72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
import torch
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from torch.utils.model_zoo import load_url
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from ..core import FaceDetector
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from .net_s3fd import s3fd
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from .bbox import nms
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from .detect import detect, batch_detect
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models_urls = {
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's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth',
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}
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class SFDDetector(FaceDetector):
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'''SF3D Detector.
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'''
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def __init__(self, device, path_to_detector=None, verbose=False, filter_threshold=0.5):
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super(SFDDetector, self).__init__(device, verbose)
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# Initialise the face detector
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if path_to_detector is None:
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model_weights = load_url(models_urls['s3fd'])
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else:
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model_weights = torch.load(path_to_detector)
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self.fiter_threshold = filter_threshold
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self.face_detector = s3fd()
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self.face_detector.load_state_dict(model_weights)
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self.face_detector.to(device)
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self.face_detector.eval()
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def _filter_bboxes(self, bboxlist):
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if len(bboxlist) > 0:
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keep = nms(bboxlist, 0.3)
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bboxlist = bboxlist[keep, :]
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bboxlist = [x for x in bboxlist if x[-1] > self.fiter_threshold]
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return bboxlist
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def detect_from_image(self, tensor_or_path):
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image = self.tensor_or_path_to_ndarray(tensor_or_path)
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bboxlist = detect(self.face_detector, image, device=self.device)[0]
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bboxlist = self._filter_bboxes(bboxlist)
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return bboxlist
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def detect_from_batch(self, tensor):
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bboxlists = batch_detect(self.face_detector, tensor, device=self.device)
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new_bboxlists = []
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for i in range(bboxlists.shape[0]):
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bboxlist = bboxlists[i]
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bboxlist = self._filter_bboxes(bboxlist)
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new_bboxlists.append(bboxlist)
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return new_bboxlists
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@property
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def reference_scale(self):
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return 195
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@property
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def reference_x_shift(self):
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return 0
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@property
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def reference_y_shift(self):
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return 0
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