text: import warnings
  warnings.filterwarnings("ignore")
  import argparse
  
  from utils.datasets import *
  from utils.utils import *
  
  import time
  
  
  def detect(save_img=False):
      out, source, weights, half, view_img, save_txt, imgsz = \
          opt.output, opt.source, opt.weights, opt.half, opt.view_img, opt.save_txt, opt.img_size
      webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
  
      # Initialize
      device = torch_utils.select_device(opt.device)
      # if os.path.exists(out):
      #     shutil.rmtree(out)  # delete output folder
      # os.makedirs(out)  # make new output folder
  
      # Load model
      google_utils.attempt_download(weights)
      model = torch.load(weights, map_location=device)['model']
      # torch.save(torch.load(weights, map_location=device), weights)  # update model if SourceChangeWarning
      # model.fuse()
      model.to(device).eval()
  
      # Second-stage classifier
      # classify = False
      # if classify:
      #     modelc = torch_utils.load_classifier(name='resnet101', n=2)  # initialize
      #     modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
      #     modelc.to(device).eval()
  
      # Half precision
      half = half and device.type != 'cpu'  # half precision only supported on CUDA
      if half:
          model.half()
  
      # Set Dataloader
      vid_path, vid_writer = None, None
      if webcam:
          view_img = True
          torch.backends.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.names if hasattr(model, 'names') else model.modules.names
      colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
  
      # Run inference
      t0 = time.time()
      img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
      _ = model(img.half() if half else img.float()) if device.type != 'cpu' else None  # run once
      for path, img, im0s, vid_cap in dataset:
          # print("pathpathpathpath",path)
          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 = torch_utils.time_synchronized()
          pred = model(img, augment=opt.augment)[0]
          t2 = torch_utils.time_synchronized()
  
          # to float
          if half:
              pred = pred.float()
  
          # Apply NMS
          pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres,
                                     fast=True, classes=opt.classes, agnostic=opt.agnostic_nms)
  
          # 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 = path[i], '%g: ' % i, im0s[i].copy()
              else:
                  p, s, im0 = path, '', im0s
  
              save_path = str(Path(out) / Path(p).name)
              s += '%gx%g ' % img.shape[2:]  # print string
              gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  #  normalization gain whwh
              if det is not None and 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 += '%g %ss, ' % (n, names[int(c)])  # add to string
  
                  # Write results
                  for *xyxy, conf, cls in det:
                      if save_txt:  # Write to file
                          xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                          with open(save_path[:save_path.rfind('.')] + '.txt', 'a') as file:
                              file.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
  
                      if save_img or view_img:  # Add bbox to image
                          label = '%s %.2f' % (names[int(cls)], conf)
                          plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
  
              # Print time (inference + NMS)
              print('%sDone. (%.3fs)' % (s, t2 - t1))
  
              # Stream results
              if True:
                  cv2.namedWindow(p,0)
                  cv2.imshow(p, im0)
                  if cv2.waitKey(1) == ord('q'):  # q to quit
                      vid_writer.release()
                      raise StopIteration
  
              # Save results (image with detections)
              if vid_writer is None:
                  fps = 22
                  w = int(im0.shape[1])
                  h = int(im0.shape[0])
                  # print("----------->>> save_path : ",save_path)
                  loc_time = time.localtime()
                  time_str = time.strftime("%Y-%m-%d_%H-%M-%S", loc_time)
                  vid_writer = cv2.VideoWriter(save_path+"_{}.mp4".format(time_str), cv2.VideoWriter_fourcc(*opt.fourcc), fps, (w, h))
              vid_writer.write(im0)
  
  
  
      if save_txt or save_img:
          print('Results saved to %s' % os.getcwd() + os.sep + out)
          if platform == 'darwin':  # MacOS
              os.system('open ' + save_path)
  
      print('Done. (%.3fs)' % (time.time() - t0))
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--weights', type=str, default='person_weights/last2.pt', help='model.pt path')
      parser.add_argument('--source', type=str, default='H:/git_project/deep_sort_pro/deep_sort_pytorch/video/dance0.mp4', help='source')  # file/folder, 0 for webcam
      parser.add_argument('--output', type=str, default='./inference/output', help='output folder')  # output folder
      parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
      parser.add_argument('--conf-thres', type=float, default=0.36, help='object confidence threshold')
      parser.add_argument('--iou-thres', type=float, default=0.55, help='IOU threshold for NMS')
      parser.add_argument('--fourcc', type=str, default='mp4v', help='output video codec (verify ffmpeg support)')
      parser.add_argument('--half', action='store_true', help='half precision FP16 inference')
      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('--classes', nargs='+', type=int, help='filter by class')
      parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
      parser.add_argument('--augment', action='store_true', help='augmented inference')
      opt = parser.parse_args()
      print(opt)
  
      with torch.no_grad():
          detect(save_img = True)
  