diff options
Diffstat (limited to 'bsie/reader/face.py')
| -rw-r--r-- | bsie/reader/face.py | 23 |
1 files changed, 12 insertions, 11 deletions
diff --git a/bsie/reader/face.py b/bsie/reader/face.py index e43b93f..5e38101 100644 --- a/bsie/reader/face.py +++ b/bsie/reader/face.py @@ -1,6 +1,5 @@ # standard imports -import operator import typing # external imports @@ -9,7 +8,7 @@ import PIL.Image import torch # bsie imports -from bsie.utils import bsfs, errors, ns +from bsie.utils import bsfs, errors # inner-module imports from . import base @@ -44,9 +43,9 @@ class FaceExtract(base.Reader): 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], + 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 @@ -81,7 +80,9 @@ class FaceExtract(base.Reader): 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]]]: + ) -> 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). @@ -106,23 +107,23 @@ class FaceExtract(base.Reader): ), reducing_gap=3) # get scale factors - sX = orig_size[0] / img.width - sY = orig_size[1] / img.height + scale_x = orig_size[0] / img.width + scale_y = orig_size[1] / img.height # rotate image (if need be) - denorm = lambda xy: (sX*xy[0], sY*xy[1]) + 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] - sX*xy[0], orig_size[1] - sY*xy[1]) + 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] - sX*xy[1], sY*xy[0]) + 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: (sX*xy[1], orig_size[1] - sY*xy[0]) + denorm = lambda xy: (scale_x*xy[1], orig_size[1] - scale_y*xy[0]) # return image and denormalization function return img, denorm |