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# standard imports
import typing
# external imports
from facenet_pytorch import MTCNN, InceptionResnetV1
import PIL.Image
import torch
# bsie imports
from bsie.utils import bsfs, errors
# 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,
): # pylint: disable=too-many-arguments
# 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
scale_x = orig_size[0] / img.width
scale_y = orig_size[1] / img.height
# rotate image (if need be)
denorm = lambda xy: (scale_x*xy[0], scale_y*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] - scale_x*xy[0], orig_size[1] - scale_y*xy[1])
elif img_ori == 6 or rotate == 270:
img = img.rotate(270, expand=True)
denorm = lambda xy: (orig_size[0] - scale_x*xy[1], scale_y*xy[0])
elif img_ori == 8 or rotate == 90:
img = img.rotate(90, expand=True)
denorm = lambda xy: (scale_x*xy[1], orig_size[1] - scale_y*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 ##
|
