{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Append UUID to SHA256 CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import join\n",
    "from pathlib import Path\n",
    "import difflib\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import pandas as pd\n",
    "import dask.dataframe as dd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# names\n",
    "DATA_STORE_NAS = '/data_store_nas/'\n",
    "dir_dataset = 'datasets/people/vgg_face2/metadata'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>sha256</th>\n",
       "      <th>identity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a39a1df855cb0c70dc553c5e9afa35b4f7c00f4011ca10...</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e360f93613baa68cede6731d2603873cdabd3993841cfd...</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  sha256  identity\n",
       "index                                                             \n",
       "0      a39a1df855cb0c70dc553c5e9afa35b4f7c00f4011ca10...        -1\n",
       "1      e360f93613baa68cede6731d2603873cdabd3993841cfd...        -1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# associate each file with an identity\n",
    "fp_index = join(DATA_STORE_NAS, dir_dataset, 'index.csv')\n",
    "df_index = pd.read_csv(fp_index).set_index('index')\n",
    "df_index['identity'] = [-1] * len(df_index)\n",
    "df_index.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3311286\n"
     ]
    },
    {
     "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>ext</th>\n",
       "      <th>fn</th>\n",
       "      <th>subdir</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jpg</td>\n",
       "      <td>0089_01</td>\n",
       "      <td>test/n006211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jpg</td>\n",
       "      <td>0168_01</td>\n",
       "      <td>test/n006211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>jpg</td>\n",
       "      <td>0213_01</td>\n",
       "      <td>test/n006211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jpg</td>\n",
       "      <td>0010_01</td>\n",
       "      <td>test/n006211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>jpg</td>\n",
       "      <td>0115_01</td>\n",
       "      <td>test/n006211</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ext       fn        subdir\n",
       "index                            \n",
       "0      jpg  0089_01  test/n006211\n",
       "1      jpg  0168_01  test/n006211\n",
       "2      jpg  0213_01  test/n006211\n",
       "3      jpg  0010_01  test/n006211\n",
       "4      jpg  0115_01  test/n006211"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get file info\n",
    "fp_files = join(DATA_STORE_NAS, dir_dataset, 'files.csv')\n",
    "df_files = pd.read_csv(fp_files).set_index('index')\n",
    "print(len(df_files))\n",
    "df_files.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9131\n"
     ]
    },
    {
     "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>class_id</th>\n",
       "      <th>description</th>\n",
       "      <th>gender</th>\n",
       "      <th>images</th>\n",
       "      <th>name</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>n000001</td>\n",
       "      <td>Dalai Lama</td>\n",
       "      <td>m</td>\n",
       "      <td>424</td>\n",
       "      <td>14th Dalai Lama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n000002</td>\n",
       "      <td>American singer-songwriter</td>\n",
       "      <td>f</td>\n",
       "      <td>315</td>\n",
       "      <td>A Fine Frenzy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      class_id                 description gender  images             name\n",
       "index                                                                     \n",
       "0      n000001                  Dalai Lama      m     424  14th Dalai Lama\n",
       "1      n000002  American singer-songwriter      f     315    A Fine Frenzy"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fp_identities = join(DATA_STORE_NAS, dir_dataset, 'identities.csv')\n",
    "df_identities = dd.read_csv(fp_identities).set_index('index')\n",
    "print(len(df_identities))\n",
    "df_identities.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_ids = {}\n",
    "for row in df_identities.itertuples():\n",
    "  class_id = row.class_id\n",
    "  index = row.Index\n",
    "  if class_id not in class_ids.keys():\n",
    "    class_ids[class_id] = index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac332e87bee54d53a0e29efbdfa86d65",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3311286), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for row in tqdm(df_index.itertuples(), total=len(df_index)):\n",
    "  file_index = row.Index\n",
    "  file_row = df_files.iloc[file_index]\n",
    "  subdir = file_row['subdir']\n",
    "  class_id = subdir.split('/')[1]\n",
    "  identity_index = class_ids[class_id]\n",
    "  df_index.at[row.Index, 'identity'] = identity_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_index_new = join(DATA_STORE_NAS, dir_dataset, 'index_new.csv')\n",
    "df_index.to_csv(fp_index_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>sha256</th>\n",
       "      <th>identity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>a39a1df855cb0c70dc553c5e9afa35b4f7c00f4011ca10...</td>\n",
       "      <td>6123</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e360f93613baa68cede6731d2603873cdabd3993841cfd...</td>\n",
       "      <td>6123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3920a8bdf523a5bf7da9258ec414a741462d0cfbec8d2c...</td>\n",
       "      <td>6123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>577ce218e4a61e612942c55fd172cac4b48becacbfc708...</td>\n",
       "      <td>6123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b27d37425a4e59dc4d37c3df331d0b69e4919338a3d46f...</td>\n",
       "      <td>6123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  sha256  identity\n",
       "index                                                             \n",
       "0      a39a1df855cb0c70dc553c5e9afa35b4f7c00f4011ca10...      6123\n",
       "1      e360f93613baa68cede6731d2603873cdabd3993841cfd...      6123\n",
       "2      3920a8bdf523a5bf7da9258ec414a741462d0cfbec8d2c...      6123\n",
       "3      577ce218e4a61e612942c55fd172cac4b48becacbfc708...      6123\n",
       "4      b27d37425a4e59dc4d37c3df331d0b69e4919338a3d46f...      6123"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python [conda env:megapixels]",
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