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
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
|
# standard imports
import operator
import typing
# external imports
from facenet_pytorch import MTCNN, InceptionResnetV1
import PIL.Image
import torch
# bsie imports
from bsie.utils import bsfs, errors, ns
# inner-module imports
from . import base
# exports
__all__: typing.Sequence[str] = (
'FaceExtract',
)
## code ##
class FaceExtract(base.Reader):
"""Extract faces and their feature vector from an image file."""
# Face patch size.
_target_size: int
# Lower bound on the detected face's probability.
_min_face_prob: float
# Face detector network.
_detector: MTCNN
# Face feature extractor network.
_embedder: InceptionResnetV1
def __init__(
self,
target_size: int = 1000,
min_face_size: int = 40,
min_face_prob: float = 0.992845,
cuda_device: str = 'cuda:0',
ext_face_size: int = 160,
thresholds: typing.Tuple[float, float, float] = [0.5, 0.6, 0.6],
factor: float = 0.709,
):
# initialize
self._device = torch.device(cuda_device if torch.cuda.is_available() else 'cpu')
# initialize the face detection network
self._target_size = target_size
self._min_face_prob = min_face_prob
self._carghash = hash((min_face_size, ext_face_size, tuple(thresholds), factor))
self._detector = MTCNN(
min_face_size=min_face_size,
image_size=ext_face_size,
thresholds=thresholds,
factor=factor,
device=self._device,
keep_all=True,
).to(self._device)
# initialize the face embedding netwrok
self._embedder = InceptionResnetV1('vggface2').to(self._device).eval()
def __repr__(self) -> str:
return f'{bsfs.typename(self)}({self._min_face_prob})'
def __eq__(self, other: typing.Any) -> bool:
return super().__eq__(other) \
and self._target_size == other._target_size \
and self._min_face_prob == other._min_face_prob \
and self._carghash == other._carghash
def __hash__(self) -> int:
return hash((super().__hash__(), self._target_size, self._min_face_prob, self._carghash))
@staticmethod
def preprocess(
img: PIL.Image.Image,
target_size: int,
rotate: typing.Union[bool, int] = True,
) -> typing.Tuple[PIL.Image.Image, typing.Callable[[typing.Tuple[float, float]], typing.Tuple[float, float]]]:
"""Preprocess an image. Return the image and a coordinate back-transformation function.
1. Scale larger side to *target_size*
2. Rotate by angle *rotate*, or auto-rotate if *rotate=None* (the default).
"""
# FIXME: re-using reader.Image would cover more file formats!
# >>> from PIL import ExifTags
# >>> exif_ori = [k for k, tag in ExifTags.TAGS.items() if tag == 'Orientation']
# >>> exif_ori = exif_ori[0]
exif_ori = 274
# scale image
orig_size = img.size
if img.size[0] > img.size[1]: # landscape
img = img.resize((target_size, int(img.height / img.width * target_size)), reducing_gap=3)
elif img.size[0] < img.size[1]: # portrait
img = img.resize((int(img.width / img.height * target_size), target_size), reducing_gap=3)
else: # square
img = img.resize((
int(img.width / img.height * target_size),
int(img.width / img.height * target_size),
), reducing_gap=3)
# get scale factors
sX = orig_size[0] / img.width
sY = orig_size[1] / img.height
# rotate image (if need be)
denorm = lambda xy: (sX*xy[0], sY*xy[1])
if rotate is not None:
# auto-rotate according to EXIF information
img_ori = img.getexif().get(exif_ori, None)
if img_ori == 3 or rotate == 180:
img = img.rotate(180, expand=True)
denorm = lambda xy: (orig_size[0] - sX*xy[0], orig_size[1] - sY*xy[1])
elif img_ori == 6 or rotate == 270:
img = img.rotate(270, expand=True)
denorm = lambda xy: (orig_size[0] - sX*xy[1], sY*xy[0])
elif img_ori == 8 or rotate == 90:
img = img.rotate(90, expand=True)
denorm = lambda xy: (sX*xy[1], orig_size[1] - sY*xy[0])
# return image and denormalization function
return img, denorm
def __call__(self, path: str) -> typing.Sequence[dict]:
try:
# open the image
img = PIL.Image.open(path)
# rotate and scale the image
img, denorm = self.preprocess(img, self._target_size)
# detect faces
boxes, probs = self._detector.detect(img)
if boxes is None: # no faces detected
return []
# ignore boxes with probability below threshold
boxes = [box for box, p in zip(boxes, probs) if p >= self._min_face_prob]
if len(boxes) == 0: # no faces detected
return []
# compute face embeddings
faces_img = self._detector.extract(img, boxes, None).to(self._device)
embeds = self._embedder(faces_img)
faces = []
for bbox, face, emb in zip(boxes, faces_img, embeds):
# face hash
ucid = bsfs.uuid.UCID.from_bytes(bytes(face.detach().cpu().numpy()))
# position / size
x0, y0 = denorm(bbox[:2])
x1, y1 = denorm(bbox[2:])
x, y = min(x0, x1), min(y0, y1)
width, height = max(x0, x1) - x, max(y0, y1) - y
# assembled
faces.append(dict(
ucid=ucid, # str
x=x, # float
y=y, # float
width=width, # float
height=height, # float
embedding=emb, # np.array
))
return faces
except PIL.UnidentifiedImageError as err: # format not supported by PIL
raise errors.UnsupportedFileFormatError(path) from err
except IOError as err: # file not found and file open errors
raise errors.ReaderError(path) from err
except RuntimeError as err: # pytorch errors
raise errors.ReaderError(path) from err
except ValueError as err: # negative seek value
raise errors.ReaderError(path) from err
## EOF ##
|
