zS3zm6yfj7wBnA8yVjS4BvZvPzgFuy+QnA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c058f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cC5y4v3MMkfEPgVOB+5vGrgE+Vs1fAVxdzc8GNtH4BOuu6r1FjdmPAU6t5g8HtgAndlD+\nF1T/PQi4C5jTKdmrTB8B/hFY20n/bnZOdewhnA70ZubDmfkssApYWEOOvcrMO4HHdhteCHRX893A\nedX8AmBVZu7IzD6gl8Z7rEVmbsvMe6v5J4HNwEw6J/+vq9kpNH54PEaHZI+ImcCbgOubhjsi+051\nFMIMYGvT40eqsfFuWmYOQOObDphWje/+fvoZJ+8nIrpo7OncBUzvhPzVLvcmYBuwMTN76JDswLXA\nR4HmS3edkh3wpOJYjOvrtRFxOHAzcFm1p7B73nGZPzOfz8zTaOzVvC4i5tIB2SNiPjBQ7Z3tcX2/\nybjL3qyOQugHjmt6PLMaG+8GImI6QEQcA2yvxvuBY5uWq/39RMRkGmWwMjPXVMMdkx8gMx8Hvg28\nis7IPgdYEBH/DXwdODMiVgLbOiB7UUch/BCYFREvjYhDgEXA2hpyDCXYtenXAhdV84uBNU3jiyLi\nkIg4HpgF3L2/Qu7FV4CezLyuaWzc54+Io3eehY+IQ4GzaZx4G/fZM/PKzDwuM19G49/0hsx8D3AL\n4zz7Lmo6E3sOjbPfvcCSus+sDpLvRuCnwNPAT4D3AlOB26rc64Ejm5ZfSuMs8WZgXs3Z5wDP0bh6\nswm4p/p6HzXe8wOvrPJuAu4D/qIaH/fZd3sfZ/Dbqwwdld1blyUVnlSUVFgIkgoLQVJhIUgqLARJ\nhYUgqbAQJBUWgqTi/wHAiRKH9ffZHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bebc450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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HOkc3Gf8AOBN4qWLsduBbxfQc4LZiehywgfInWJeK96aM2U8EziymjwU2A6c2UP6PFf89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\nlvwfpT0RKAVyALIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bd9bb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/BdgBvKyN8j+3+vM5wP3AzHbJXmX6APBPwLp2+n+zf6pjC2EG0J2ZuzLzaWA1ML+GHIeV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c122e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/+dWfzwPuAWZ3SvYq04eAvwXWddL/mz1THVsI5wC9mbk9M58BVgMLasixX5l5N/D4XsML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\nsBAkFRaCpMJCkFRYCJIKC0FS8X8xkQYheoYE5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bf2f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bfac150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bf12350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bcb5990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b64403e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bfc3510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c225a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c059f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bcb0450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0d4b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bcf6e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bca5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = test==5\n",
    "mask_orig = mask.copy()\n",
    "values = []\n",
    "\n",
    "for i in xrange(40):\n",
    "    mask = mahotas.erode(mask)\n",
    "    vi = gp.Util.vi((mask+binary).astype(np.uint64).flatten(), gt.astype(np.uint64).flatten())\n",
    "    #print i, vi\n",
    "    values.append(vi)\n",
    "    imshow(mask+binary)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6b5be476d0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hYY5y9ycyu2N3PeuKM47Xs4a7fwo8BXyTGF7PGpk4nwS+GbPreQxwSube39+B\n48xsFLCssdeyWMl980AoM2tNGAg1tkjvnTMza5tpJWFm7YATgNejjWoLxpaf5mOB8zPPzwOeyP4H\nEdkizswPY43Ticc1/SvwprvfUmtfHK/nV+KM2/U0s91qShlmth3QH5hBzK5nPXHOjNP1dPer3L2T\nu+9PyJPPufsPgHE09lq6e1EeQAUwF3gLGFqs921kjPsBMwk/mLPjFCfwEKGn0VpgIaFnx87As5nr\nOgnYKaZxPgC8lrm2Ywj1wyhjPAbYWOv/enrm53OXOF3PrcQZt+vZPRPbDGAWcHlmf9yuZ31xxup6\n1oq3LzC2qddSg5hERBJIN1RFRBJIyV1EJIGU3EVEEkjJXUQkgZTcRUQSSMldRCSBlNxFRBJIyV1E\nJIH+H1ZddDRQBBbYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b64099e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f143a62f150>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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q2dk0/ng8R49kj4h+4GLgi03DPZH9qDoKYQGwr+n2M9VYt5ubmSPQeNIBc6vxYx/PMF3y\neCJigMaezkPAvF7IX+1y7wT2Azsycxc9kh24BbgGaL501yvZAU8qtqKrr9dGxMnAPcBV1Z7CsXm7\nMn9mvpSZ59LYq3lfRCylB7JHxCXASLV3drxv9+u67M3qKIRh4PSm2/3VWLcbiYh5ABExHzhQjQ8D\npzUtV/vjiYgZNMrgzszcXA33TH6AzHweuA94D72RfQmwPCK+B3wF+GBE3Ans74HsRR2F8G1gYUS8\nNSJmAauALTXkGE8wuum3AJdX85cBm5vGV0XErIg4A1gIPDJVIV/BHcCuzLy1aazr80fEm4+ehY+I\nE4ELaJx46/rsmXl9Zp6emW+j8Tu9PTM/CnyNLs8+Sk1nYi+kcfZ7ELiu7jOrY+S7G/gB8CLwfeAK\nYA6wrcq9FXhj0/JraZwl3g0sqzn7EuAIjas3O4HvVP+/39Tt+YFzqrw7gX8HPl2Nd332Yx7H+/nZ\nVYaeyu5LlyUVnlSUVFgIkgoLQVJhIUgqLARJhYUgqbAQJBUWgqTi/wFnhcnBPLV4FgAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f143963eed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import