Update scripts/utils.py

scripts/utils.py

@@ -1,19 +1,19 @@
 import torch
 import numpy as np
 from PIL import Image
-import pymeshlab
 import pymeshlab as ml
-from pymeshlab import PercentageValue
 from pytorch3d.renderer import TexturesVertex
 from pytorch3d.structures import Meshes
 from rembg import new_session, remove
-import torch
-import torch.nn.functional as F
-from typing import List, Tuple
-from PIL import Image
 import trimesh
+from typing import List, Tuple
+import torch.nn.functional as F
+
+# Constants
+NEG_PROMPT = "sketch, sculpture, hand drawing, outline, single color, NSFW, lowres, bad anatomy, bad hands, text, error, missing fingers, yellow sleeves, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, (worst quality:1.4), (low quality:1.4)"
 
-providers = [
+# CUDA Configuration
+CUDA_PROVIDERS = [
  ('CUDAExecutionProvider', {
  'device_id': 0,
  'arena_extend_strategy': 'kSameAsRequested',
@@ -22,298 +22,222 @@ providers = [
  })
 ]
 
-session = new_session(providers=providers)
-
-NEG_PROMPT="sketch, sculpture, hand drawing, outline, single color, NSFW, lowres, bad anatomy,bad hands, text, error, missing fingers, yellow sleeves, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry,(worst quality:1.4),(low quality:1.4)"
+# Initialize rembg session
+rembg_session = new_session(providers=CUDA_PROVIDERS)
 
+# Mesh Loading and Conversion Functions
 def load_mesh_with_trimesh(file_name, file_type=None):
- import trimesh
- mesh: trimesh.Trimesh = trimesh.load(file_name, file_type=file_type)
+ mesh = trimesh.load(file_name, file_type=file_type)
  if isinstance(mesh, trimesh.Scene):
- assert len(mesh.geometry) > 0
- # save to obj first and load again to avoid offset issue
- from io import BytesIO
- with BytesIO() as f:
- mesh.export(f, file_type="obj")
- f.seek(0)
- mesh = trimesh.load(f, file_type="obj")
- if isinstance(mesh, trimesh.Scene):
- # we lose texture information here
- mesh = trimesh.util.concatenate(
- tuple(trimesh.Trimesh(vertices=g.vertices, faces=g.faces)
- for g in mesh.geometry.values()))
- assert isinstance(mesh, trimesh.Trimesh)
-
+ mesh = _process_trimesh_scene(mesh)
+ 
  vertices = torch.from_numpy(mesh.vertices).T
  faces = torch.from_numpy(mesh.faces).T
- colors = None
- if mesh.visual is not None:
- if hasattr(mesh.visual, 'vertex_colors'):
- colors = torch.from_numpy(mesh.visual.vertex_colors)[..., :3].T / 255.
- if colors is None:
- # print("Warning: no vertex color found in mesh! Filling it with gray.")
- colors = torch.ones_like(vertices) * 0.5
+ colors = _get_mesh_colors(mesh)
+ 
  return vertices, faces, colors
 
-def meshlab_mesh_to_py3dmesh(mesh: pymeshlab.Mesh) -> Meshes:
+def _process_trimesh_scene(mesh):
+ from io import BytesIO
+ with BytesIO() as f:
+ mesh.export(f, file_type="obj")
+ f.seek(0)
+ mesh = trimesh.load(f, file_type="obj")
+ if isinstance(mesh, trimesh.Scene):
+ mesh = trimesh.util.concatenate(
+ tuple(trimesh.Trimesh(vertices=g.vertices, faces=g.faces)
+ for g in mesh.geometry.values()))
+ return mesh
+
+def _get_mesh_colors(mesh):
+ if mesh.visual is not None and hasattr(mesh.visual, 'vertex_colors'):
+ return torch.from_numpy(mesh.visual.vertex_colors)[..., :3].T / 255.
+ return torch.ones_like(mesh.vertices.T) * 0.5
+
+# Mesh Conversion Functions
+def meshlab_mesh_to_py3dmesh(mesh: ml.Mesh) -> Meshes:
  verts = torch.from_numpy(mesh.vertex_matrix()).float()
  faces = torch.from_numpy(mesh.face_matrix()).long()
  colors = torch.from_numpy(mesh.vertex_color_matrix()[..., :3]).float()
  textures = TexturesVertex(verts_features=[colors])
  return Meshes(verts=[verts], faces=[faces], textures=textures)
 
