add kg nb

2 files changed, 1041 insertions, 0 deletions

diff --git a/megapixels/notebooks/datasets/imdb_wiki/imdb_wiki_kg.ipynb b/megapixels/notebooks/datasets/imdb_wiki/imdb_wiki_kg.ipynb
new file mode 100644
index 00000000..b9a77fda
--- /dev/null
+++ b/megapixels/notebooks/datasets/imdb_wiki/imdb_wiki_kg.ipynb
@@ -0,0 +1,468 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# IMDB-WIKI Knowledge Graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import os.path as osp\n",
+    "from os.path import join\n",
+    "from glob import glob\n",
+    "import random\n",
+    "import math\n",
+    "from datetime import datetime\n",
+    "import requests\n",
+    "import json\n",
+    "import urllib\n",
+    "\n",
+    "import cv2 as cv\n",
+    "import pandas as pd\n",
+    "from scipy.io import loadmat\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from tqdm import tqdm_notebook as tqdm\n",
+    "%reload_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp_meta = '/data_store_hdd/datasets/people/imdb_wiki/metadata/imdb_wiki.csv'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_meta = pd.read_csv(fp_meta).set_index('index')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>celeb_id</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>filepath</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>name</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>y1</th>\n",
+       "      <th>y2</th>\n",
+       "      <th>year_photo</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm124825600_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>1072.926000</td>\n",
+       "      <td>1214.784000</td>\n",
+       "      <td>161.838000</td>\n",
+       "      <td>303.696000</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm3343756032_1899-5-10_1970.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>477.184000</td>\n",
+       "      <td>622.592000</td>\n",
+       "      <td>100.352000</td>\n",
+       "      <td>245.760000</td>\n",
+       "      <td>1970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm577153792_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>114.969643</td>\n",
+       "      <td>451.686572</td>\n",
+       "      <td>114.969643</td>\n",
+       "      <td>451.686572</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm946909184_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>622.885506</td>\n",
+       "      <td>844.339008</td>\n",
+       "      <td>424.217504</td>\n",
+       "      <td>645.671006</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm980463616_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>1013.859002</td>\n",
+       "      <td>1201.586128</td>\n",
+       "      <td>233.882042</td>\n",
+       "      <td>421.609168</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       celeb_id        dob                                      filepath  \\\n",
+       "index                                                                      \n",
+       "0          6488  1900-5-11   01/nm0000001_rm124825600_1899-5-10_1968.jpg   \n",
+       "1          6488  1900-5-11  01/nm0000001_rm3343756032_1899-5-10_1970.jpg   \n",
+       "2          6488  1900-5-11   01/nm0000001_rm577153792_1899-5-10_1968.jpg   \n",
+       "3          6488  1900-5-11   01/nm0000001_rm946909184_1899-5-10_1968.jpg   \n",
+       "4          6488  1900-5-11   01/nm0000001_rm980463616_1899-5-10_1968.jpg   \n",
+       "\n",
