content: import os
  import numpy as np
  import torch
  from PIL import Image
  import time
  
  from segment_anything import sam_model_registry, SamPredictor
  
  def sam_init(device_id=0):
      sam_checkpoint = os.path.join(os.path.dirname(__file__), "../sam_vit_h_4b8939.pth")
      model_type = "vit_h"
  
      device = "cuda:{}".format(device_id) if torch.cuda.is_available() else "cpu"
  
      sam = sam_model_registry[model_type](checkpoint=sam_checkpoint).to(device=device)
      predictor = SamPredictor(sam)
      return predictor
  
  def sam_out_nosave(predictor, input_image, *bbox_sliders):
      bbox = np.array(bbox_sliders)
      image = np.asarray(input_image)
  
      start_time = time.time()
      predictor.set_image(image)
  
      masks_bbox, scores_bbox, logits_bbox = predictor.predict(
          box=bbox,
          multimask_output=True
      )
  
      print(f"SAM Time: {time.time() - start_time:.3f}s")
      out_image = np.zeros((image.shape[0], image.shape[1], 4), dtype=np.uint8)
      out_image[:, :, :3] = image
      out_image_bbox = out_image.copy()
      out_image_bbox[:, :, 3] = masks_bbox[-1].astype(np.uint8) * 255 # np.argmax(scores_bbox)
      torch.cuda.empty_cache()
      return Image.fromarray(out_image_bbox, mode='RGBA') 