{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Count Images for LFW\n",
    "\n",
    "- use sub-directory as `identity_key`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "from os.path import join\n",
    "from glob import glob\n",
    "from pathlib import Path\n",
    "from pprint import pprint\n",
    "\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from slugify import slugify\n",
    "\n",
    "import sys\n",
    "sys.path.append('/work/megapixels_dev/megapixels')\n",
    "from app.utils import file_utils\n",
    "from app.settings import types, app_cfg\n",
    "from app.models.data_store import DataStore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get image counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_dataset = types.Dataset.LFW\n",
    "opt_data_store = types.DataStore.HDD\n",
    "data_store = DataStore(opt_data_store, opt_dataset)\n",
    "# get filepath out\n",
    "fp_records = data_store.metadata(types.Metadata.FILE_RECORD)\n",
    "fp_img_counts = data_store.metadata(types.Metadata.IMAGE_COUNT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_records = pd.read_csv(fp_records).set_index('index')\n",
    "records = df_records.to_dict('records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paths\n",
    "fp_dirs = '/data_store_hdd/datasets/people/lfw/media/original/'\n",
    "\n",
    "fp_out = '/data_store_hdd/datasets/people/lfw/metadata/image_counts.csv'\n",
    "\n",
    "# glob\n",
    "dirs = glob(join(fp_dirs,'*'))\n",
    "\n",
    "# count images\n",
    "image_counts = []\n",
    "\n",
    "for d in tqdm(dirs):\n",
    "  # get number of images\n",
    "  files = file_utils.glob_multi(d, ['jpg', 'png'], recursive=False)\n",
    "  count = len(files)\n",
    "  name = Path(d).stem\n",
    "  image_counts.append({'identity_key': name, 'count': count})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_counts = pd.DataFrame.from_dict(image_counts)\n",
    "df_counts.index.name = 'index'\n",
    "df_counts.to_csv(fp_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>count</th>\n",
       "      <th>identity_key</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>14</td>\n",
       "      <td>Kim_Clijsters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>William_Rosenberg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>John_Brady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Juan_Ignacio_Chela</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Floyd_Keith</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count        identity_key\n",
       "index                           \n",
       "0         14       Kim_Clijsters\n",
       "1          1   William_Rosenberg\n",
       "2          2          John_Brady\n",
       "3          3  Juan_Ignacio_Chela\n",
       "4          1         Floyd_Keith"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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