+       "      gender          name           x1           x2          y1          y2  \\\n",
+       "index                                                                          \n",
+       "0          m  Fred Astaire  1072.926000  1214.784000  161.838000  303.696000   \n",
+       "1          m  Fred Astaire   477.184000   622.592000  100.352000  245.760000   \n",
+       "2          m  Fred Astaire   114.969643   451.686572  114.969643  451.686572   \n",
+       "3          m  Fred Astaire   622.885506   844.339008  424.217504  645.671006   \n",
+       "4          m  Fred Astaire  1013.859002  1201.586128  233.882042  421.609168   \n",
+       "\n",
+       "       year_photo  \n",
+       "index              \n",
+       "0            1968  \n",
+       "1            1970  \n",
+       "2            1968  \n",
+       "3            1968  \n",
+       "4            1968  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_meta.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api_key = open('/work/megapixels_dev/3rdparty/knowledge-graph-api/.api_key').read()\n",
+    "\n",
+    "def get_knowledge(q, api_key):\n",
+    "  service_url = 'https://kgsearch.googleapis.com/v1/entities:search'\n",
+    "  params = {\n",
+    "      'query': q,\n",
+    "      'limit': 5,\n",
+    "      'indent': True,\n",
+    "      'key': api_key,\n",
+    "      }\n",
+    "  url = service_url + '?' + urllib.parse.urlencode(params)  # TODO: use requests\n",
+    "  response = json.loads(urllib.request.urlopen(url).read())\n",
+    "  response = response.get('itemListElement', [])\n",
+    "  if len(response) > 0:\n",
+    "    result = response[0].get('result', [])\n",
+    "    result['score'] = response[0]['resultScore']\n",
+    "    return result\n",
+    "  else:\n",
+    "    return []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "General Secretary of the Communist Party of China\n",
+      "Xi Jinping\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'url'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m--------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                           Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-106-654588fe3a11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'description'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'url'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'score'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'url'"
+     ]
+    }
+   ],
+   "source": [
+    "# test\n",
+    "q = 'Xi Jinping'\n",
+    "r = get_knowledge(q, api_key)\n",
+    "print(r['description'])\n",
+    "print(r['name'])\n",
+    "print(r['url'])\n",
+    "print(r['score'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kg:/m/06ff60\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(r['@id'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'@id': 'kg:/g/11f4ksbzcm',\n",
+      " '@type': ['Thing', 'Event'],\n",
+      " 'detailedDescription': {'articleBody': 'On February 14, 2018, a gunman opened '\n",
+      "                                        'fire at Marjory Stoneman Douglas High '\n",
+      "                                        'School in Parkland, Florida, killing '\n",
+      "                                        'seventeen students and staff members '\n",
+      "                                        'and injuring seventeen others. ',\n",
+      "                         'license': 'https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License',\n",
