{
 "metadata": {
  "name": "",
  "signature": "sha256:f3bc4f45b166c31af2f396fe04ac8f168b7a0b691271c7d0253e6d73e1ef037e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle\n",
      "from matplotlib import pyplot as pl\n",
      "from skimage import data\n",
      "import numpy as np\n",
      "%run util.py\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model = pickle.load(open(MODEL_NAME, 'r'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# usar brush 20\n",
      "imagem = data.imread('josino.png', as_grey=True)\n",
      "# TODO: por que?\n",
      "imagem = 16 - imagem*16\n",
      "\n",
      "fig = pl.figure(figsize=(3, 3))\n",
      "ax = fig.add_subplot(1,1,1)\n",
      "ax.imshow(imagem, cmap=CMAP, interpolation='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.image.AxesImage at 0x7fb55fc98f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAL8AAADDCAYAAADTCsC8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npCyWn5TF8pOyWH5SFstPyvofb9quReTv0FUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb55fc45550>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "model.predict(imagem.ravel())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "array([1])"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}