text: import pdb
  import torch
  from PIL import Image
  from LightheadRCNN_Learner import LightHeadRCNN_Learner
  from utils.dataset import coco_dataset, prepare_img
  import argparse
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(description='detect over image')
      parser.add_argument('-f','--file_path', help='image url to detect', default='data/room.jpg', type=str)
      parser.add_argument('-o','--output_path', help='detection output path', default='data/room_detected.jpg', type=str)
      parser.add_argument('-m','--model_name', help='trained model path', default='final/lighthead_rcnn_model_gpu.pth', type=str)
      args = parser.parse_args()
      learner = LightHeadRCNN_Learner(training=False)
      learner.load_state_dict(torch.load(learner.conf.work_space/args.model_name))
      img = Image.open(args.file_path)
      predicted_img = learner.predict_on_img(img, preset='detect', return_img=True, with_scores=False, original_size=True)
      predicted_img.save(args.output_path)