+      "                         'url': 'https://en.wikipedia.org/wiki/Stoneman_Douglas_High_School_shooting'},\n",
+      " 'image': {'contentUrl': 'http://t1.gstatic.com/images?q=tbn:ANd9GcQmY7VqmGt4zEJU8Rc4EwPWroYd-L0QQ5wkZfiFO-WRqNBC-FPN',\n",
+      "           'url': 'https://en.wikipedia.org/wiki/Stoneman_Douglas_High_School_shooting'},\n",
+      " 'name': 'Stoneman Douglas High School shooting',\n",
+      " 'score': 60.411652}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir_msceleb = '/data_store_hdd/datasets/people/msceleb/media/original/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kgs_msceleb = os.listdir(dir_msceleb)\n",
+    "kgs_msceleb = ['/' + x.replace('.','/') for x in kgs_msceleb]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'/m/06ff60' in kgs_msceleb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_kg_by_id(kg_id, api_key):\n",
+    "  service_url = 'https://kgsearch.googleapis.com/v1/entities:search'\n",
+    "  params = {\n",
+    "      'ids': kg_id,\n",
+    "      'limit': 1,\n",
+    "      'indent': True,\n",
+    "      'key': api_key,\n",
+    "      }\n",
+    "  url = service_url + '?' + urllib.parse.urlencode(params)  # TODO: use requests\n",
+    "  try:\n",
+    "    response = json.loads(urllib.request.urlopen(url).read())\n",
+    "    response = response.get('itemListElement', [])\n",
+    "    result = response[0].get('result', [])\n",
+    "    result['score'] = response[0]['resultScore']\n",
+    "    return result\n",
+    "  except Exception as e:\n",
+    "    return []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = get_kg_by_id('/m/0100n5bs', api_key)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:megapixels]",
+   "language": "python",
+   "name": "conda-env-megapixels-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/megapixels/notebooks/datasets/imdb_wiki/imdb_wiki_meta_debug.ipynb b/megapixels/notebooks/datasets/imdb_wiki/imdb_wiki_meta_debug.ipynb
new file mode 100644
index 00000000..648fb9ac
--- /dev/null
+++ b/megapixels/notebooks/datasets/imdb_wiki/imdb_wiki_meta_debug.ipynb
@@ -0,0 +1,573 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 06: Face pose dlib/MTCNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import os.path as osp\n",
+    "from os.path import join\n",
+    "from glob import glob\n",
+    "import random\n",
+    "import math\n",
+    "from datetime import datetime\n",
+    "\n",
+    "import cv2 as cv\n",
+    "import pandas as pd\n",
+    "from scipy.io import loadmat\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from tqdm import tqdm_notebook as tqdm\n",
+    "%reload_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load Metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fp_mat = '/data_store_hdd/datasets/people/imdb_wiki/downloads/imdb.mat'\n",
+    "dir_out = '/data_store_hdd/datasets/people/imdb_wiki/metadata/'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat_data = loadmat(fp_mat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# row 3\n",
+    "def load_parse_imdb_mat(mat):\n",
+    "  metadata = mat['imdb'][0][0]\n",
+    "  results = []\n",
+    "  num_records = len(metadata[0][0])\n",
+    "  print(f'loaded: {num_records} records')\n",
+    "  for i in tqdm(range(num_records), total=num_records):\n",
+    "    dob_matlab = metadata[0][0][i]\n",
+    "    dob = datetime.fromordinal(dob_matlab)\n",
+    "    dob_str = f'{dob.year}-{dob.month}-{dob.day}'\n",
