scripts/tag_analysis_on_10k_tags.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of 10K tags (after we validated them)\n",
    "\n",
    "Here we analyze how well our net (trained on the manual tags) performed on 10K random images. We found ground truth by manually going through the predictions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os.path\n",
    "import re\n",
    "import sys\n",
    "import tarfile\n",
    "import time\n",
    "import multiprocessing as mp\n",
    "import itertools\n",
    "import csv\n",
    "\n",
    "import tensorflow.python.platform\n",
    "from six.moves import urllib\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import h5py\n",
    "\n",
    "import glob\n",
    "import cPickle as pickle\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "from tensorflow.python.platform import gfile\n",
    "from run_inference import predict_star, predict\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Animals',\n",
       " 'Objects',\n",
       " 'Maps',\n",
       " 'Text',\n",
       " 'Nature',\n",
       " 'People',\n",
       " 'Seals',\n",
       " 'Miniatures',\n",
       " 'Diagrams',\n",
       " 'Architecture',\n",
       " 'Decorations',\n",
       " 'Landscape']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get old tags\n",
    "\n",
    "image_to_net_tag = pickle.load(open(\"/data/image_to_tags_10k.pkl\", 'r'))\n",
    "image_to_net_scores = pickle.load(open(\"/data/images_to_scores_10k.pkl\", 'r'))\n",
    "labels = []\n",
    "with open(\"/data/retrain_manualtags1/output_labels700.txt\", 'r') as ifile:\n",
    "    for line in ifile:\n",
    "        labels.append(line.rstrip().capitalize())\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get new tags\n",
    "\n",
    "image_to_human_tag = {}\n",
    "\n",
    "for folder in glob.glob(\"/data/images_validation_10k_cleaned/*\"):  # correct ones\n",
    "    tag = os.path.basename(folder)\n",
    "    if tag not in labels: continue\n",
    "    for jpg in glob.glob(folder + \"/*.jpg\"):\n",
    "        img = os.path.basename(jpg).replace(\".jpg\", \"\")\n",
    "        image_to_human_tag[img] = tag.lower()\n",
    "\n",
    "for folder in glob.glob(\"/data/images_validation_10k_cleaned/brian_confusion/*\"):  # correct ones\n",
    "    tag = os.path.basename(folder)\n",
    "    if 'temp' in tag: tag = tag.replace(\"_temp\", \"\")\n",
    "    for jpg in glob.glob(folder + \"/*.jpg\"):\n",
    "        img = os.path.basename(jpg).replace(\".jpg\", \"\")\n",
    "        image_to_human_tag[img] = tag.lower()\n",
    "\n",
    "for folder in glob.glob(\"/data/images_validation_10k_cleaned/luda_confusion/*\"):  # correct ones\n",
    "    tag = os.path.basename(folder)\n",
    "    if 'Temp' in tag: tag = tag.replace(\"_Temp\", \"\")\n",
    "    for jpg in glob.glob(folder + \"/*.jpg\"):\n",
    "        img = os.path.basename(jpg).replace(\".jpg\", \"\")\n",
    "        image_to_human_tag[img] = tag.lower()\n",
    "            \n",
    "for folder in glob.glob(\"/data/images_validation_10k_cleaned/karen_confusion/*\"):  # correct ones\n",
    "    tag = os.path.basename(folder)\n",
    "    if 'temp' in tag: tag = tag.replace(\"_temp\", \"\")\n",
    "    for jpg in glob.glob(folder + \"/*.jpg\"):\n",
    "        img = os.path.basename(jpg).replace(\".jpg\", \"\")\n",
    "        image_to_human_tag[img] = tag.lower()\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 1 accuracy: 0.835819401341\n",
      "Top 3 accuracy: 0.930123135449\n",
      "Top 5 accuracy: 0.968265091601\n"
     ]
    }
   ],
   "source": [
    "num_intop1 = 0\n",
    "num_intop3 = 0\n",
    "num_intop5 = 0\n",
    "num_total = 0\n",
    "image_to_tags = {}\n",
    "for img in image_to_net_scores:\n",
    "    top_3_tags = np.argsort(image_to_net_scores[img])[::-1][:3] \n",
    "    top_3_tags = [labels[x].lower() for x in top_3_tags]\n",
    "    top_5_tags = np.argsort(image_to_net_scores[img])[::-1][:5] \n",
    "    top_5_tags = [labels[x].lower() for x in top_5_tags]\n",
    "    \n",
    "    if img not in image_to_human_tag: continue\n",
    "    if image_to_human_tag[img] == image_to_net_tag[img]:\n",
    "        num_intop1 += 1\n",
    "    if image_to_human_tag[img] in top_3_tags:\n",
