64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
import time
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import numpy as np
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import cv2
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import torch
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from torchvision import transforms
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from .nets import S3FDNet
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from .box_utils import nms_
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from latentsync.utils.util import check_model_and_download
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PATH_WEIGHT = "checkpoints/auxiliary/sfd_face.pth"
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img_mean = np.array([104.0, 117.0, 123.0])[:, np.newaxis, np.newaxis].astype("float32")
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class S3FD:
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def __init__(self, device="cuda"):
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tstamp = time.time()
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self.device = device
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print("[S3FD] loading with", self.device)
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self.net = S3FDNet(device=self.device).to(self.device)
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check_model_and_download(PATH_WEIGHT)
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state_dict = torch.load(PATH_WEIGHT, map_location=self.device, weights_only=True)
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self.net.load_state_dict(state_dict)
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self.net.eval()
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print("[S3FD] finished loading (%.4f sec)" % (time.time() - tstamp))
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def detect_faces(self, image, conf_th=0.8, scales=[1]):
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w, h = image.shape[1], image.shape[0]
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bboxes = np.empty(shape=(0, 5))
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with torch.no_grad():
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for s in scales:
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scaled_img = cv2.resize(image, dsize=(0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR)
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scaled_img = np.swapaxes(scaled_img, 1, 2)
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scaled_img = np.swapaxes(scaled_img, 1, 0)
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scaled_img = scaled_img[[2, 1, 0], :, :]
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scaled_img = scaled_img.astype("float32")
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scaled_img -= img_mean
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scaled_img = scaled_img[[2, 1, 0], :, :]
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x = torch.from_numpy(scaled_img).unsqueeze(0).to(self.device)
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y = self.net(x)
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detections = y.data
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scale = torch.Tensor([w, h, w, h])
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for i in range(detections.size(1)):
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j = 0
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while detections[0, i, j, 0] > conf_th:
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score = detections[0, i, j, 0]
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pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
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bbox = (pt[0], pt[1], pt[2], pt[3], score)
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bboxes = np.vstack((bboxes, bbox))
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j += 1
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keep = nms_(bboxes, 0.1)
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bboxes = bboxes[keep]
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return bboxes
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