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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from os.path import join\n",
"import sys\n",
"from pathlib import Path\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from PIL import Image\n",
"import cv2 as cv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"fp_attr = '/data_store_hdd/datasets/people/megaface/metadata/face_attributes.csv'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(fp_attr)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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>index</th>\n",
" <th>age_apparent</th>\n",
" <th>age_real</th>\n",
" <th>f</th>\n",
" <th>m</th>\n",
" <th>roi_index</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>11.05</td>\n",
" <td>18.63</td>\n",
" <td>0.8155</td>\n",
" <td>0.1845</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>28.59</td>\n",
" <td>26.64</td>\n",
" <td>0.0219</td>\n",
" <td>0.9781</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>12.09</td>\n",
" <td>19.08</td>\n",
" <td>0.6808</td>\n",
" <td>0.3192</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>39.36</td>\n",
" <td>51.36</td>\n",
" <td>0.9943</td>\n",
" <td>0.0057</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>41.84</td>\n",
" <td>52.25</td>\n",
" <td>0.8226</td>\n",
" <td>0.1774</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index age_apparent age_real f m roi_index\n",
"0 0 11.05 18.63 0.8155 0.1845 0\n",
"1 1 28.59 26.64 0.0219 0.9781 1\n",
"2 2 12.09 19.08 0.6808 0.3192 2\n",
"3 3 39.36 51.36 0.9943 0.0057 3\n",
"4 4 41.84 52.25 0.8226 0.1774 4"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Less than 21yr: 311,973 (apparent), 168,619 (real)\n",
"Less than 18yr: 175,628 (apparent), 53,602 (real)\n",
"Less than 12yr: 35,235 (apparent), 773 (real)\n",
"Less than 8yr: 1,488 (apparent), 0 (real)\n"
]
}
],
"source": [
"brackets = [21, 18, 12, 8]\n",
"for b in brackets:\n",
" age_ap = len(df[df['age_apparent'] < b])\n",
" age_real = len(df[df['age_real'] < b])\n",
" print(f\"Less than {b}yr: {age_ap:,} (apparent), {age_real:,} (real)\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1559780"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "megapixels",
"language": "python",
"name": "megapixels"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|
