content: # Open Source Model Licensed under the Apache License Version 2.0 and Other Licenses of the Third-Party Components therein:
  # The below Model in this distribution may have been modified by THL A29 Limited ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
  
  # Copyright (C) 2024 THL A29 Limited, a Tencent company.  All rights reserved. 
  # The below software and/or models in this distribution may have been 
  # modified by THL A29 Limited ("Tencent Modifications"). 
  # All Tencent Modifications are Copyright (C) THL A29 Limited.
  
  # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT 
  # except for the third-party components listed below. 
  # Hunyuan 3D does not impose any additional limitations beyond what is outlined 
  # in the repsective licenses of these third-party components. 
  # Users must comply with all terms and conditions of original licenses of these third-party 
  # components and must ensure that the usage of the third party components adheres to 
  # all relevant laws and regulations. 
  
  # For avoidance of doubts, Hunyuan 3D means the large language models and 
  # their software and algorithms, including trained model weights, parameters (including 
  # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, 
  # fine-tuning enabling code and other elements of the foregoing made publicly available 
  # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
  
  import os
  import time
  import torch
  import random
  import numpy as np
  from PIL import Image
  from einops import rearrange
  from PIL import Image, ImageSequence
  
  from .utils import seed_everything, timing_decorator, auto_amp_inference
  from .utils import get_parameter_number, set_parameter_grad_false
  from Hunyuan3D_V1.svrm.predictor import MV23DPredictor
  
  
  class Views2Mesh():
      def __init__(self, mv23d_cfg_path, mv23d_ckt_path, device="cuda:0", use_lite=False):
          '''
              mv23d_cfg_path: config yaml file 
              mv23d_ckt_path: path to ckpt
              use_lite: 
          '''
          self.mv23d_predictor = MV23DPredictor(mv23d_ckt_path, mv23d_cfg_path, device=device)  
          self.mv23d_predictor.model.eval()
          self.order = [0, 1, 2, 3, 4, 5] if use_lite else [0, 2, 4, 5, 3, 1]
          set_parameter_grad_false(self.mv23d_predictor.model)
          print('view2mesh model', get_parameter_number(self.mv23d_predictor.model))
  
      @torch.no_grad()
      @timing_decorator("views to mesh")
      @auto_amp_inference
      def __call__(
          self,
          views_pil=None, 
          cond_pil=None, 
          gif_pil=None, 
          seed=0, 
          target_face_count = 90000,
          do_texture_mapping = True,
          save_folder='./outputs/test'
      ):
          '''
              can set views_pil, cond_pil simutaously or set gif_pil only
              seed: int
              target_face_count: int 
              save_folder: path to save mesh files
          '''
          save_dir = save_folder
          os.makedirs(save_dir, exist_ok=True)
  
          if views_pil is not None and cond_pil is not None:
              show_image = rearrange(np.asarray(views_pil, dtype=np.uint8), 
                                     '(n h) (m w) c -> (n m) h w c', n=3, m=2)
              views = [Image.fromarray(show_image[idx]) for idx in self.order] 
              image_list = [cond_pil]+ views
              image_list = [img.convert('RGB') for img in image_list]
          elif gif_pil is not None:
              image_list = [img.convert('RGB') for img in ImageSequence.Iterator(gif_pil)]
          
          image_input = image_list[0]
          image_list = image_list[1:] + image_list[:1]
          
          seed_everything(seed)
          vtx_refine, faces_refine, vtx_colors = self.mv23d_predictor.predict(
              image_list, 
              save_dir = save_dir, 
              image_input = image_input,
              target_face_count = target_face_count,
              do_texture_mapping = do_texture_mapping
          )
          torch.cuda.empty_cache()
          return vtx_refine, faces_refine, vtx_colors
          