partition_comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5be0c790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5ba8ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bd61590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bae4bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bc0f590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bf1f6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0d4cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5be4edd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5be1ea50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bfb5f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c230490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c180bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c029f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c10bd90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c169fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0ef150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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B51YIZtb3+JDBzBIXgpkluRSCpGmSXpL0sqR5eWTYG0l3SOqQ9ELJ2FBJqyVtlLRK0pCS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\nEheCmSUuBDNL/h8RvyZz57zEfwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bbec890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c2975d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c1df710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Si6RNkibkkzplaZC0RtJGSRskXZuN9/r8kgZL+qWk9Vn+m+sle0meAZKekdSUPa+b7ABE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\nF4KZJS4EM0tcCGaW/B9tiCNQLL6ecwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c165a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PRMT9EfH+anzM54+ISRHx/YjYVOW/qlOyN+SZEBF3R8Sa6nnHZAcgM/frg4ES+jFwAnAw\ncA/wwv2dY4iMfwq8BLivYexa4CPV8uXANdXyTGATA59g3VW9t6gx+7HAS6rlw4EtwAs7KP+zqv8e\nBNwBzOqU7FWmDwD/BKzppL83Ox917CGcDvRk5kOZ+RSwEphXQ469yszbgUd3G54HLK+WlwPnVctz\ngZWZuSMze4EeBt5jLTJzW2beUy0/DmwGptM5+X9bLU5i4IfHo3RI9oiYDrwOuKFhuCOy71RHIUwD\ntjY8f6QaG+umZGY/DHzTAVOq8d3fTx9j5P1ERBcDezp3AFM7IX+1y70J2AZsyMxuOiQ7cB3wYaDx\n0l2nZAc8qdiMMX29NiIOB24BLq32FHbPOybzZ+YzmXkaA3s1fxYRs+mA7BFxLtBf7Z3tcX2/wZjL\n3qiOQugDjm94Pr0aG+v6I2IqQEQcC2yvxvuA4xrm1f5+ImIiA2WwIjNXV8Mdkx8gM38NfBN4GZ2R\nfRYwNyL+G/gy8OqIWAFs64DsRR2FcBcwIyJOiIhDgAXAmhpyDCXYtenXABdWywuB1Q3jCyLikIg4\nEZgB3Lm/Qu7FF4DuzLy+YWzM54+IY3aehY+IQ4GzGDjxNuazZ+YVmXl8Zj6fgb/T6zPzHcCtjPHs\nu6jpTOwcBs5+9wCL6j6zOki+m4CfAE8ADwPvBCYDt1W51wFHNcxfzMBZ4s3A2TVnnwU8zcDVm03A\n3dWf99FjPT/w4irvJuBe4EPV+JjPvtv7OIPfX2XoqOzeuiyp8KSipMJCkFRYCJIKC0FSYSFIKiwE\nSYWFIKmwECQV/w9CsBqWcaR99wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bf13f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5ba91490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bb81bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bbfbf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PIEkbJDVnj+smOwAR0a8TxRJ6ETgJGAI8C5zS3zm6yfj7wBnA8yVjS4BvZvPzgFuy+QnA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5ba3e3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cC5y4v3MMkfEPgVOB+5vGrgE+Vs1fAVxdzc8GNtH4BOuu6r1FjdmPAU6t5g8HtgAndlD+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\nhYUgqbAQJBUWgqTi/wHAiRKH9ffZHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bfe8910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HOkc3Gf8AOBN4qWLsduBbxfQc4LZiehywgfInWJeK96aM2U8EziymjwU2A6c2UP6PFf89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\nlvwfpT0RKAVyALIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bbb0250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/BdgBvKyN8j+3+vM5wP3AzHbJXmX6APBPwLp2+n+zf6pjC2EG0J2ZuzLzaWA1ML+GHIeV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bcd6fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/+dWfzwPuAWZ3SvYq04eAvwXWddL/mz1THVsI5wC9mbk9M58BVgMLasixX5l5N/D4XsML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\nsBAkFRaCpMJCkFRYCJIKC0FS8X8xkQYheoYE5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0afc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bcb6cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bfbda50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0bcf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bf14490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c177bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c02cf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bb563d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5baf3150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bb2d750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bebc950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R5q+qx6rqhcys1fx6kjWMQfYkrwSmu7WzfY7vD1h02Qf1UQi7gGcM3F7ZjS1200lWACQ5\nCfhWN74LOGVgud7fT5IjmCmDG6tqUzc8NvkBquph4OPAixiP7KuBC5P8N/BB4KVJbgR2j0H2po9C\nuAtYleSZSY4C1gGbe8gxm7Bn028GLu3mLwE2DYyvS3JUklOBVcCdhyrkE3gvsL2q3jUwtujzJ3na\n43vhkxwDnMvMjrdFn72qrqyqZ1TVs5j5P72lql4PfJRFnn0PPe2JPZ+Zvd+TwBV971ndT76bgG8A\nPwa+BrwBOBG4vct9G/DUgeXXM7OXeAdwXs/ZVwM/ZebozTbg7u7v+2cXe37geV3ebcC9wF9244s+\n+17v4yX8/1GGscruqcuSGncqSmosBEmNhSCpsRAkNRaCpMZCkNRYCJIaC0FS83+1moylJgafGwAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5bc5cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = test==5\n",
    "mask_orig = mask.copy()\n",
    "values_ri = []\n",
    "\n",
    "for i in xrange(40):\n",
    "    mask = mahotas.erode(mask)\n",
    "    ri = partition_comparison.rand_index((mask+binary).astype(np.uint64).ravel(), gt.astype(np.uint64).ravel())\n",
    "    #print i, vi\n",
    "    values_ri.append(ri)\n",
    "    imshow(mask+binary)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6b5bec57d0>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V8PDDYT74v/kbWLs26kQiEmdtDsuYWSdgNTAS2AwsACa7e13WOt2B/wIucfdNZna0u3+k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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b6441c0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(values_ri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mask = test==5\n",
    "mask_orig = mask.copy()\n",
    "values_split_vi = []\n",
    "values_merge_vi = []\n",
    "values_f1_vi = []\n",
    "\n",
    "for i in xrange(1):#(40):\n",
    "    mask = mahotas.erode(mask)\n",
    "    v = segmentation_metrics((mask+binary).astype(np.uint64).ravel(), gt.astype(np.uint64).ravel())\n",
    "    \n",
    "    #print i, vi\n",
    "    values_split_vi.append(v['VI']['split'])\n",
    "    values_merge_vi.append(v['VI']['merge'])    \n",
    "    values_f1_vi.append(v['VI']['F-score'])    \n",
    "    \n",
    "    #imshow(mask+binary)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6018350774867397]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values_split_vi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6b5bcb6cd0>]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t3T0vX0A5sBhYCozN13kzzLgPMJ/wF/OtOOUEHiPMNPoKWE6Y2dEWeCHZrtOB\nNjHN+QiwINm2TxHGD6PMeDywJeX/9RvJv5/t4tSe28kZt/Y8NJltHvAmcFXy9bi1Z105Y9WeKXl7\nAVMb2pa6iUlEpAjpgqqISBFScRcRKUIq7iIiRUjFXUSkCKm4i4gUIRV3EZEipOIuIlKEVNxFRIrQ\n/wPrZOgLdyqC/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c0df090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(values_merge_vi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6b5bf88c50>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3ErNhA5xwAuy1V1gbvpl+NhNJPW3WIbRuDbNnw/r1YUz855/HTiQiMai4l6Btt4WHHoId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\nuIuIlCAVdxGREqTiLiJSglTcRURK0P8H/NlmHe92YHsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6b5c257550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(values_f1_vi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