+    "    year_photo = metadata[1][0][i]\n",
+    "    fp = metadata[2][0][i][0]\n",
+    "    gender_val = metadata[3][0][i]\n",
+    "    if gender_val == 0:\n",
+    "      gender = 'f'\n",
+    "    elif gender_val == 1:\n",
+    "      gender = 'm'\n",
+    "    else:\n",
+    "      gender = None\n",
+    "    name = metadata[4][0][i][0]\n",
+    "    roi = metadata[5][0][i][0]\n",
+    "    face_conf = metadata[6][0][i]\n",
+    "    face_conf_second = metadata[7][0][i]\n",
+    "    celeb_id = metadata[9][0][i]\n",
+    "    result = {\n",
+    "      'dob': dob_str,\n",
+    "      'year_photo': year_photo,\n",
+    "      'filepath': fp,\n",
+    "      'gender': gender,\n",
+    "      'name': name,\n",
+    "      'x1': roi[0],\n",
+    "      'y1': roi[1],\n",
+    "      'x2': roi[2],\n",
+    "      'y2': roi[3],\n",
+    "      'celeb_id': celeb_id\n",
+    "    }\n",
+    "    results.append(result)\n",
+    "  return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loaded: 460723 records\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d50c6e22d1694b54815a86d85cda6241",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=460723), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "results_meta = load_parse_imdb_mat(mat_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_meta = pd.DataFrame.from_dict(results_meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>celeb_id</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>filepath</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>name</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>y1</th>\n",
+       "      <th>y2</th>\n",
+       "      <th>year_photo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm124825600_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>1072.926000</td>\n",
+       "      <td>1214.784000</td>\n",
+       "      <td>161.838000</td>\n",
+       "      <td>303.696000</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm3343756032_1899-5-10_1970.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>477.184000</td>\n",
+       "      <td>622.592000</td>\n",
+       "      <td>100.352000</td>\n",
+       "      <td>245.760000</td>\n",
+       "      <td>1970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm577153792_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>114.969643</td>\n",
+       "      <td>451.686572</td>\n",
+       "      <td>114.969643</td>\n",
+       "      <td>451.686572</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm946909184_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>622.885506</td>\n",
+       "      <td>844.339008</td>\n",
+       "      <td>424.217504</td>\n",
+       "      <td>645.671006</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm980463616_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>1013.859002</td>\n",
+       "      <td>1201.586128</td>\n",
+       "      <td>233.882042</td>\n",
+       "      <td>421.609168</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   celeb_id        dob                                      filepath gender  \\\n",
+       "0      6488  1900-5-11   01/nm0000001_rm124825600_1899-5-10_1968.jpg      m   \n",
+       "1      6488  1900-5-11  01/nm0000001_rm3343756032_1899-5-10_1970.jpg      m   \n",
+       "2      6488  1900-5-11   01/nm0000001_rm577153792_1899-5-10_1968.jpg      m   \n",
+       "3      6488  1900-5-11   01/nm0000001_rm946909184_1899-5-10_1968.jpg      m   \n",
