text: import argparse
  from sys import platform
  
  from models import *  # set ONNX_EXPORT in models.py
  from utils.datasets import *
  from utils.utils import *
  
  
  def detect(save_txt=False, save_img=False):
      img_size = (320, 192) if ONNX_EXPORT else opt.img_size  # (320, 192) or (416, 256) or (608, 352) for (height, width)
      out, source, weights, half, view_img = opt.output, opt.source, opt.weights, opt.half, opt.view_img
      webcam = source == '0' or source.startswith('rtsp') or source.startswith('http') or source.endswith('.txt')
  
      # Initialize
      device = torch_utils.select_device(device='cpu' if ONNX_EXPORT else opt.device)
      if os.path.exists(out):
          shutil.rmtree(out)  # delete output folder
      os.makedirs(out)  # make new output folder
  
      # Initialize model
      model = Darknet(opt.cfg, img_size)
  
      # Load weights
      attempt_download(weights)
      if weights.endswith('.pt'):  # pytorch format
          model.load_state_dict(torch.load(weights, map_location=device)['model'])
      else:  # darknet format
          _ = load_darknet_weights(model, weights)
  
      # Fuse Conv2d + BatchNorm2d layers
      # model.fuse()
  
      # Eval mode
      model.to(device).eval()
  
      # Export mode
      if ONNX_EXPORT:
          img = torch.zeros((1, 3) + img_size)  # (1, 3, 320, 192)
          torch.onnx.export(model, img, 'weights/export.onnx', verbose=True)
          return
  
      # 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=img_size, half=half)
      else:
          save_img = True
          dataset = LoadImages(source, img_size=img_size, half=half)
  
      # Get classes and colors
      classes = load_classes(parse_data_cfg(opt.data)['names'])
      colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(classes))]
  
      # Run inference
      t0 = time.time()
      for path, img, im0s, vid_cap in dataset:
          t = time.time()
  
          # Get detections
          img = torch.from_numpy(img).to(device)
          if img.ndimension() == 3:
              img = img.unsqueeze(0)
          pred, _ = model(img)
  
          if opt.half:
              pred = pred.float()
  
          for i, det in enumerate(non_max_suppression(pred, opt.conf_thres, opt.nms_thres)):  # detections per image
              if webcam:  # batch_size >= 1
                  p, s, im0 = path[i], '%g: ' % i, im0s[i]
              else:
                  p, s, im0 = path, '', im0s
  
              save_path = str(Path(out) / Path(p).name)
              s += '%gx%g ' % img.shape[2:]  # print string
              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, classes[int(c)])  # add to string
  
                  # Write results
                  for *xyxy, conf, _, cls in det:
                      if save_txt:  # Write to file
                          with open(save_path + '.txt', 'a') as file:
                              file.write(('%g ' * 6 + '\n') % (*xyxy, cls, conf))
  
                      if save_img or view_img:  # Add bbox to image
                          label = '%s %.2f' % (classes[int(cls)], conf)
                          plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
  
              print('%sDone. (%.3fs)' % (s, time.time() - t))
  
              # Stream results
              if view_img:
                  cv2.imshow(p, im0)
  
              # Save results (image with detections)
              if save_img:
                  if dataset.mode == 'images':
                      cv2.imwrite(save_path, im0)
                  else:
                      if vid_path != save_path:  # new video
                          vid_path = save_path
                          if isinstance(vid_writer, cv2.VideoWriter):
                              vid_writer.release()  # release previous video writer
  
                          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(*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 ' + out + ' ' + save_path)
  
      print('Done. (%.3fs)' % (time.time() - t0))
  
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--cfg', type=str, default='cfg/yolov3-1cls.cfg', help='cfg file path')
      parser.add_argument('--data', type=str, default='data/coco.data', help='coco.data file path')
      parser.add_argument('--weights', type=str, default='weights/test/backup270.pt', help='path to weights file')
      parser.add_argument('--source', type=str, default='video', help='source')  # input file/folder, 0 for webcam
      parser.add_argument('--output', type=str, default='output', help='output folder')  # output folder
      parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
      parser.add_argument('--conf-thres', type=float, default=0.7, help='object confidence threshold')
      parser.add_argument('--nms-thres', type=float, default=0.5, help='iou threshold for non-maximum suppression')
      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='device id (i.e. 0 or 0,1) or cpu')
      parser.add_argument('--view-img', action='store_true', help='display results')
      opt = parser.parse_args()
      print(opt)
  
      with torch.no_grad():
          detect()
  