code: import argparse
  import time
  from pathlib import Path
  
  import cv2
  import torch
  import torch.backends.cudnn as cudnn
  from numpy import random
  
  from models.experimental import attempt_load
  from utils.datasets import LoadStreams, LoadImages
  from utils.general import check_img_size, check_requirements, non_max_suppression, apply_classifier, scale_coords
  from utils.general import xyxy2xywh, strip_optimizer, set_logging, increment_path
  from utils.plots import plot_one_box
  from utils.torch_utils import select_device, load_classifier, time_synchronized
  
  from utils.draw_name import draw_name
  
  
  def detect(save_img=False):
      source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
      webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
          ('rtsp://', 'rtmp://', 'http://'))
  
      # Directories
      save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
      (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
  
      # Initialize
      set_logging()
      device = select_device(opt.device)
      half = device.type != 'cpu'  # half precision only supported on CUDA
  
      # Load model
      model = attempt_load(weights, map_location=device)  # load FP32 model
      imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
      if half:
          model.half()  # to FP16
  
      # Second-stage classifier
      classify = False
      if classify:
          modelc = load_classifier(name='resnet101', n=2)  # initialize
          modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
  
      # Set Dataloader
      vid_path, vid_writer = None, None
      if webcam:
          view_img = True
          cudnn.benchmark = True  # set True to speed up constant image size inference
          dataset = LoadStreams(source, img_size=imgsz)
      else:
          save_img = True
          dataset = LoadImages(source, img_size=imgsz)
  
      # Get names and colors
      names = model.module.names if hasattr(model, 'module') else model.names
      colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
  
      # Run inference
      t0 = time.time()
      img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
      _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
      for path, img, im0s, vid_cap in dataset:
          img = torch.from_numpy(img).to(device)
          img = img.half() if half else img.float()  # uint8 to fp16/32
          img /= 255.0  # 0 - 255 to 0.0 - 1.0
          if img.ndimension() == 3:
              img = img.unsqueeze(0)
  
          # Inference
          t1 = time_synchronized()
          pred = model(img, augment=opt.augment)[0]
  
          # Apply NMS
          pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
          t2 = time_synchronized()
  
          # Apply Classifier
          if classify:
              pred = apply_classifier(pred, modelc, img, im0s)
  
          # Process detections
          for i, det in enumerate(pred):  # detections per image
              if webcam:  # batch_size >= 1
                  p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count
              else:
                  p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)
  
              p = Path(p)  # to Path
              save_path = str(save_dir / p.name)  # img.jpg
              txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt
              s += '%gx%g ' % img.shape[2:]  # print string
              gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
              if len(det):
                  # Rescale boxes from img_size to im0 size
                  det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
  
                  # Print results
                  for c in det[:, -1].unique():
                      n = (det[:, -1] == c).sum()  # detections per class
                      s += f'{n} {names[int(c)]}s, '  # add to string
  
                  # Write results
                  for *xyxy, conf, cls in reversed(det):
                      if save_txt:  # Write to file
                          xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                          line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
                          with open(txt_path + '.txt', 'a') as f:
                              f.write(('%g ' * len(line)).rstrip() % line + '\n')
  
                      if save_img or view_img:  # Add bbox to image
                          cv2.imwrite("img.jpg", im0)
                          im0 = draw_name(im0, colors[int(cls)])  # 填上人名
                          label = f'{names[int(cls)]} {conf:.2f}'
                          plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
  
              # Print time (inference + NMS)
              print(f'{s}Done. ({t2 - t1:.3f}s)')
  
              # Stream results
              if view_img:
                  cv2.imshow('Masks detect', im0)
  
              # Save results (image with detections)
              if save_img:
                  if dataset.mode == 'image':
                      cv2.imwrite(save_path, im0)
                  else:  # 'video'
                      if vid_path != save_path:  # new video
                          vid_path = save_path
                          if isinstance(vid_writer, cv2.VideoWriter):
                              vid_writer.release()  # release previous video writer
  
                          fourcc = 'mp4v'  # output video codec
                          fps = vid_cap.get(cv2.CAP_PROP_FPS)
                          w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                          h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                          vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
                      vid_writer.write(im0)
  
      if save_txt or save_img:
          s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
          print(f"Results saved to {save_dir}{s}")
  
      print(f'Done. ({time.time() - t0:.3f}s)')
  
  
  def main():
      parser = argparse.ArgumentParser()
  
      parser.add_argument('--weights', nargs='+', type=str, default='runs/train/exp/weights/best.pt',
                          help='model.pt path(s)')
      parser.add_argument('--source', type=str, default='0', help='source')  # file/folder, 0 for webcam
      parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
      parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
      parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
      parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
      parser.add_argument('--view-img', action='store_true', help='display results')
      parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
      parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
      parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
      parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
      parser.add_argument('--augment', action='store_true', help='augmented inference')
      parser.add_argument('--update', action='store_true', help='update all models')
      parser.add_argument('--project', default='runs/detect', help='save results to project/name')
      parser.add_argument('--name', default='exp', help='save results to project/name')
      parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
      global opt
      opt = parser.parse_args()
      print(opt)
      check_requirements()
  
      with torch.no_grad():
          if opt.update:  # update all models (to fix SourceChangeWarning)
              for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                  detect()
                  strip_optimizer(opt.weights)
          else:
              detect()
  
apis: torch.load torch.no_grad torch.tensor torch.from_numpy torch.zeros numpy.random.randint