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 math
  import time
  import torch
  import numpy as np
  from tqdm import tqdm
  from PIL import Image, ImageSequence
  from omegaconf import OmegaConf
  from torchvision import transforms
  from safetensors.torch import save_file, load_file
  from .ldm.util import instantiate_from_config
  from .ldm.vis_util import render
  
  class MV23DPredictor(object):
  	def __init__(self, ckpt_path, cfg_path, elevation=15, number_view=60, 
  				render_size=256, device="cuda:0") -> None:
  		self.device = device
  		self.elevation = elevation
  		self.number_view = number_view
  		self.render_size = render_size
  
  		self.elevation_list = [0, 0, 0, 0, 0, 0, 0]
  		self.azimuth_list = [0, 60, 120, 180, 240, 300, 0]
  
  		st = time.time()
  		self.model = self.init_model(ckpt_path, cfg_path)
  		print(f"=====> mv23d model init time: {time.time() - st}")
  
  		self.input_view_transform = transforms.Compose([
  			transforms.Resize(504, interpolation=Image.BICUBIC),
  			transforms.ToTensor(),
  		])
  		self.final_input_view_transform = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
  
  	def init_model(self, ckpt_path, cfg_path):
  		config = OmegaConf.load(cfg_path)
  		model = instantiate_from_config(config.model)
  
  		weights = load_file(ckpt_path)
  		model.load_state_dict(weights)
  
  		model.to(self.device)
  		model = model.eval()
  		model.render.half()
  		print(f'Load model successfully')
  		return model
  
  	def create_camera_to_world_matrix(self, elevation, azimuth, cam_dis=1.5):
  		# elevation azimuth are radians
  		# Convert elevation and azimuth angles to Cartesian coordinates on a unit sphere
  		x = np.cos(elevation) * np.cos(azimuth)
  		y = np.cos(elevation) * np.sin(azimuth)
  		z = np.sin(elevation)
  
  		# Calculate camera position, target, and up vectors
  		camera_pos = np.array([x, y, z]) * cam_dis
  		target = np.array([0, 0, 0])
  		up = np.array([0, 0, 1])
  
  		# Construct view matrix
  		forward = target - camera_pos
  		forward /= np.linalg.norm(forward)
  		right = np.cross(forward, up)
  		right /= np.linalg.norm(right)
  		new_up = np.cross(right, forward)
  		new_up /= np.linalg.norm(new_up)
  		cam2world = np.eye(4)
  		cam2world[:3, :3] = np.array([right, new_up, -forward]).T
  		cam2world[:3, 3] = camera_pos
  		return cam2world
  
  	def refine_mask(self, mask, k=16):
  		mask /= 255.0
  		boder_mask = (mask >= -math.pi / 2.0 / k + 0.5) & (mask <= math.pi / 2.0 / k + 0.5)
  		mask[boder_mask] = 0.5 * np.sin(k * (mask[boder_mask] - 0.5)) + 0.5
  		mask[mask < -math.pi / 2.0 / k + 0.5] = 0.0
  		mask[mask > math.pi / 2.0 / k + 0.5] = 1.0
  		return (mask * 255.0).astype(np.uint8)
  
  	def load_images_and_cameras(self, input_imgs, elevation_list, azimuth_list):
  		input_image_list = []
  		input_cam_list = []
  		for input_view_image, elevation, azimuth in zip(input_imgs, elevation_list, azimuth_list):
  			input_view_image = self.input_view_transform(input_view_image)  
  			input_image_list.append(input_view_image)
  
  			input_view_cam_pos = self.create_camera_to_world_matrix(np.radians(elevation), np.radians(azimuth))
  			input_view_cam_intrinsic = np.array([35. / 32, 35. /32, 0.5, 0.5])
  			input_view_cam = torch.from_numpy(
  				np.concatenate([input_view_cam_pos.reshape(-1), input_view_cam_intrinsic], 0)
  			).float()
  			input_cam_list.append(input_view_cam)
  
  		pixels_input = torch.stack(input_image_list, dim=0)
  		input_images = self.final_input_view_transform(pixels_input)
  		input_cams = torch.stack(input_cam_list, dim=0)
  		return input_images, input_cams
  
  	def load_data(self, intput_imgs):
  		assert (6+1) == len(intput_imgs)
  		
  		input_images, input_cams = self.load_images_and_cameras(intput_imgs, self.elevation_list, self.azimuth_list)
  		input_cams[-1, :] = 0 # for user input view
  		
  		data = {}
  		data["input_view"] = input_images.unsqueeze(0).to(self.device)    # 1 4 3 512 512
  		data["input_view_cam"] = input_cams.unsqueeze(0).to(self.device)  # 1 4 20
  		return data
  
  	@torch.no_grad()
  	def predict(
  		self, 
  		intput_imgs, 
  		save_dir = "outputs/", 
  		image_input = None,
  		target_face_count = 10000,
  		do_texture_mapping = True,
  	):
  		
  		with torch.amp.autocast('cuda'):
  			vtx_refine, faces_refine, vtx_colors = self.model.generate_mesh(
  				data = self.load_data(intput_imgs),
  				target_face_count = target_face_count,
  			)
  			
  			#self.model.export_mesh_with_uv(
  			#	vtx_refine, faces_refine, vtx_colors,
  			#	do_texture_mapping = do_texture_mapping,
  			#	out_dir = save_dir,
  			#)
  		
  		return vtx_refine, faces_refine, vtx_colors
  