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
|
"""
Converts ROIs to face vector
NB: the VGG Face2 extractor should be used with MTCNN ROIs (not square)
the DLIB face extractor should be used with DLIB ROIs (square)
see https://github.com/ox-vgg/vgg_face2 for TAR@FAR
"""
import click
from app.settings import types
from app.utils import click_utils
from app.settings import app_cfg as cfg
@click.command()
@click.option('-o', '--output', 'opt_fp_out', default=None,
help='Override enum output filename CSV')
@click.option('-m', '--media', 'opt_dir_media', default=None,
help='Override enum media directory')
@click.option('--store', 'opt_data_store',
type=cfg.DataStoreVar,
default=click_utils.get_default(types.DataStore.HDD),
show_default=True,
help=click_utils.show_help(types.Dataset))
@click.option('--dataset', 'opt_dataset',
type=cfg.DatasetVar,
required=True,
show_default=True,
help=click_utils.show_help(types.Dataset))
@click.option('--size', 'opt_size',
type=(int, int), default=cfg.DEFAULT_SIZE_FACE_DETECT,
help='Output image size')
@click.option('-e', '--extractor', 'opt_extractor',
default=click_utils.get_default(types.FaceExtractor.VGG),
type=cfg.FaceExtractorVar,
help='Type of extractor framework/network to use')
@click.option('-j', '--jitters', 'opt_jitters', default=cfg.DLIB_FACEREC_JITTERS,
help='Number of jitters (only for dlib')
@click.option('-p', '--padding', 'opt_padding', default=cfg.FACEREC_PADDING,
help='Percentage ROI padding')
@click.option('--slice', 'opt_slice', type=(int, int), default=(None, None),
help='Slice list of files')
@click.option('-f', '--force', 'opt_force', is_flag=True,
help='Force overwrite file')
@click.option('-g', '--gpu', 'opt_gpu', default=0,
help='GPU index')
@click.pass_context
def cli(ctx, opt_fp_out, opt_dir_media, opt_data_store, opt_dataset, opt_size,
opt_extractor, opt_slice, opt_force, opt_gpu, opt_jitters, opt_padding):
"""Converts face ROIs to vectors"""
import sys
import os
from os.path import join
from pathlib import Path
from glob import glob
from tqdm import tqdm
import numpy as np
import dlib # must keep a local reference for dlib
import cv2 as cv
import pandas as pd
from app.models.bbox import BBox
from app.models.data_store import DataStore
from app.utils import logger_utils, file_utils, im_utils
from app.processors import face_extractor
# -------------------------------------------------
# init here
log = logger_utils.Logger.getLogger()
# set data_store
data_store = DataStore(opt_data_store, opt_dataset)
# get filepath out
fp_out = data_store.metadata(types.Metadata.FACE_VECTOR) if opt_fp_out is None else opt_fp_out
if not opt_force and Path(fp_out).exists():
log.error('File exists. Use "-f / --force" to overwite')
return
# init face processors
if opt_extractor == types.FaceExtractor.DLIB:
log.debug('set dlib')
extractor = face_extractor.ExtractorDLIB(gpu=opt_gpu, jitters=opt_jitters)
elif opt_extractor == types.FaceExtractor.VGG:
extractor = face_extractor.ExtractorVGG()
# load data
fp_record = data_store.metadata(types.Metadata.FILE_RECORD)
df_record = pd.read_csv(fp_record, dtype=cfg.FILE_RECORD_DTYPES).set_index('index')
fp_roi = data_store.metadata(types.Metadata.FACE_ROI)
df_roi = pd.read_csv(fp_roi).set_index('index')
if opt_slice:
df_roi = df_roi[opt_slice[0]:opt_slice[1]]
# -------------------------------------------------
# process images
df_img_groups = df_roi.groupby('record_index')
log.debug('processing {:,} groups'.format(len(df_img_groups)))
vecs = []
for record_index, df_img_group in tqdm(df_img_groups):
# make fp
ds_record = df_record.iloc[record_index]
fp_im = data_store.face(ds_record.subdir, ds_record.fn, ds_record.ext)
im = cv.imread(fp_im)
im = im_utils.resize(im, width=opt_size[0], height=opt_size[1])
for roi_index, df_img in df_img_group.iterrows():
# get bbox
x, y, w, h = df_img.x, df_img.y, df_img.w, df_img.h
dim = (ds_record.width, ds_record.height)
# get face vector
bbox = BBox.from_xywh(x, y, w, h) # norm
# compute vec
vec = extractor.extract(im, bbox) # use normalized BBox
vec_str = extractor.to_str(vec)
vec_obj = {'vec':vec_str, 'roi_index': roi_index, 'record_index':record_index}
vecs.append(vec_obj)
# -------------------------------------------------
# save data
# create DataFrame and save to CSV
df = pd.DataFrame.from_dict(vecs)
df.index.name = 'index'
file_utils.mkdirs(fp_out)
df.to_csv(fp_out)
# save script
file_utils.write_text(' '.join(sys.argv), '{}.sh'.format(fp_out))
|
