Spaces:
Running
Running
File size: 1,809 Bytes
80ebd45 |
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 |
import os
import cv2
from torch.utils.model_zoo import load_url
from ..core import FaceDetector
from .net_s3fd import s3fd
from .bbox import *
from .detect import *
models_urls = {
's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth',
}
class SFDDetector(FaceDetector):
def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False):
super(SFDDetector, self).__init__(device, verbose)
# Initialise the face detector
if not os.path.isfile(path_to_detector):
model_weights = load_url(models_urls['s3fd'])
else:
model_weights = torch.load(path_to_detector)
self.face_detector = s3fd()
self.face_detector.load_state_dict(model_weights)
self.face_detector.to(device)
self.face_detector.eval()
def detect_from_image(self, tensor_or_path):
image = self.tensor_or_path_to_ndarray(tensor_or_path)
bboxlist = detect(self.face_detector, image, device=self.device)
keep = nms(bboxlist, 0.3)
bboxlist = bboxlist[keep, :]
bboxlist = [x for x in bboxlist if x[-1] > 0.5]
return bboxlist
def detect_from_batch(self, images):
bboxlists = batch_detect(self.face_detector, images, device=self.device)
keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])]
bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)]
bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists]
return bboxlists
@property
def reference_scale(self):
return 195
@property
def reference_x_shift(self):
return 0
@property
def reference_y_shift(self):
return 0
|