-
-def py3dmesh_to_meshlab_mesh(meshes: Meshes) -> pymeshlab.Mesh:
+def py3dmesh_to_meshlab_mesh(meshes: Meshes) -> ml.Mesh:
  colors_in = F.pad(meshes.textures.verts_features_packed().cpu().float(), [0,1], value=1).numpy().astype(np.float64)
- m1 = pymeshlab.Mesh(
+ return ml.Mesh(
  vertex_matrix=meshes.verts_packed().cpu().float().numpy().astype(np.float64),
  face_matrix=meshes.faces_packed().cpu().long().numpy().astype(np.int32),
  v_normals_matrix=meshes.verts_normals_packed().cpu().float().numpy().astype(np.float64),
  v_color_matrix=colors_in)
- return m1
-
-
-def to_pyml_mesh(vertices,faces):
- m1 = pymeshlab.Mesh(
- vertex_matrix=vertices.cpu().float().numpy().astype(np.float64),
- face_matrix=faces.cpu().long().numpy().astype(np.int32),
- )
- return m1
-
-
-def to_py3d_mesh(vertices, faces, normals=None):
- from pytorch3d.structures import Meshes
- from pytorch3d.renderer.mesh.textures import TexturesVertex
- mesh = Meshes(verts=[vertices], faces=[faces], textures=None)
- if normals is None:
- normals = mesh.verts_normals_packed()
- # set normals as vertext colors
- mesh.textures = TexturesVertex(verts_features=[normals / 2 + 0.5])
- return mesh
-
-
-def from_py3d_mesh(mesh):
- return mesh.verts_list()[0], mesh.faces_list()[0], mesh.textures.verts_features_packed()
 
+# Normal Map Rotation Functions
 def rotate_normalmap_by_angle(normal_map: np.ndarray, angle: float):
- """
- rotate along y-axis
- normal_map: np.array, shape=(H, W, 3) in [-1, 1]
- angle: float, in degree
- """
- angle = angle / 180 * np.pi
- R = np.array([[np.cos(angle), 0, np.sin(angle)], [0, 1, 0], [-np.sin(angle), 0, np.cos(angle)]])
+ angle_rad = np.radians(angle)
+ R = np.array([
+ [np.cos(angle_rad), 0, np.sin(angle_rad)],
+ [0, 1, 0],
+ [-np.sin(angle_rad), 0, np.cos(angle_rad)]
+ ])
  return np.dot(normal_map.reshape(-1, 3), R.T).reshape(normal_map.shape)
 
-# from view coord to front view world coord
-def rotate_normals(normal_pils, return_types='np', rotate_direction=1) -> np.ndarray: # [0, 255]
+def rotate_normals(normal_pils, return_types='np', rotate_direction=1):
  n_views = len(normal_pils)
  ret = []
  for idx, rgba_normal in enumerate(normal_pils):
- # rotate normal
- normal_np = np.array(rgba_normal)[:, :, :3] / 255 # in [-1, 1]
- alpha_np = np.array(rgba_normal)[:, :, 3] / 255 # in [0, 1]
- normal_np = normal_np * 2 - 1
- normal_np = rotate_normalmap_by_angle(normal_np, rotate_direction * idx * (360 / n_views))
- normal_np = (normal_np + 1) / 2
- normal_np = normal_np * alpha_np[..., None] # make bg black
- rgba_normal_np = np.concatenate([normal_np * 255, alpha_np[:, :, None] * 255] , axis=-1)
- if return_types == 'np':
- ret.append(rgba_normal_np)
- elif return_types == 'pil':
- ret.append(Image.fromarray(rgba_normal_np.astype(np.uint8)))
- else:
- raise ValueError(f"return_types should be 'np' or 'pil', but got {return_types}")
+ normal_np = _process_normal_map(rgba_normal, idx, n_views, rotate_direction)
+ ret.append(_format_output(normal_np, return_types))
  return ret
 
+def _process_normal_map(rgba_normal, idx, n_views, rotate_direction):
+ normal_np = np.array(rgba_normal)[:, :, :3] / 255 * 2 - 1
+ alpha_np = np.array(rgba_normal)[:, :, 3] / 255
+ normal_np = rotate_normalmap_by_angle(normal_np, rotate_direction * idx * (360 / n_views))
+ normal_np = (normal_np + 1) / 2 * alpha_np[..., None]
+ return np.concatenate([normal_np * 255, alpha_np[:, :, None] * 255], axis=-1)
+
+def _format_output(normal_np, return_types):
+ if return_types == 'np':
+ return normal_np
+ elif return_types == 'pil':
+ return Image.fromarray(normal_np.astype(np.uint8))
+ else:
+ raise ValueError(f"return_types should be 'np' or 'pil', but got {return_types}")
 
