[img.shape[1], img.shape[0], img.shape[1], img.shape[0]]) img -= (104, 117, 123) img = img.transpose(2, 0, 1) img = torch.from_numpy(img).unsqueeze(0) img = img.to(device) scale = scale.to(device) _t['forward_pass'].tic() loc, conf, landms = net(img) # forward pass _t['forward_pass'].toc() _t['misc'].tic() priorbox = PriorBox(cfg, image_size=(im_height, im_width)) priors = priorbox.forward() priors = priors.to(device) prior_data = priors.data boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance']) boxes = boxes * scale / resize boxes = boxes.cpu().numpy() scores = conf.squeeze(0).data.cpu().numpy()[:, 1] landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance']) scale1 = torch.Tensor([ img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2] ]) scale1 = scale1.to(device) landms = landms * scale1 / resize landms = landms.cpu().numpy() # ignore low scores
def detect(self, img, *_): # testing scale img = img.astype(np.float32) target_size = 1600 max_size = 2150 im_shape = img.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) resize = float(target_size) / float(im_size_min) # prevent bigger axis from being more than max_size: if np.round(resize * im_size_max) > max_size: resize = float(max_size) / float(im_size_max) if self.origin_size: resize = 1 if resize != 1: img = cv2.resize(img, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) im_height, im_width, _ = img.shape scale = torch.Tensor( [img.shape[1], img.shape[0], img.shape[1], img.shape[0]]) img -= (104, 117, 123) img = img.transpose(2, 0, 1) img = torch.from_numpy(img).unsqueeze(0) img = img.to(self.device) scale = scale.to(self.device) self._t['forward_pass'].tic() loc, conf, landms = self.net(img) # forward pass self._t['forward_pass'].toc() self._t['misc'].tic() priorbox = PriorBox(self.cfg, image_size=(im_height, im_width)) priors = priorbox.forward() priors = priors.to(self.device) prior_data = priors.data boxes = decode(loc.data.squeeze(0), prior_data, self.cfg['variance']) boxes = boxes * scale / resize boxes = boxes.cpu().numpy() scores = conf.squeeze(0).data.cpu().numpy()[:, 1] landms = decode_landm(landms.data.squeeze(0), prior_data, self.cfg['variance']) scale1 = torch.Tensor([ img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2], img.shape[3], img.shape[2] ]) scale1 = scale1.to(self.device) landms = landms * scale1 / resize landms = landms.cpu().numpy() # ignore low scores inds = np.where(scores > self.confidence_threshold)[0] boxes = boxes[inds] landms = landms[inds] scores = scores[inds] # keep top-K before NMS order = scores.argsort()[::-1] # order = scores.argsort()[::-1][:args.top_k] boxes = boxes[order] landms = landms[order] scores = scores[order] # do NMS dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) keep = py_cpu_nms(dets, self.nms_threshold) # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu) dets = dets[keep, :] landms = landms[keep] # keep top-K faster NMS # dets = dets[:args.keep_top_k, :] # landms = landms[:args.keep_top_k, :] #dets = np.concatenate((dets, landms), axis=1) self._t['misc'].toc() return dets