content: import os
  import torch
  import argparse
  from PIL import Image
  from utils.zero123_utils import init_model, predict_stage1_gradio, zero123_infer
  from utils.sam_utils import sam_init, sam_out_nosave
  from utils.utils import pred_bbox, image_preprocess_nosave, gen_poses, convert_mesh_format
  from elevation_estimate.estimate_wild_imgs import estimate_elev
  
  
  def preprocess(predictor, raw_im, lower_contrast=False):
      raw_im.thumbnail([512, 512], Image.Resampling.LANCZOS)
      image_sam = sam_out_nosave(predictor, raw_im.convert("RGB"), pred_bbox(raw_im))
      input_256 = image_preprocess_nosave(image_sam, lower_contrast=lower_contrast, rescale=True)
      torch.cuda.empty_cache()
      return input_256
  
  def stage1_run(model, device, exp_dir,
                 input_im, scale, ddim_steps):
      # folder to save the stage 1 images
      stage1_dir = os.path.join(exp_dir, "stage1_8")
      os.makedirs(stage1_dir, exist_ok=True)
  
      # stage 1: generate 4 views at the same elevation as the input
      output_ims = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(4)), device=device, ddim_steps=ddim_steps, scale=scale)
      
      # stage 2 for the first image
      # infer 4 nearby views for an image to estimate the polar angle of the input
      stage2_steps = 50 # ddim_steps
      zero123_infer(model, exp_dir, indices=[0], device=device, ddim_steps=stage2_steps, scale=scale)
      # estimate the camera pose (elevation) of the input image.
      try:
          polar_angle = int(estimate_elev(exp_dir))
      except:
          print("Failed to estimate polar angle")
          polar_angle = 90
      print("Estimated polar angle:", polar_angle)
      gen_poses(exp_dir, polar_angle)
  
      # stage 1: generate another 4 views at a different elevation
      if polar_angle <= 75:
          output_ims_2 = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(4,8)), device=device, ddim_steps=ddim_steps, scale=scale)
      else:
          output_ims_2 = predict_stage1_gradio(model, input_im, save_path=stage1_dir, adjust_set=list(range(8,12)), device=device, ddim_steps=ddim_steps, scale=scale)
      torch.cuda.empty_cache()
      return 90-polar_angle, output_ims+output_ims_2
      
  def stage2_run(model, device, exp_dir,
                 elev, scale, stage2_steps=50):
      # stage 2 for the remaining 7 images, generate 7*4=28 views
      if 90-elev <= 75:
          zero123_infer(model, exp_dir, indices=list(range(1,8)), device=device, ddim_steps=stage2_steps, scale=scale)
      else:
          zero123_infer(model, exp_dir, indices=list(range(1,4))+list(range(8,12)), device=device, ddim_steps=stage2_steps, scale=scale)
  
  def reconstruct(exp_dir, output_format=".ply", device_idx=0, resolution=256):
      exp_dir = os.path.abspath(exp_dir)
      main_dir_path = os.path.abspath(os.path.dirname("./"))
      os.chdir('reconstruction/')
  
      bash_script = f'CUDA_VISIBLE_DEVICES={device_idx} python exp_runner_generic_blender_val.py \
                      --specific_dataset_name {exp_dir} \
                      --mode export_mesh \
                      --conf confs/one2345_lod0_val_demo.conf \
                      --resolution {resolution}'
      print(bash_script)
      os.system(bash_script)
      os.chdir(main_dir_path)
  
      ply_path = os.path.join(exp_dir, f"mesh.ply")
      if output_format == ".ply":
          return ply_path
      if output_format not in [".obj", ".glb"]:
          print("Invalid output format, must be one of .ply, .obj, .glb")
          return ply_path
      return convert_mesh_format(exp_dir, output_format=output_format)
  
  
  def predict_multiview(shape_dir, args):
      device = f"cuda:{args.gpu_idx}"
  
      # initialize the zero123 model
      models = init_model(device, 'zero123-xl.ckpt', half_precision=args.half_precision)
      model_zero123 = models["turncam"]
  
      # initialize the Segment Anything model
      predictor = sam_init(args.gpu_idx)
      input_raw = Image.open(args.img_path)
  
      # preprocess the input image
      input_256 = preprocess(predictor, input_raw)
  
      # generate multi-view images in two stages with Zero123.
      # first stage: generate N=8 views cover 360 degree of the input shape.
      elev, stage1_imgs = stage1_run(model_zero123, device, shape_dir, input_256, scale=3, ddim_steps=75)
      # second stage: 4 local views for each of the first-stage view, resulting in N*4=32 source view images.
      stage2_run(model_zero123, device, shape_dir, elev, scale=3, stage2_steps=50)
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser(description='Process some integers.')
      parser.add_argument('--img_path', type=str, default="./demo/demo_examples/01_wild_hydrant.png", help='Path to the input image')
      parser.add_argument('--gpu_idx', type=int, default=0, help='GPU index')
      parser.add_argument('--half_precision', action='store_true', help='Use half precision')
      parser.add_argument('--mesh_resolution', type=int, default=256, help='Mesh resolution')
      parser.add_argument('--output_format', type=str, default=".ply", help='Output format: .ply, .obj, .glb')
  
      args = parser.parse_args()
  
      assert(torch.cuda.is_available())
  
      shape_id = args.img_path.split('/')[-1].split('.')[0]
      shape_dir = f"./exp/{shape_id}"
      os.makedirs(shape_dir, exist_ok=True)
  
      predict_multiview(shape_dir, args)
  
      # utilize cost volume-based 3D reconstruction to generate textured 3D mesh
      mesh_path = reconstruct(shape_dir, output_format=args.output_format, device_idx=args.gpu_idx, resolution=args.mesh_resolution)
      print("Mesh saved to:", mesh_path)
  