{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# VGG Face2 Names\n", "\n", "- add knowledge graph description with socre" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "\n", "import os\n", "from os.path import join\n", "import math\n", "from glob import glob\n", "from random import randint\n", "\n", "import numpy as np\n", "import pandas as pd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "import time\n", "import datetime\n", "import requests\n", "import json\n", "import urllib\n", "\n", "import sys\n", "sys.path.append('/work/megapixels_dev/megapixels/')\n", "from app.utils import file_utils" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "DATA_STORE = '/data_store_nas/'\n", "dir_dataset = join(DATA_STORE, 'datasets/people/vgg_face2')\n", "fp_meta = join(dir_dataset, 'identity_meta_test.csv')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "# load names\n", "df_meta = pd.read_csv(fp_meta)\n", "meta = df_meta.to_dict('index')\n", "meta_orig = meta.copy()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "# change first_last to first last\n", "for idx, item in meta.items():\n", " name = item['name'].replace('_',' ')\n", " if '. ' in name[:3] and '. ' in name[2:6]:\n", " name = name[:6].replace('. ', '') + ' ' + name[6:]\n", " item['name'] = name" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
| \n", " | class_id | \n", "name | \n", "images | \n", "flag | \n", "gender | \n", "
|---|---|---|---|---|---|
| 0 | \n", "n000001 | \n", "14th_Dalai_Lama | \n", "424 | \n", "0 | \n", "m | \n", "
| 1 | \n", "n000002 | \n", "A_Fine_Frenzy | \n", "315 | \n", "1 | \n", "f | \n", "
| 2 | \n", "n000003 | \n", "A._A._Gill | \n", "205 | \n", "1 | \n", "m | \n", "
| 3 | \n", "n000004 | \n", "A._J._Buckley | \n", "387 | \n", "1 | \n", "m | \n", "
| 4 | \n", "n000005 | \n", "A._J._Pierzynski | \n", "229 | \n", "1 | \n", "m | \n", "