{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Append UUID to SHA256 CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys\n",
    "sys.path.append('/work/megapixels_dev/megapixels/')\n",
    "from app.utils import file_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_STORE = '/data_store_nas/'\n",
    "dir_dataset = join(DATA_STORE, 'datasets/people/lfw/')\n",
    "dir_metadata = join(dir_dataset, 'metadata')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp_files = join(dir_metadata, 'image_files.csv')\n",
    "fp_rois = join(dir_metadata, 'image_rois.csv')\n",
    "fp_identities = join(dir_metadata, 'identities.csv')\n",
    "fp_pose = join(dir_metadata, 'image_pose.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rois = pd.read_csv(fp_rois).set_index('index')\n",
    "df_files = pd.read_csv(fp_files).set_index('index')\n",
    "df_identities = pd.read_csv(fp_identities).set_index('index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>h</th>\n",
       "      <th>image_height</th>\n",
       "      <th>image_index</th>\n",
       "      <th>image_width</th>\n",
       "      <th>w</th>\n",
       "      <th>x</th>\n",
       "      <th>y</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.330000</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>250</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>0.336667</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.393333</td>\n",
       "      <td>250</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>0.393333</td>\n",
       "      <td>0.286667</td>\n",
       "      <td>0.313333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              h  image_height  image_index  image_width         w         x  \\\n",
       "index                                                                         \n",
       "0      0.330000           250            0          250  0.330000  0.336667   \n",
       "1      0.393333           250            1          250  0.393333  0.286667   \n",
       "\n",
       "              y  \n",
       "index            \n",
       "0      0.350000  \n",
       "1      0.313333  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rois.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>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>AJ_Cook_0001</td>\n",
       "      <td>AJ_Cook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>jpg</td>\n",
       "      <td>AJ_Lamas_0001</td>\n",
       "      <td>AJ_Lamas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ext             fn    subdir\n",
       "index                              \n",
       "0      jpg   AJ_Cook_0001   AJ_Cook\n",
       "1      jpg  AJ_Lamas_0001  AJ_Lamas"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_files.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>name</th>\n",
       "      <th>description</th>\n",
       "      <th>gender</th>\n",
       "      <th>images</th>\n",
       "      <th>image_index</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>A. J. Cook</td>\n",
       "      <td>Canadian actress</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AJ Lamas</td>\n",
       "      <td>American actor</td>\n",
       "      <td>m</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             name       description gender  images  image_index\n",
       "index                                                          \n",
       "0      A. J. Cook  Canadian actress      f       1            0\n",
       "1        AJ Lamas    American actor      m       1            1"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_identities.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5749"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_identities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13233"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices_included = list(df_rois['image_index'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices_all = list(range(13233))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{6072, 1556, 11207}\n"
     ]
    }
   ],
   "source": [
    "print(set(indices_all) - set(indices_included))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm_notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_roi_groups = df_rois.groupby('image_index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df3b603ad0464cb6983862fbf6716d02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=13230), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AJ_Cook_0001\n",
      "0.33\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for df_roi_group_idx, df_roi_group in tqdm(df_roi_groups):\n",
    "  image_index = df_roi_group.image_index.values[0]\n",
    "  pds_file = df_files.iloc[image_index]\n",
    "  print(pds_file.fn)\n",
    "  w = df_roi_group.w.values[0]\n",
    "  print(w)\n",
    "  break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from app.models.bbox import BBox"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import operator\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "bboxes = []\n",
    "dim = (250, 250)\n",
    "bboxes.append(BBox.from_xywh_dim(10, 10, 40, 40, dim))\n",
    "bboxes.append(BBox.from_xywh_dim(10, 10, 20, 20, dim))\n",
    "bboxes.append(BBox.from_xywh_dim(10, 10, 200, 200, dim))\n",
    "bboxes.append(BBox.from_xywh_dim(10, 10, 30, 30, dim))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6399999999999999\n",
      "0.0256\n",
      "0.0144\n",
      "0.006399999999999998\n",
      "\n",
      "0.6399999999999999\n",
      "0.0256\n",
      "0.0144\n",
      "0.006399999999999998\n",
      "max area is: 0.6399999999999999\n"
     ]
    }
   ],
   "source": [
    "for b in bboxes:\n",
    "  print(b.area)\n",
    "bboxes.sort(key = operator.attrgetter('area'), reverse=True)[0]\n",
    "print('')\n",
    "for b in bboxes:\n",
    "  print(b.area)\n",
    "\n",
    "print('max area is: {}'.format(bboxes[0].area))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "bboxes.sort(key = operator.attrgetter('area'), reverse=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<app.models.bbox.BBox at 0x7ff360047978>,\n",
       " <app.models.bbox.BBox at 0x7ff3600479b0>,\n",
       " <app.models.bbox.BBox at 0x7ff3600479e8>,\n",
       " <app.models.bbox.BBox at 0x7ff360047a20>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bboxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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
}