+       "4      6488  1900-5-11   01/nm0000001_rm980463616_1899-5-10_1968.jpg      m   \n",
+       "\n",
+       "           name           x1           x2          y1          y2  year_photo  \n",
+       "0  Fred Astaire  1072.926000  1214.784000  161.838000  303.696000        1968  \n",
+       "1  Fred Astaire   477.184000   622.592000  100.352000  245.760000        1970  \n",
+       "2  Fred Astaire   114.969643   451.686572  114.969643  451.686572        1968  \n",
+       "3  Fred Astaire   622.885506   844.339008  424.217504  645.671006        1968  \n",
+       "4  Fred Astaire  1013.859002  1201.586128  233.882042  421.609168        1968  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_meta.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create DataFrame for metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_results = pd.DataFrame.from_dict(results_meta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>celeb_id</th>\n",
+       "      <th>dob</th>\n",
+       "      <th>filepath</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>name</th>\n",
+       "      <th>x1</th>\n",
+       "      <th>x2</th>\n",
+       "      <th>y1</th>\n",
+       "      <th>y2</th>\n",
+       "      <th>year_photo</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm124825600_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>1072.926000</td>\n",
+       "      <td>1214.784000</td>\n",
+       "      <td>161.838000</td>\n",
+       "      <td>303.696000</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm3343756032_1899-5-10_1970.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>477.184000</td>\n",
+       "      <td>622.592000</td>\n",
+       "      <td>100.352000</td>\n",
+       "      <td>245.760000</td>\n",
+       "      <td>1970</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm577153792_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>114.969643</td>\n",
+       "      <td>451.686572</td>\n",
+       "      <td>114.969643</td>\n",
+       "      <td>451.686572</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm946909184_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>622.885506</td>\n",
+       "      <td>844.339008</td>\n",
+       "      <td>424.217504</td>\n",
+       "      <td>645.671006</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6488</td>\n",
+       "      <td>1900-5-11</td>\n",
+       "      <td>01/nm0000001_rm980463616_1899-5-10_1968.jpg</td>\n",
+       "      <td>m</td>\n",
+       "      <td>Fred Astaire</td>\n",
+       "      <td>1013.859002</td>\n",
+       "      <td>1201.586128</td>\n",
+       "      <td>233.882042</td>\n",
+       "      <td>421.609168</td>\n",
+       "      <td>1968</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   celeb_id        dob                                      filepath gender  \\\n",
+       "0      6488  1900-5-11   01/nm0000001_rm124825600_1899-5-10_1968.jpg      m   \n",
+       "1      6488  1900-5-11  01/nm0000001_rm3343756032_1899-5-10_1970.jpg      m   \n",
+       "2      6488  1900-5-11   01/nm0000001_rm577153792_1899-5-10_1968.jpg      m   \n",
+       "3      6488  1900-5-11   01/nm0000001_rm946909184_1899-5-10_1968.jpg      m   \n",
+       "4      6488  1900-5-11   01/nm0000001_rm980463616_1899-5-10_1968.jpg      m   \n",
+       "\n",
+       "           name           x1           x2          y1          y2  year_photo  \n",
+       "0  Fred Astaire  1072.926000  1214.784000  161.838000  303.696000        1968  \n",