-def rotate_normalmap_by_angle_torch(normal_map, angle):
- """
- rotate along y-axis
- normal_map: torch.Tensor, shape=(H, W, 3) in [-1, 1], device='cuda'
- angle: float, in degree
- """
- angle = torch.tensor(angle / 180 * np.pi).to(normal_map)
- R = torch.tensor([[torch.cos(angle), 0, torch.sin(angle)], 
- [0, 1, 0], 
- [-torch.sin(angle), 0, torch.cos(angle)]]).to(normal_map)
- return torch.matmul(normal_map.view(-1, 3), R.T).view(normal_map.shape)
-
-def do_rotate(rgba_normal, angle):
- rgba_normal = torch.from_numpy(rgba_normal).float().cuda() / 255
- rotated_normal_tensor = rotate_normalmap_by_angle_torch(rgba_normal[..., :3] * 2 - 1, angle)
- rotated_normal_tensor = (rotated_normal_tensor + 1) / 2
- rotated_normal_tensor = rotated_normal_tensor * rgba_normal[:, :, [3]] # make bg black
- rgba_normal_np = torch.cat([rotated_normal_tensor * 255, rgba_normal[:, :, [3]] * 255], dim=-1).cpu().numpy()
- return rgba_normal_np
-
-def rotate_normals_torch(normal_pils, return_types='np', rotate_direction=1):
- n_views = len(normal_pils)
- ret = []
- for idx, rgba_normal in enumerate(normal_pils):
- # rotate normal
- angle = rotate_direction * idx * (360 / n_views)
- rgba_normal_np = do_rotate(np.array(rgba_normal), angle)
- if return_types == 'np':
- ret.append(rgba_normal_np)
- elif return_types == 'pil':
- ret.append(Image.fromarray(rgba_normal_np.astype(np.uint8)))
- else:
- raise ValueError(f"return_types should be 'np' or 'pil', but got {return_types}")
- return ret
-
+# Background Change Functions
 def change_bkgd(img_pils, new_bkgd=(0., 0., 0.)):
- ret = []
  new_bkgd = np.array(new_bkgd).reshape(1, 1, 3)
- for rgba_img in img_pils:
- img_np = np.array(rgba_img)[:, :, :3] / 255
- alpha_np = np.array(rgba_img)[:, :, 3] / 255
- ori_bkgd = img_np[:1, :1]
- # color = ori_color * alpha + bkgd * (1-alpha)
- # ori_color = (color - bkgd * (1-alpha)) / alpha
- alpha_np_clamp = np.clip(alpha_np, 1e-6, 1) # avoid divide by zero
- ori_img_np = (img_np - ori_bkgd * (1 - alpha_np[..., None])) / alpha_np_clamp[..., None]
- img_np = np.where(alpha_np[..., None] > 0.05, ori_img_np * alpha_np[..., None] + new_bkgd * (1 - alpha_np[..., None]), new_bkgd)
- rgba_img_np = np.concatenate([img_np * 255, alpha_np[..., None] * 255], axis=-1)
- ret.append(Image.fromarray(rgba_img_np.astype(np.uint8)))
- return ret
-
-def change_bkgd_to_normal(normal_pils) -> List[Image.Image]:
- n_views = len(normal_pils)
- ret = []
- for idx, rgba_normal in enumerate(normal_pils):
- # calcuate background normal
- target_bkgd = rotate_normalmap_by_angle(np.array([[[0., 0., 1.]]]), idx * (360 / n_views))
- normal_np = np.array(rgba_normal)[:, :, :3] / 255 # in [-1, 1]
- alpha_np = np.array(rgba_normal)[:, :, 3] / 255 # in [0, 1]
- normal_np = normal_np * 2 - 1
- old_bkgd = normal_np[:1,:1]
- normal_np[alpha_np > 0.05] = (normal_np[alpha_np > 0.05] - old_bkgd * (1 - alpha_np[alpha_np > 0.05][..., None])) / alpha_np[alpha_np > 0.05][..., None]
- normal_np = normal_np * alpha_np[..., None] + target_bkgd * (1 - alpha_np[..., None])
- normal_np = (normal_np + 1) / 2
- rgba_normal_np = np.concatenate([normal_np * 255, alpha_np[..., None] * 255] , axis=-1)
- ret.append(Image.fromarray(rgba_normal_np.astype(np.uint8)))
- return ret
-
-
-def fix_vert_color_glb(mesh_path):
- from pygltflib import GLTF2, Material, PbrMetallicRoughness
- obj1 = GLTF2().load(mesh_path)
- obj1.meshes[0].primitives[0].material = 0
- obj1.materials.append(Material(
- pbrMetallicRoughness = PbrMetallicRoughness(
- baseColorFactor = [1.0, 1.0, 1.0, 1.0],
- metallicFactor = 0.,
- roughnessFactor = 1.0,
- ),
- emissiveFactor = [0.0, 0.0, 0.0],
- doubleSided = True,
- ))
- obj1.save(mesh_path)
+ return [_process_image(rgba_img, new_bkgd) for rgba_img in img_pils]
+
+def _process_image(rgba_img, new_bkgd):
+ img_np = np.array(rgba_img)[:, :, :3] / 255
+ alpha_np = np.array(rgba_img)[:, :, 3] / 255
+ ori_bkgd = img_np[:1, :1]
+ alpha_np_clamp = np.clip(alpha_np, 1e-6, 1)
+ ori_img_np = (img_np - ori_bkgd * (1 - alpha_np[..., None])) / alpha_np_clamp[..., None]
+ img_np = np.where(alpha_np[..., None] > 0.05, ori_img_np * alpha_np[..., None] + new_bkgd * (1 - alpha_np[..., None]), new_bkgd)
+ rgba_img_np = np.concatenate([img_np * 255, alpha_np[..., None] * 255], axis=-1)
+ return Image.fromarray(rgba_img_np.astype(np.uint8))
+
+# Mesh Cleaning Function
+def simple_clean_mesh(pyml_mesh: ml.Mesh, apply_smooth=True, stepsmoothnum=1, apply_sub_divide=False, sub_divide_threshold=0.25):
+ ms = ml.MeshSet()
+ ms.add_mesh(pyml_mesh, "cube_mesh")
+ 
+ if apply_smooth:
+ ms.apply_filter("apply_coord_laplacian_smoothing", stepsmoothnum=stepsmoothnum, cotangentweight=False)
+ if apply_sub_divide:
+ ms.apply_filter("meshing_repair_non_manifold_vertices")
+ ms.apply_filter("meshing_repair_non_manifold_edges", method='Remove Faces')
+ ms.apply_filter("meshing_surface_subdivision_loop", iterations=2, threshold=ml.PercentageValue(sub_divide_threshold))
+ return meshlab_mesh_to_py3dmesh(ms.current_mesh())
 