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    "import numpy as np\n",
    "from scipy.ndimage.filters import maximum_filter\n",
    "import fast64counter\n",
    "import mahotas\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "import time\n",
    "import glob\n",
    "import os\n",
    "import cPickle\n",
    "\n",
    "def thin_boundaries(im, mask):\n",
    "    im = im.copy()\n",
    "    assert (np.all(im >= 0)), \"Label images must be non-negative\"\n",
    "\n",
    "    # make sure image is not all zero\n",
    "    if np.sum(im) == 0:\n",
    "       im[:] = 1.0\n",
    "       im[0,:] = 2.0\n",
    "\n",
    "    # repeatedly expand regions by one pixel until the background is gone\n",
    "    while (im[mask] == 0).sum() > 0:\n",
    "        zeros = (im == 0)\n",
    "        im[zeros] = maximum_filter(im, 3)[zeros]\n",
    "\n",
    "    # make sure image is not constant to avoid zero division\n",
    "    if len(np.unique(im))==1:\n",
    "        im[0,:] = 5\n",
    "    return im\n",
    "\n",
    "def Rand(pair, gt, pred, alpha):\n",
    "    '''Parameterized Rand score\n",
    "\n",
    "    Arguments are pairwise fractions, ground truth fractions, and prediction\n",
    "    fractions.\n",
    "\n",
    "    Equation 3 from Arganda-Carreras et al., 2015\n",
    "    alpha = 0 is Rand-Split, alpha = 1 is Rand-Merge\n",
    "\n",
    "    '''\n",
    "\n",
    "    return np.sum(pair ** 2) / (alpha * np.sum(gt ** 2) +\n",
    "                                (1.0 - alpha) * np.sum(pred ** 2))\n",
    "\n",
    "def VI(pair, gt, pred, alpha):\n",
    "    ''' Parameterized VI score\n",
    "\n",
    "    Arguments are pairwise fractions, ground truth fractions, and prediction\n",
    "    fractions.\n",
    "\n",
    "    Equation 6 from Arganda-Carreras et al., 2015\n",
    "    alpha = 0 is VI-Split, alpha = 1 is VI-Merge\n",
    "    '''\n",
    "\n",
    "    pair_entropy = - np.sum(pair * np.log(pair))\n",
    "    gt_entropy = - np.sum(gt * np.log(gt))\n",
    "    pred_entropy = - np.sum(pred * np.log(pred))\n",
    "    mutual_information = gt_entropy + pred_entropy - pair_entropy\n",
    "\n",
    "    vi = mutual_information / ((1.0 - alpha) * gt_entropy + alpha * pred_entropy)\n",
    "    \n",
    "    out = [pair_entropy, gt_entropy, pred_entropy, mutual_information, vi]\n",
    "    \n",
    "    return out\n",
    "\n",
    "def segmentation_metrics(ground_truth, prediction, seq=False):\n",
    "    '''Computes adjusted FRand and VI between ground_truth and prediction.\n",
    "\n",
    "    Metrics from: Crowdsourcing the creation of image segmentation algorithms\n",
    "    for connectomics, Arganda-Carreras, et al., 2015, Frontiers in Neuroanatomy\n",
    "\n",
    "    ground_truth - correct labels\n",
    "    prediction - predicted labels\n",
    "\n",
    "    Boundaries (label == 0) in prediction are thinned until gone, then are\n",
    "    masked to foreground (label > 0) in ground_truth.\n",
    "\n",
    "    Return value is ((FRand, FRand_split, FRand_merge), (VI, VI_split, VI_merge)).\n",
    "\n",
    "    If seq is True, then it is assumed that the ground_truth and prediction are\n",
    "    sequences that should be processed elementwise.\n",
    "\n",
    "    '''\n",
    "\n",
    "    # make non-sequences into sequences to simplify the code below\n",
    "    if not seq:\n",
    "        ground_truth = [ground_truth]\n",
    "        prediction = [prediction]\n",
    "\n",
    "    counter_pairwise = fast64counter.ValueCountInt64()\n",
    "    counter_gt = fast64counter.ValueCountInt64()\n",