    "        num_intop3 += 1\n",
    "    if image_to_human_tag[img] in top_5_tags:\n",
    "        num_intop5 += 1\n",
    "    num_total += 1\n",
    "\n",
    "print \"Top 1 accuracy: {}\".format(float(num_intop1)/num_total)\n",
    "print \"Top 3 accuracy: {}\".format(float(num_intop3)/num_total)\n",
    "print \"Top 5 accuracy: {}\".format(float(num_intop5)/num_total)\n",
    "#print images_to_scores.keys()[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# compare with each other --> confusion matrix and metrics\n",
    "\n",
    "ground_y = []\n",
    "pred_y = []\n",
    "x = 0\n",
    "for img in image_to_human_tag:\n",
    "    if img not in image_to_net_tag:\n",
    "        x+= 1 \n",
    "        continue     \n",
    "    else:\n",
    "        ground_y.append(image_to_human_tag[img])\n",
    "        pred_y.append(image_to_net_tag[img])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Animals</th>\n",
       "      <th>Architecture</th>\n",
       "      <th>Decorations</th>\n",
       "      <th>Diagrams</th>\n",
       "      <th>Landscape</th>\n",
       "      <th>Maps</th>\n",
       "      <th>Miniatures</th>\n",
       "      <th>Nature</th>\n",
       "      <th>Objects</th>\n",
       "      <th>People</th>\n",
       "      <th>Seals</th>\n",
       "      <th>Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Animals</th>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Architecture</th>\n",
       "      <td>1</td>\n",
       "      <td>848</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decorations</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diagrams</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>449</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Landscape</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1283</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maps</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>594</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miniatures</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nature</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>862</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Objects</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>563</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>People</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>121</td>\n",
       "      <td>11</td>\n",
       "      <td>81</td>\n",
       "      <td>45</td>\n",
       "      <td>1498</td>\n",
       "      <td>92</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seals</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>480</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Text</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Animals  Architecture  Decorations  Diagrams  Landscape  Maps  \\\n",
       "Animals           283             0            0         1          3    25   \n",
       "Architecture        1           848            0         1         40    13   \n",
       "Decorations         2             2          901         3          1    19   \n",
       "Diagrams            4             3            1       449          0    30   \n",
       "Landscape           4             2            0         3       1283    34   \n",
       "Maps                0             0            0         4          0   594   \n",
       "Miniatures          0             3           14         1          0     7   \n",
       "Nature             12             0            0         0          5    27   \n",
       "Objects             8             1            0        10          3    30   \n",
       "People             40             3            0         3          7   121   \n",
       "Seals               0             0            3        15          0    20   \n",
       "Text                0             1            0         5          1     9   \n",
       "\n",
       "              Miniatures  Nature  Objects  People  Seals  Text  \n",
       "Animals                2       9       10      11      4    28  \n",
       "Architecture           0       6       21      13      7    31  \n",
       "Decorations           41       1       18       1     26    44  \n",
       "Diagrams               1       1       12       0      1    30  \n",
