{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Acessing CSV data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 random import randint\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import cv2 as cv\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_STORE = '/data_store/'\n",
    "dir_datasets = join(DATA_STORE, 'datasets/people')\n",
    "dir_lfw = join(dir_datasets, 'lfw')\n",
    "dir_lfw_metadata = join(dir_lfw, 'metadata')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set fp_\n",
    "fp_identities = join(dir_lfw_metadata, 'identities.csv')\n",
    "fp_files = join(dir_lfw_metadata, 'image_files.csv')\n",
    "fp_index = join(dir_lfw_metadata, 'image_index.csv')\n",
    "fp_rois = join(dir_lfw_metadata, 'image_rois.csv')\n",
    "fp_uuids = join(dir_lfw_metadata, 'image_uuids.csv')\n",
    "fp_pose = join(dir_lfw_metadata, 'image_pose.csv')\n",
    "fp_vecs = join(dir_lfw_metadata, 'image_vecs.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load csvs into dataframes\n",
    "df_identities = pd.read_csv(fp_identities).set_index('index')\n",
    "df_files = pd.read_csv(fp_files).set_index('index')\n",
    "df_index = pd.read_csv(fp_index).set_index('index')\n",
    "df_rois = pd.read_csv(fp_rois).set_index('index')\n",
    "df_uuids = pd.read_csv(fp_uuids).set_index('index')\n",
    "df_pose = pd.read_csv(fp_pose).set_index('index')\n",
    "df_vecs = pd.read_csv(fp_vecs).set_index('index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identity attributes\n",
    "\n",
    "- preview identities\n",
    "- here the 'index' is unique and refers to the identity\n",
    "- in other files it is referred to as 'identity_index'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_identities.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### File attributes\n",
    "\n",
    "- preview files\n",
    "- NB: this information is not needed for the website\n",
    "- information needed to load an image from the local directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_files.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Index\n",
    "\n",
    "- every image has an index\n",
    "- the `identity_index` refers back to the `identities.csv` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\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_index</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>550937b71b9af36b6083fa1ce7c76e97e3254c439614a6...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46d7ddeec9b00add61ade2f89277d74e8264a2b6cec193...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  sha256  identity_index\n",
       "index                                                                   \n",
       "0      550937b71b9af36b6083fa1ce7c76e97e3254c439614a6...               0\n",
       "1      46d7ddeec9b00add61ade2f89277d74e8264a2b6cec193...               1"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_index.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROIs (regions of interest)\n",
    "\n",
    "- refers to the detected face in image files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        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": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rois.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UUIDs\n",
    "\n",
    "- a separate file that includes the UUID for each image file\n",
    "- separated to reduce file size for larger CSVs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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>uuid</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f03fd921-2d56-4e83-8115-f658d6a72287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0c96c5bb-dbd1-4584-bd68-af11664b98bb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       uuid\n",
       "index                                      \n",
       "0      f03fd921-2d56-4e83-8115-f658d6a72287\n",
       "1      0c96c5bb-dbd1-4584-bd68-af11664b98bb"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uuids.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pose\n",
    "\n",
    "- a separate file that includes pose information\n",
    "- used for filtering out images that are too angled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_index</th>\n",
       "      <th>pitch</th>\n",
       "      <th>roll</th>\n",
       "      <th>yaw</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</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</td>\n",
       "      <td>11.162645</td>\n",
       "      <td>10.415886</td>\n",
       "      <td>22.997190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20.788283</td>\n",
       "      <td>-9.626046</td>\n",
       "      <td>-11.075164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       image_index      pitch       roll        yaw\n",
       "index                                              \n",
       "0                0  11.162645  10.415886  22.997190\n",
       "1                1  20.788283  -9.626046 -11.075164"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pose.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Face vectors\n",
    "\n",
    "- each face vector is associated with an ROI\n",
    "- each ROI is associated with an image and an identity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_index</th>\n",
       "      <th>roi_index</th>\n",
       "      <th>vec</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.0776684582233429,0.13982562720775604,-0.001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.12392709404230118,0.07749050855636597,0.026...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       image_index  roi_index  \\\n",
       "index                           \n",
       "0                0          0   \n",
       "1                1          1   \n",
       "\n",
       "                                                     vec  \n",
       "index                                                     \n",
       "0      -0.0776684582233429,0.13982562720775604,-0.001...  \n",
       "1      -0.12392709404230118,0.07749050855636597,0.026...  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vecs.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vector_idx: 1 --> roi_idx: 1, image_index: 1\n"
     ]
    }
   ],
   "source": [
    "# given a vector index, get identity meta and image\n",
    "vector_idx = 1\n",
    "# create a pandas.core.series.Series (ds) to store the row results\n",
    "ds_vec = df_vecs.iloc[vector_idx]\n",
    "# get the ROI index\n",
    "roi_idx = ds_vec.image_index\n",
    "# get the image index\n",
    "image_index = ds_vec.roi_index\n",
    "print(f'vector_idx: {vector_idx} --> roi_idx: {roi_idx}, image_index: {image_index}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: AJ Lamas, description: American actor, gender: m, images: 1\n"
     ]
    }
   ],
   "source": [
    "# use the roi index to get identity index from the identity DataFrame\n",
    "ds_index = df_index.iloc[image_index]\n",
    "# now get the identity index\n",
    "identity_idx = ds_index.identity_index\n",
    "# then use the identity index to get the identity meta\n",
    "ds_identity = df_identities.iloc[identity_idx]\n",
    "# get the name and description\n",
    "name = ds_identity.fullname\n",
    "desc = ds_identity.description\n",
    "gender = ds_identity.gender\n",
    "nimages = ds_identity.images\n",
    "print(f'name: {name}, description: {desc}, gender: {gender}, images: {nimages}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/data_store/datasets/people/lfw/images/AJ_Lamas/AJ_Lamas_0001.jpg\n"
     ]
    }
   ],
   "source": [
    "# use the image index to get file meta\n",
    "ds_file = df_files.iloc[image_index]\n",
    "fn = ds_file.fn\n",
    "subdir = ds_file.subdir\n",
    "ext = ds_file.ext\n",
    "fp_im = join(dir_lfw, 'images', subdir, f'{fn}.{ext}')\n",
    "print(fp_im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f19306d5e10>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot image\n",
    "im = cv.imread(fp_im)\n",
    "im_rgb = cv.cvtColor(im, cv.COLOR_BGR2RGB)\n",
    "plt.imshow(im_rgb)"
   ]
  },
  {
   "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
}