    "    counter_pred = fast64counter.ValueCountInt64()\n",
    "\n",
    "    for gt, pred in zip(ground_truth, prediction):\n",
    "        mask = (gt > 0)\n",
    "        pred = thin_boundaries(pred, mask)\n",
    "        gt = gt[mask].astype(np.int32)\n",
    "        pred = pred[mask].astype(np.int32)\n",
    "        counter_pairwise.add_values_pair32(gt, pred)\n",
    "        counter_gt.add_values_32(gt)\n",
    "        counter_pred.add_values_32(pred)\n",
    "\n",
    "    # fetch counts\n",
    "    frac_pairwise = counter_pairwise.get_counts()[1]\n",
    "    frac_gt = counter_gt.get_counts()[1]\n",
    "    frac_pred = counter_pred.get_counts()[1]\n",
    "\n",
    "    # normalize to probabilities\n",
    "    frac_pairwise = frac_pairwise.astype(np.double) / frac_pairwise.sum()\n",
    "    frac_gt = frac_gt.astype(np.double) / frac_gt.sum()\n",
    "    frac_pred = frac_pred.astype(np.double) / frac_pred.sum()\n",
    "\n",
    "    alphas = {'F-score': 0.5, 'split': 0.0, 'merge': 1.0}\n",
    "\n",
    "    Rand_scores = {k: Rand(frac_pairwise, frac_gt, frac_pred, v) for k, v in alphas.items()}\n",
    "    VI_scores = {k: VI(frac_pairwise, frac_gt, frac_pred, v) for k, v in alphas.items()}\n",
    "\n",
    "    return {'Rand': Rand_scores, 'VI': VI_scores}\n",
    "\n",
    "\n",
    "# Just doing one, so the interface is easier for the network training\n",
    "# And yes that means I should refactor the function above... when I have time\n",
    "def quick_Rand(gt, pred, seq=False):\n",
    "    counter_pairwise = fast64counter.ValueCountInt64()\n",
    "    counter_gt = fast64counter.ValueCountInt64()\n",
    "    counter_pred = fast64counter.ValueCountInt64()\n",
    "\n",
    "    mask = (gt > 0)\n",
    "    pred = thin_boundaries(pred, mask)\n",
    "    gt = gt[mask].astype(np.int32)\n",
    "    pred = pred[mask].astype(np.int32)\n",
    "    counter_pairwise.add_values_pair32(gt, pred)\n",
    "    counter_gt.add_values_32(gt)\n",
    "    counter_pred.add_values_32(pred)\n",
    "\n",
    "    # fetch counts\n",
    "    frac_pairwise = counter_pairwise.get_counts()[1]\n",
    "    frac_gt = counter_gt.get_counts()[1]\n",
    "    frac_pred = counter_pred.get_counts()[1]\n",
    "\n",
    "    # normalize to probabilities\n",
    "    frac_pairwise = frac_pairwise.astype(np.double) / frac_pairwise.sum()\n",
    "    frac_gt = frac_gt.astype(np.double) / frac_gt.sum()\n",
    "    frac_pred = frac_pred.astype(np.double) / frac_pred.sum()\n",
    "\n",
    "    return Rand(frac_pairwise, frac_gt, frac_pred, 0.5)\n",
    "\n",
    "def Rand_membrane_prob(im_pred, im_gt):\n",
    "    Rand_score = []\n",
    "    for thresh in np.arange(0,1,0.1):\n",
    "        # white regions, black boundaries\n",
    "        im_seg = im_pred>thresh\n",
    "        # connected components\n",
    "        seeds, nr_regions = mahotas.label(im_seg)\n",
    "        result = quick_Rand(im_gt, seeds)        \n",
    "        Rand_score.append(result)\n",
    "\n",
    "    return np.max(Rand_score)\n",
    "\n",
    "def run_evaluation_boundary_predictions(network_name):\n",
    "    pathPrefix = './AC4_small/'\n",
    "    img_gt_search_string = pathPrefix + 'labels/*.tif'\n",
    "    img_pred_search_string = pathPrefix + 'boundaryProbabilities/'+network_name+'/*.tif'\n",
    "\n",
    "    img_files_gt = sorted( glob.glob( img_gt_search_string ) )\n",
    "    img_files_pred = sorted( glob.glob( img_pred_search_string ) )\n",
    "\n",
    "    allVI = []\n",
    "    allVI_split = []\n",
    "    allVI_merge = []\n",
    "\n",
    "    allRand = []\n",
    "    allRand_split = []\n",
    "    allRand_merge = []\n",
    "\n",
    "    for i in xrange(np.shape(img_files_pred)[0]):\n",
    "        print img_files_pred[i]\n",
    "        im_gt = mahotas.imread(img_files_gt[i])\n",
