12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061 |
- import time
- import numpy as np
- import cv2
- import torch
- from torchvision import transforms
- from .nets import S3FDNet
- from .box_utils import nms_
- PATH_WEIGHT = './detectors/s3fd/weights/sfd_face.pth'
- img_mean = np.array([104., 117., 123.])[:, np.newaxis, np.newaxis].astype('float32')
- class S3FD():
- def __init__(self, device='cuda'):
- tstamp = time.time()
- self.device = device
- print('[S3FD] loading with', self.device)
- self.net = S3FDNet(device=self.device).to(self.device)
- state_dict = torch.load(PATH_WEIGHT, map_location=self.device)
- self.net.load_state_dict(state_dict)
- self.net.eval()
- print('[S3FD] finished loading (%.4f sec)' % (time.time() - tstamp))
-
- def detect_faces(self, image, conf_th=0.8, scales=[1]):
- w, h = image.shape[1], image.shape[0]
- bboxes = np.empty(shape=(0, 5))
- with torch.no_grad():
- for s in scales:
- scaled_img = cv2.resize(image, dsize=(0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR)
- scaled_img = np.swapaxes(scaled_img, 1, 2)
- scaled_img = np.swapaxes(scaled_img, 1, 0)
- scaled_img = scaled_img[[2, 1, 0], :, :]
- scaled_img = scaled_img.astype('float32')
- scaled_img -= img_mean
- scaled_img = scaled_img[[2, 1, 0], :, :]
- x = torch.from_numpy(scaled_img).unsqueeze(0).to(self.device)
- y = self.net(x)
- detections = y.data
- scale = torch.Tensor([w, h, w, h])
- for i in range(detections.size(1)):
- j = 0
- while detections[0, i, j, 0] > conf_th:
- score = detections[0, i, j, 0]
- pt = (detections[0, i, j, 1:] * scale).cpu().numpy()
- bbox = (pt[0], pt[1], pt[2], pt[3], score)
- bboxes = np.vstack((bboxes, bbox))
- j += 1
- keep = nms_(bboxes, 0.1)
- bboxes = bboxes[keep]
- return bboxes
|