content: import os
  import trimesh
  import numpy as np
  import argparse
  import json
  import torch
  from huggingface_hub import snapshot_download
  
  from partcrafter_src.utils.image_utils import prepare_image
  from partcrafter_src.models.briarmbg import BriaRMBG
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser()
      parser.add_argument('--input', type=str, default='preprocessed_data/scissors/scissors.png')
      parser.add_argument('--output', type=str, default='preprocessed_data')
      args = parser.parse_args()
  
      input_path = args.input
      output_path = args.output
  
      assert os.path.exists(input_path), f'{input_path} does not exist'
  
      mesh_name = os.path.basename(os.path.dirname(input_path))
      output_path = os.path.join(output_path, mesh_name)
      if not os.path.exists(output_path):
          os.makedirs(output_path)
  
      rmbg_weights_dir = "pretrained_weights/RMBG-1.4"
      snapshot_download(repo_id="briaai/RMBG-1.4", local_dir=rmbg_weights_dir)
      device = "cuda" if torch.cuda.is_available() else "cpu"
      rmbg_net = BriaRMBG.from_pretrained(rmbg_weights_dir).to(device)
      rendering_rmbg = prepare_image(input_path, bg_color=np.array([1.0, 1.0, 1.0]), rmbg_net=rmbg_net, device=device)
      rendering_rmbg.save(os.path.join(output_path, f'rendering_rmbg.png'))