    "        im_pred = mahotas.imread(img_files_pred[i])\n",
    "        im_pred = im_pred / 255.0\n",
    "\n",
    "        VI_score = []\n",
    "        VI_score_split = []\n",
    "        VI_score_merge = []\n",
    "\n",
    "        Rand_score = []\n",
    "        Rand_score_split = []\n",
    "        Rand_score_merge = []\n",
    "    \n",
    "        start_time = time.clock()\n",
    "\n",
    "        for thresh in np.arange(0,1,0.05):\n",
    "            # white regions, black boundaries\n",
    "            im_seg = im_pred>thresh\n",
    "            # connected components\n",
    "            seeds, nr_regions = mahotas.label(im_seg)\n",
    "            \n",
    "            result = segmentation_metrics(im_gt, seeds, seq=False)   \n",
    "            \n",
    "            VI_score.append(result['VI']['F-score'])\n",
    "            VI_score_split.append(result['VI']['split'])\n",
    "            VI_score_merge.append(result['VI']['merge'])\n",
    "\n",
    "            Rand_score.append(result['Rand']['F-score'])\n",
    "            Rand_score_split.append(result['Rand']['split'])\n",
    "            Rand_score_merge.append(result['Rand']['merge'])\n",
    "\n",
    "        print \"This took in seconds: \", time.clock() - start_time\n",
    "\n",
    "        allVI.append(VI_score)\n",
    "        allVI_split.append(VI_score_split)\n",
    "        allVI_merge.append(VI_score_merge)\n",
    "\n",
    "        allRand.append(Rand_score)\n",
    "        allRand_split.append(Rand_score_split)\n",
    "        allRand_merge.append(Rand_score_merge)\n",
    "        \n",
    "    with open(pathPrefix+network_name+'.pkl', 'wb') as file:\n",
    "        cPickle.dump((allVI, allVI_split, allVI_merge, allRand, allRand_split, allRand_merge), file)\n",
    "    \n",
    "\n",
    "    # for i in xrange(len(allVI)):\n",
    "    #     plt.plot(np.arange(0,1,0.05), allVI[i], 'g', alpha=0.5)\n",
    "    # plt.plot(np.arange(0,1,0.05), np.mean(allVI, axis=0), 'r')\n",
    "    # plt.show()\n",
    "    \n",
    "def run_evaluation_segmentations3D():\n",
    "    # first test how to convert a great boundary segmentation quickly into 3d objects\n",
    "    pathPrefix = './AC4/'\n",
    "    img_gt_search_string = pathPrefix + 'labels/*.tif'\n",
    "    img_pred_search_string = pathPrefix + 'boundaryProbabilities/IDSIA/*.tif'\n",
    "\n",
    "    img_files_gt = sorted( glob.glob( img_gt_search_string ) )\n",
    "    img_files_pred = sorted( glob.glob( img_pred_search_string ) )\n",
    "    \n",
    "    s = 100\n",
    "    img_gt_volume = np.zeros((1024,1024,s))\n",
    "    img_pred_volume = np.zeros((1024,1024,s))\n",
    "\n",
    "    for i in xrange(s):\n",
    "        print img_files_gt[i]\n",
    "        # read image\n",
    "        img_gt = mahotas.imread(img_files_gt[i])\n",
    "        img_gt_volume[:,:,i] = img_gt\n",
    "        # compute gradient to get perfect segmentation\n",
    "        img_gt = np.gradient(img_gt)\n",
    "        img_gt = np.sqrt(img_gt[0]**2 + img_gt[1]**2)\n",
    "        #img_gt = mahotas.morph.erode(img_gt == 0)\n",
    "        img_pred_volume[:,:,i] = img_gt == 0\n",
    "\n",
    "\n",
    "    all_VI = []\n",
    "    for i in xrange(20):\n",
    "        print i\n",
    "        if i>0:\n",
    "            for j in xrange(s):\n",
    "                img_pred_volume[:,:,j] = mahotas.morph.erode(img_pred_volume[:,:,j]>0)\n",
    "\n",
    "    # connected component labeling\n",
    "    print \"labeling\"\n",
    "    seeds, nr_objects = mahotas.label(img_pred_volume)\n",
    "    # compute scores\n",
    "    print \"computing metric\"\n",
    "    result = segmentation_metrics(img_gt_volume, seeds, seq=False)   \n",
    "    print result\n",
    "    all_VI.append(result['VI']['F-score'])\n",
    "    return seeds\n"
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