1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
|
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Age Gender to CSV for Site"
]
},
{
"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",
"import math\n",
"import time\n",
"from glob import glob\n",
"import datetime\n",
"from collections import OrderedDict\n",
"from operator import itemgetter\n",
"from datetime import datetime\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from pathlib import Path\n",
"from tqdm import tqdm_notebook as tqdm\n",
"import imutils\n",
"import random\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import random\n",
"\n",
"import sys\n",
"sys.path.append('/work/megapixels_dev/megapixels')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"dk = 'helen'\n",
"fp_in = '/data_store_hdd/datasets/people/helen/metadata/face_attributes.csv'\n",
"fp_out_age = f'/work/megapixels_dev/site/content/pages/datasets/{dk}/assets/age.csv'\n",
"fp_out_gender = f'/work/megapixels_dev/site/content/pages/datasets/{dk}/assets/gender.csv'"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(fp_in)"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"# Age\n",
"results = []\n",
"brackets = [(0, 12), (13, 18), (19,24), (25, 34), (35, 44), (45, 54), (55, 64), (64, 75), (75, 100)]\n",
"df_age = df['age_real']\n",
"\n",
"for a1, a2 in brackets:\n",
" n = len(df_age.loc[((df_age >= a1) & (df_age <= a2))])\n",
" results.append({'age': f'{a1} - {a2}', 'faces': n})\n",
" \n",
"df_out = pd.DataFrame.from_dict(results)\n",
"df_out = df_out[['age','faces']]\n",
"df_out.to_csv(fp_out_age, index=False)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"# Gender\n",
"results = []\n",
"\n",
"df_f = df['f']\n",
"nm = len(df_f.loc[((df_f < 0.33))])\n",
"nnb = len(df_f.loc[((df_f >= 0.33) & (df_f <= 0.66))])\n",
"nf = len(df_f.loc[((df_f > 0.66))])\n",
"\n",
"results = []\n",
"results.append({'gender': 'male', 'faces':nm})\n",
"results.append({'gender': 'female', 'faces': nf})\n",
"results.append({'gender': 'they', 'faces': nnb})\n",
"\n",
"df_out = pd.DataFrame.from_dict(results)\n",
"df_out = df_out[['gender','faces']]\n",
"df_out.to_csv(fp_out_gender, index=False)"
]
},
{
"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
}
|
