{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport cv2\nimport operator\n\n\nsamples = pd.read_csv('../input/sample_submission.csv')\nsamples.sample(10)\n\n\n\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "#parameters\nblock_size = 2\nk_size = 3\nk =0.04\ndef featurize(img_file):\n    img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)\n    img = np.float32(img)\n    dst = cv2.cornerHarris(img,block_size,k_size,k)\n    return -1 * dst.mean()\n    \ndef make_filename(set_id,day_id):\n    return \"../input/test_sm/set{0}_{1}.jpeg\".format(set_id,day_id)\n\ndef reorder(set_id):\n    order_day = { d : d / 10 for d in range(1,6)}\n    for d in order_day:\n        order_day[d] = featurize(make_filename(set_id, d ))\n    ordered_day = sorted(order_day.items(), key=operator.itemgetter(1))\n    return \"{0} {1} {2} {3} {4}\".format(*[d[0] for d in ordered_day])\n    \n\n\n        "
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "samples['day'] = samples['setId'].map(reorder)\nsamples.sample(10)"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "samples.to_csv('naive_submit.csv', index=False)"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": ""
 }
],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}}, "nbformat": 4, "nbformat_minor": 0}