+       "1  Fred Astaire   477.184000   622.592000  100.352000  245.760000        1970  \n",
+       "2  Fred Astaire   114.969643   451.686572  114.969643  451.686572        1968  \n",
+       "3  Fred Astaire   622.885506   844.339008  424.217504  645.671006        1968  \n",
+       "4  Fred Astaire  1013.859002  1201.586128  233.882042  421.609168        1968  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_results.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_results.to_csv(join(dir_out,'imdb_wiki.csv'), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Count Images per Person"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_name_groups = df_results.groupby('name')\n",
+    "images_per_person = []\n",
+    "for name, df_name in df_name_groups:\n",
+    "  images_per_person.append({'name': name, 'num_images': len(df_name)})\n",
+    "df_images_per_person = pd.DataFrame.from_dict(images_per_person)\n",
+    "df_images_per_person.to_csv(join(dir_out, 'imdb_images_per_person.csv'), index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Find Face Size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sizes = [(x['x2'] - x['x1']) for x in results_meta]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "buckets = list(range(0,500,50))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JfjCt75vk+u6+aVq/KskB0/IBSa5Mkmn7DVP9H5Yv2Wd75QAAsGYsLJBX1a8kuaa7L1pUG7egLydU1YVVdeG11147ujsAAPBDixwhf1iSX62qLZlNJ3lUktck2auqNkx1Dkxy9bR8dZKDkmTafrckX58vX7LP9sp/Qnef0t2Hdfdh++23386fGQAA7CILC+Td/dLuPrC7N2b2pcz3d/czknwgyZOmasclefe0fOa0nmn7+7u7p/KnTndhuXeSQ5J8PMkFSQ6Z7tpy+6mNMxd1PgAAsAgbbr7KLvefkpxRVa9I8skkb5jK35DkTVW1OcnWzAJ2uvuyqnpbkk8nuSnJ87v7+0lSVS9Ick6SPZKc2t2XreqZAADATlqVQN7dH0zywWn585ndIWVpnW8n+fXt7P/KJK9cpvzsJGfvwq4CAMCq8qROAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYaEWBvKpeWFV3rZk3VNUnqurIRXcOAAB2dysdIX92d38jyZFJ9k7yzCQnLaxXAACwTqw0kNf0/vgkb+ruy+bKAACAW2mlgfyiqnpfZoH8nKq6S5IfLK5bAACwPmxYYb3jk9w/yee7+8aq2jfJsxbXLQAAWB9WOkLeSQ5N8lvT+p2S7LmQHgEAwDqy0kD+Z0kekuRp0/o3k7x2IT0CAIB1ZKVTVh7c3Q+sqk8mSXdfV1W3X2C/AABgXVjpCPn3qmqPzKaupKr2iy91AgDATlvpCPnJSd6Z5B5V9cokT0rynxfWK1gFGzedtSrtbDnp6FVpBwBYm1YUyLv7zVV1UZJHZ3b/8Sd09+UL7RkAAKwDKwrkVbVPkmuSvHWu7Hbd/b1FdQwAANaDlc4h/0SSa5P8U5IrpuUtVfWJqnrQojoHAAC7u5UG8nOTPL67797d+yZ5XJL3JHleZrdEBAAAboWVBvIjuvucbSvd/b4kD+nu85PcYSE9AwCAdWCld1n5clX9pyRnTOtPSfLV6VaIbn8IAAC30kpHyJ+e5MAk75peB09leyR58mK6BgAAu7+V3vbwa0l+czubN++67gAAwPqy0tse7pfkJUnum2TPbeXd/agF9QsAANaFlU5ZeXOSzyS5d5I/TLIlyQUL6hMAAKwbKw3k+3b3G5J8r7v/rrufncToOAAA7KSV3mVl2xM5v1xVRyf5UpJ9FtMlAABYP1YayF9RVXdL8uIkf5Lkrkn+t4X1CgAA1omV3mXlPdPiDUkeubjuAADA+rLSu6zcO7PbHm6c36e7f3Ux3QIAgPVhpVNW3pXkDUn+Jp7MCQAAu8xKA/m3u/vkhfYEAADWoZXe9vA1VXViVT2kqh647bWjHapqz6r6eFX9Y1VdVlV/OJXfu6o+VlWbq+ovq+r2U/kdpvXN0/aNc8d66VT+2ap67Fz5UVPZ5qradIvPHgAABlvpCPnPJ3lmZvce3zZlpbPje5F/J8mjuvtbVXW7JB+uqvcm+e0kr+7uM6rqz5Mcn+R10/t13X2fqnpqklcleUpVHZrkqZk9JfReSf62qv7t1MZrkzwmyVVJLqiqM7v70ys8JwAAGG6lgfzXk/xMd393pQfu7k7yrWn1dtNrW4h/+lR+WpI/yCyQHzMtJ8nbk/xpVdVUfkZ3fyfJP1fV5iSHT/U2d/fnk6SqzpjqCuQAAKwZK52y8qkke93Sg1fVHlV1cZJrkpyb5HNJru/um6YqVyU5YFo+IMmVSTJtvyHJvvPlS/bZXjkAAKwZKx0h3yvJZ6rqgsymoiS5+dsedvf3k9y/qvZK8s4kP3trO7ozquqEJCckycEHHzyiCwAAsKyVBvITd6aR7r6+qj6Q5CFJ9qqqDdMo+IFJrp6qXZ3koCRXVdWGJHdL8vW58m3m99le+dL2T0lySpIcdthhvTPnAgAAu9JKn9T5d7f0wFW1X5LvTWH8jpl9+fJVST6Q5ElJzkhyXJJ3T7ucOa1/dNr+/u7uqjozyVuq6o8z+1LnIUk+nqSSHDI9tOjqzL74uW1uOgAArAk7DORV9c3Mvoj5E5sy+97mXXew+z2TnFZVe2Q2V/1t3f2eqvp0kjOq6hVJPpnZA4cyvb9p+tLm1swCdrr7sqp6W2Zf1rwpyfOnqTCpqhckOSfJHklO7e7LVnLSAABwW7HDQN7dd7m1B+7uS5I8YJnyz+dHd0mZL/92ZndzWe5Yr0zyymXKz05y9q3tIwAAjLbSu6wAAAALIJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMtGF0BwDmbdx01sLb2HLS0QtvAwBWygg5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAG0Z3AHZ3GzedtfA2tpx09MLbAAAWY2Ej5FV1UFV9oKo+XVWXVdULp/J9qurcqrpiet97Kq+qOrmqNlfVJVX1wLljHTfVv6Kqjpsrf1BVXTrtc3JV1aLOBwAAFmGRU1ZuSvLi7j40yRFJnl9VhybZlOS87j4kyXnTepI8Lskh0+uEJK9LZgE+yYlJHpzk8CQnbgvxU53nzu131ALPBwAAdrmFBfLu/nJ3f2Ja/maSy5MckOSYJKdN1U5L8oRp+Zgkp/fM+Un2qqp7JnlsknO7e2t3X5fk3CRHTdvu2t3nd3cnOX3uWAAAsCasypc6q2pjkgck+ViS/bv7y9OmryTZf1o+IMmVc7tdNZXtqPyqZcqXa/+Eqrqwqi689tprd+pcAABgV1p4IK+qOyf56yQv6u5vzG+bRrZ70X3o7lO6+7DuPmy//fZbdHMAALBiCw3kVXW7zML4m7v7HVPxV6fpJpner5nKr05y0NzuB05lOyo/cJlyAABYMxZ5l5VK8oYkl3f3H89tOjPJtjulHJfk3XPlx053WzkiyQ3T1JZzkhxZVXtPX+Y8Msk507ZvVNURU1vHzh0LAADWhEXeh/xhSZ6Z5NKqungqe1mSk5K8raqOT/KFJE+etp2d5PFJNie5McmzkqS7t1bVHyW5YKr38u7eOi0/L8kbk9wxyXunFwAArBkLC+Td/eEk27sv+KOXqd9Jnr+dY52a5NRlyi9Mcr+d6CYAAAy1KndZAQAAlieQAwDAQAI5AAAMtMgvdQKrZOOms0Z3AQC4lYyQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADAMBAAjkAAAy0YXQHAGC1bNx01sLb2HLS0QtvA9i9GCEHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgTaM7gAAcMtt3HTWwtvYctLRC28DMEIOAABDCeQAADCQQA4AAAMJ5AAAMJBADgAAAwnkAAAwkEAOAAADCeQAADCQBwMB685qPFAlWZ2Hqng4DMDaZ4QcAAAGEsgBAGAgU1YAYBdarSlRwO5DIAdYEMEMgJUwZQUAAAYSyAEAYCCBHAAABhLIAQBgIIEcAAAGEsgBAGAgtz3cjbjFGgDA2mOEHAAABhLIAQBgIIEcAAAGEsgBAGAggRwAAAZaWCCvqlOr6pqq+tRc2T5VdW5VXTG97z2VV1WdXFWbq+qSqnrg3D7HTfWvqKrj5sofVFWXTvucXFW1qHMBAIBFWeQI+RuTHLWkbFOS87r7kCTnTetJ8rgkh0yvE5K8LpkF+CQnJnlwksOTnLgtxE91nju339K2AADgNm9hgby7P5Rk65LiY5KcNi2fluQJc+Wn98z5SfaqqnsmeWySc7t7a3dfl+TcJEdN2+7a3ed3dyc5fe5YAACwZqz2HPL9u/vL0/JXkuw/LR+Q5Mq5eldNZTsqv2qZcgAAWFOGPamzu7uqejXaqqoTMpsKk4MPPng1mgQAbkNW42nWW046euFtsHta7RHyr07TTTK9XzOVX53koLl6B05lOyo/cJnyZXX3Kd19WHcftt9+++30SQAAwK6y2oH8zCTb7pRyXJJ3z5UfO91t5YgkN0xTW85JcmRV7T19mfPIJOdM275RVUdMd1c5du5YAACwZixsykpVvTXJI5LcvaquyuxuKScleVtVHZ/kC0mePFU/O8njk2xOcmOSZyVJd2+tqj9KcsFU7+Xdve2Los/L7E4ud0zy3ukFAABrysICeXc/bTubHr1M3U7y/O0c59Qkpy5TfmGS++1MHwEAYDRP6gQAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEWdh9yAAB2vY2bzlp4G1tOOnrhbfAjRsgBAGAggRwAAAYSyAEAYCCBHAAABhLIAQBgIIEcAAAGEsgBAGAggRwAAAYSyAEAYCBP6gQAhlqNJ0/CbZkRcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABjIXVYA2KHVugPGlpOOXpV2AG5rBHIAAH7Mavwh7o/wHzFlBQAABhLIAQBgIFNWAAB2AU8c5dYSyAEAWHW+MP4jpqwAAMBAAjkAAAxkygoAtwnm3wLrlRFyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYaMPoDgAAt00bN501uguwLhghBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICBBHIAABhIIAcAgIEEcgAAGGjNB/KqOqqqPltVm6tq0+j+AADALbGmA3lV7ZHktUkel+TQJE+rqkPH9goAAFZuTQfyJIcn2dzdn+/u7yY5I8kxg/sEAAArttYD+QFJrpxbv2oqAwCANWHD6A6shqo6IckJ0+q3quqzA7px9yRfG9Au47n265drvz657uuXa38bVK9alWaWu/Y/vdKd13ogvzrJQXPrB05lP6a7T0lyymp1ajlVdWF3HzayD4zh2q9frv365LqvX679+rWz136tT1m5IMkhVXXvqrp9kqcmOXNwnwAAYMXW9Ah5d99UVS9Ick6SPZKc2t2XDe4WAACs2JoO5EnS3WcnOXt0P1Zg6JQZhnLt1y/Xfn1y3dcv13792qlrX929qzoCAADcQmt9DjkAAKxpAvkqqKqjquqzVbW5qjaN7g+7VlWdWlXXVNWn5sr2qapzq+qK6X3vqbyq6uTp38IlVfXAcT1nZ1TVQVX1gar6dFVdVlUvnMpd+91cVe1ZVR+vqn+crv0fTuX3rqqPTdf4L6ebDaSq7jCtb562bxzZf3ZOVe1RVZ+sqvdM6677OlBVW6rq0qq6uKounMp22e97gXzBqmqPJK9N8rgkhyZ5WlUdOrZX7GJvTHLUkrJNSc7r7kOSnDetJ7N/B4dMrxOSvG6V+siud1OSF3f3oUmOSPL86b9t1373950kj+ruX0xy/yRHVdURSV6V5NXdfZ8k1yU5fqp/fJLrpvJXT/VYu16Y5PK5ddd9/Xhkd99/7vaGu+z3vUC+eIcn2dzdn+/u7yY5I8kxg/vELtTdH0qydUnxMUlOm5ZPS/KEufLTe+b8JHtV1T1Xp6fsSt395e7+xLT8zcz+B31AXPvd3nQNvzWt3m56dZJHJXnWA83gAAAFkklEQVT7VL702m/7N/H2JI+uqlql7rILVdWBSY5O8vppveK6r2e77Pe9QL54ByS5cm79qqmM3dv+3f3lafkrSfaflv172A1NH0U/IMnH4tqvC9O0hYuTXJPk3CSfS3J9d980VZm/vj+89tP2G5Lsu7o9Zhf570lekuQH0/q+cd3Xi07yvqq6aHoCfLILf9+v+dsewm1dd3dVuZ3Rbqqq7pzkr5O8qLu/MT8A5trvvrr7+0nuX1V7JXlnkp8d3CUWrKp+Jck13X1RVT1idH9YdQ/v7qur6h5Jzq2qz8xv3Nnf90bIF+/qJAfNrR84lbF7++q2j6em92umcv8ediNVdbvMwvibu/sdU7Frv4509/VJPpDkIZl9LL1toGv++v7w2k/b75bk66vcVXbew5L8alVtyWz66aOSvCau+7rQ3VdP79dk9kf44dmFv+8F8sW7IMkh07ewb5/kqUnOHNwnFu/MJMdNy8clefdc+bHTN7CPSHLD3MddrCHTXNA3JLm8u/94bpNrv5urqv2mkfFU1R2TPCaz7xB8IMmTpmpLr/22fxNPSvL+9hCQNae7X9rdB3b3xsz+X/7+7n5GXPfdXlXdqarusm05yZFJPpVd+Pveg4FWQVU9PrN5Z3skObW7Xzm4S+xCVfXWJI9IcvckX01yYpJ3JXlbkoOTfCHJk7t76xTi/jSzu7LcmORZ3X3hiH6zc6rq4Un+Psml+dF80pdlNo/ctd+NVdUvZPYFrj0yG9h6W3e/vKp+JrOR032SfDLJv+/u71TVnknelNn3DLYmeWp3f35M79kVpikrv9Pdv+K67/6ma/zOaXVDkrd09yurat/sot/3AjkAAAxkygoAAAwkkAMAwEACOQAADCSQAwDAQAI5AAAMJJADrFFV9eqqetHc+jlV9fq59f+rqn67qu5VVW/fzjE+WFWHTcsvmyvfWFWfWmE/XlRVx97Kc3hBVT371uwLsLsQyAHWrn9I8tAkqaqfyuxe+Ped2/7QJB/p7i9195OW2X+pl918lR83PYHw2Uneckv3nZya5Ddv5b4AuwWBHGDt+khmj2xPZkH8U0m+WVV7V9Udkvxckk/Mj3ZX1R2r6oyquryq3pnkjlP5SUnuWFUXV9Wbp2PuUVX/T1VdVlXvm55KudSjknyiu2+ajvPBqnrNdJxPVdXhU/lrqup/n5YfW1Ufqqqf6u4bk2zZVg9gPRLIAdao7v5Skpuq6uDMRsM/mtmTQh+S5LAkl3b3d5fs9h+T3NjdP5fZU2UfNB1rU5J/6e77T48DT5JDkry2u++b5PokT1ymGw9LctGSsn/V3fdP8rzMRsCT5KVJnlJVj0xycmZPrtv2hNMLk/zSLf4BAOwmBHKAte0jmYXxbYH8o3Pr/7BM/V9O8hdJ0t2XJLlkB8f+5+6+eFq+KMnGZercM8m1S8reOh3/Q0nuWlV7TSPhz01ybpI/7e7PzdW/Jsm9dtAPgN2aQA6wtm2bR/7zmU1ZOT+zEfKHZhbWd8Z35pa/n2TDMnX+JcmeS8p6O+s/n+Tr+cnwved0HIB1SSAHWNs+kuRXkmzt7u9399Yke2UWypcL5B9K8vQkqar7JfmFuW3fq6rb3cL2L09ynyVlT5mO//AkN3T3DVX100lenOQBSR5XVQ+eq/9vM/tjAmBdEsgB1rZLM7u7yvlLym7o7q8tU/91Se5cVZcneXl+fP73KUkumftS50q8N7NpMPO+XVWfTPLnSY6vqkryhiS/M817Pz7J66tq28j6wzKbygKwLlX30k8WAWDlpru1vKS7r6iqD2YWvC9c4b4PSPLb3f3MRfYR4LbMCDkAO2tTZl/uvDXunuT3d2FfANYcI+QAADCQEXIAABhIIAcAgIEEcgAAGEggBwCAgQRyAAAYSCAHAICB/n8av9LMcfvPhwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import pyplot as plt \n",
+    "import numpy as np  \n",
+    "bins = list(range(0,500,20))\n",
+    "plt.figure(figsize=(12,8))\n",
+    "plt.hist(sizes, bins=bins)\n",
+    "plt.title(\"Face Image Sizes\") \n",
+    "plt.ylabel(\"Images\")\n",
+    "plt.xlabel(\"Width (px)\")\n",
+    "plt.yticks(range(0, 60000, 10000))\n",
+    "plt.title('IMDB-Wiki: Face Pixel Size')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "dob: date of birth (Matlab serial date number)\n",
+    "photo_taken: year when the photo was taken\n",
+    "full_path: path to file\n",
+    "gender: 0 for female and 1 for male, NaN if unknown\n",
+    "name: name of the celebrity\n",
+    "face_location: location of the face. To crop the face in Matlab run\n",
+    "\n",
+    "img(face_location(2):face_location(4),face_location(1):face_location(3),:))\n",
+    "\n",
+    "face_score: detector score (the higher the better). Inf implies that no face was found in the image and the face_location then just returns the entire image\n",
+    "second_face_score: detector score of the face with the second highest score. This is useful to ignore images with more than one face. second_face_score is NaN if no second face was detected.\n",
+    "celeb_names (IMDB only): list of all celebrity names\n",
+    "celeb_id (IMDB only): index of celebrity name\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:megapixels]",
+   "language": "python",
+   "name": "conda-env-megapixels-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}