content: from tqdm import tqdm
  from PIL import Image
  import numpy as np
  import torch
  from typing import List
  import comfy.utils
  
  from .remesh import calc_vertex_normals
  from .opt import MeshOptimizer
  from .func import make_star_cameras_orthographic, make_star_cameras_orthographic_py3d
  from .render import NormalsRenderer, Pytorch3DNormalsRenderer
  from Unique3D.scripts.utils import to_py3d_mesh, init_target
  
  def reconstruct_stage1(pils: List[Image.Image], steps=100, vertices=None, faces=None, start_edge_len=0.1, end_edge_len=0.02, decay=0.995, return_mesh=False, loss_expansion_weight=0.1, gain=0.05):
      assert len(pils) == 4
      mv,proj = make_star_cameras_orthographic(4, 1)
      renderer = NormalsRenderer(mv,proj,list(pils[0].size))
      # cameras = make_star_cameras_orthographic_py3d([0, 270, 180, 90], device="cuda", focal=1., dist=4.0)
      # renderer = Pytorch3DNormalsRenderer(cameras, list(pils[0].size), device="cuda")
      
      target_images = init_target(pils, new_bkgd=(0., 0., 0.)) # 4s
      # 1. no rotate
      target_images = target_images[[0, 3, 2, 1]]
  
      # 2. init from coarse mesh
      opt = MeshOptimizer(vertices, faces, local_edgelen=False, gain=gain, edge_len_lims=(end_edge_len, start_edge_len))
  
      vertices = opt.vertices
  
      mask = target_images[..., -1] < 0.5
  
      comfy_pbar = comfy.utils.ProgressBar(steps)
  
      for i in tqdm(range(steps)):
          opt.zero_grad()
          opt._lr *= decay
          normals = calc_vertex_normals(vertices,faces)
          images = renderer.render(vertices,normals,faces)
          
          loss_expand = 0.5 * ((vertices+normals).detach() - vertices).pow(2).mean()
          
          t_mask = images[..., -1] > 0.5
          loss_target_l2 = (images[t_mask] - target_images[t_mask]).abs().pow(2).mean()
          loss_alpha_target_mask_l2 = (images[..., -1][mask] - target_images[..., -1][mask]).pow(2).mean()
          
          loss = loss_target_l2 + loss_alpha_target_mask_l2 + loss_expand * loss_expansion_weight
          
          # out of box
          loss_oob = (vertices.abs() > 0.99).float().mean() * 10
          loss = loss + loss_oob
          
          loss.backward()
          opt.step()
  
          vertices,faces = opt.remesh(poisson=False)
  
          comfy_pbar.update_absolute(i + 1)
  
      vertices, faces = vertices.detach(), faces.detach()
      
      if return_mesh:
          return to_py3d_mesh(vertices, faces)
      else:
          return vertices, faces
  