       "Landscape              1      31       12      16      2    94  \n",
       "Maps                   0      13        1       2      0    22  \n",
       "Miniatures           264       0        8       0     41    10  \n",
       "Nature                 0     862        5       4      0    62  \n",
       "Objects                1       2      563       2     10    31  \n",
       "People                11      81       45    1498     92   136  \n",
       "Seals                  8       0       12       1    480     9  \n",
       "Text                   1       3        3       0      5   324  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confm = confusion_matrix(ground_y, pred_y)\n",
    "import pandas as pd\n",
    "data = pd.DataFrame(confm)\n",
    "data.columns = sorted(labels)\n",
    "data.index = sorted(labels)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7TmD9RyIRTPuOtGzdlpOankCD+om57QojFId/iS6eSd7VwAT/8evANf7j2dba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Q2XXXQ+W2655RpLFOUEqpNdtHgxo0a/z4hhQ3nntWEsXbqM14aPCJKlonlk0JNUyqtEx7+1Dx0l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\nERERkSykQp6IiIhIFvp/9h1H8u/D5G0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f272e83ab90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 9))\n",
    "ax = fig.add_subplot(111)\n",
    "sns.heatmap(data, annot=True, fmt='d')\n",
    "ax.set_xlabel(\"Prediction\")\n",
    "ax.set_ylabel(\"Ground truth\")\n",
    "plt.title(\"Confusion matrix on 10K data\")\n",
    "plt.savefig(\"confusion_matrix_10k.pdf\", format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# WE NEED TO ADD TAGS TO THE METADATA PICKLE (MANUAL + 10K) #\n",
    "(image_metadata, book_metadata, image_to_idx) = pickle.load(open(\"/data/all_metadata.pkl\", 'r'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add tag into metadata pickle for 10k\n",
    "\n",
    "for img in image_to_human_tag:\n",
    "    idx = image_to_idx[img]\n",
    "    image_metadata[idx][-1] = image_to_human_tag[img]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1346\n"
     ]
    }
   ],
   "source": [
    "# add tag into metadata pickle for manual 1500\n",
    "\n",
    "counter = 0\n",
    "with open(\"/home/ubuntu/ArtHistoryNet/analysis/20160228_image_to_manual_tags.csv\", 'r') as ifile:\n",
    "    for line in ifile:\n",
    "        row = line.split('| ')\n",
    "        if len(row) < 2: continue\n",
    "        name = row[0].split('|')[1]\n",
    "        imgname = '_'.join(name.split('_')[:4]) + '_'\n",
    "        tag = row[1].rstrip().lower()\n",
    "        \n",
    "        if imgname in image_to_idx:\n",
    "            idx = image_to_idx[imgname]\n",
    "            image_metadata[idx][-1] = tag\n",
    "            counter += 1\n",
    "\n",
    "print counter        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pickle.dump((image_metadata, book_metadata, image_to_idx), open(\"/data/all_metadata_w11ktags.pkl\", 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "001253944_0_000087_1_\n",
      "004014492_02_000113_1_\n",
      "003421394_0_000375_1_\n",
      "003542706_01_000125_1_\n",
      "002528499_0_000188_2_\n",
      "003314928_01_000007_1_\n",
      "001216372_01_000168_2_\n",
      "003257008_0_000204_1_\n",
      "002895626_0_000115_1_\n",
      "001744607_03_000013_2_\n",
      "001176069_0_000053_1_\n",
      "003392660_0_000181_1_\n",
      "002556396_0_000044_2_\n",
      "002367074_03_000142_2_\n"
     ]
    }
   ],
   "source": [
    "for img in image_to_net_tag:\n",
    "    if img not in image_to_human_tag: continue\n",
    "    if image_to_net_tag[img] == \"decorations\" and image_to_human_tag[img] == \"miniatures\":\n",
    "        print img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2727aefbd0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cTDgTF2Sr1UKhUMD29jauXbuGW7du4fDwEDLZUbIVJSMBYAzh4sWLmJ6eRjAY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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2727bcf410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(mpimg.imread(\"/data/images_validation_10k/Decorations/004014492_02_000113_1_.jpg\"), cmap = mpl.cm.Greys_r)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}