| 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 = 'checkpoints/auxiliary/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 | |