60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
import os
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import cv2
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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 *
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from .detect import *
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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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def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False):
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super(SFDDetector, self).__init__(device, verbose)
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# Initialise the face detector
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if not os.path.isfile(path_to_detector):
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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.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 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)
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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] > 0.5]
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return bboxlist
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def detect_from_batch(self, images):
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bboxlists = batch_detect(self.face_detector, images, device=self.device)
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keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])]
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bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)]
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bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists]
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return 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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