+# Image Processing Functions
+def expand2square(pil_img, background_color):
+ width, height = pil_img.size
+ if width == height:
+ return pil_img
+ new_size = max(width, height)
+ result = Image.new(pil_img.mode, (new_size, new_size), background_color)
+ offset = ((new_size - width) // 2, (new_size - height) // 2)
+ result.paste(pil_img, offset)
+ return result
 
-def srgb_to_linear(c_srgb):
- c_linear = np.where(c_srgb <= 0.04045, c_srgb / 12.92, ((c_srgb + 0.055) / 1.055) ** 2.4)
- return c_linear.clip(0, 1.)
+def simple_preprocess(input_image, rembg_session=rembg_session, background_color=255):
+ RES = 2048
+ input_image.thumbnail([RES, RES], Image.Resampling.LANCZOS)
+ if input_image.mode != 'RGBA':
+ image_rem = input_image.convert('RGBA')
+ input_image = remove(image_rem, alpha_matting=False, session=rembg_session)
 
+ arr = np.asarray(input_image)
+ alpha = arr[:, :, -1]
+ x_nonzero, y_nonzero = np.nonzero(alpha > 60)
+ x_min, x_max = x_nonzero.min(), x_nonzero.max()
+ y_min, y_max = y_nonzero.min(), y_nonzero.max()
+ arr = arr[x_min:x_max+1, y_min:y_max+1]
+ input_image = Image.fromarray(arr)
+ return expand2square(input_image, (background_color, background_color, background_color, 0))
 
+# Mesh Saving Functions
 def save_py3dmesh_with_trimesh_fast(meshes: Meshes, save_glb_path, apply_sRGB_to_LinearRGB=True):
- # convert from pytorch3d meshes to trimesh mesh
  vertices = meshes.verts_packed().cpu().float().numpy()
  triangles = meshes.faces_packed().cpu().long().numpy()
  np_color = meshes.textures.verts_features_packed().cpu().float().numpy()
+ 
  if save_glb_path.endswith(".glb"):
- # rotate 180 along +Y
  vertices[:, [0, 2]] = -vertices[:, [0, 2]]
 
  if apply_sRGB_to_LinearRGB:
  np_color = srgb_to_linear(np_color)
- assert vertices.shape[0] == np_color.shape[0]
- assert np_color.shape[1] == 3
- assert 0 <= np_color.min() and np_color.max() <= 1, f"min={np_color.min()}, max={np_color.max()}"
+ 
  mesh = trimesh.Trimesh(vertices=vertices, faces=triangles, vertex_colors=np_color)
  mesh.remove_unreferenced_vertices()
- # save mesh
  mesh.export(save_glb_path)
+ 
  if save_glb_path.endswith(".glb"):
  fix_vert_color_glb(save_glb_path)
- print(f"saving to {save_glb_path}")
-
+ print(f"Saved to {save_glb_path}")
 
-def save_glb_and_video(save_mesh_prefix: str, meshes: Meshes, with_timestamp=True, dist=3.5, azim_offset=180, resolution=512, fov_in_degrees=1 / 1.15, cam_type="ortho", view_padding=60, export_video=True) -> Tuple[str, str]:
+def save_glb_and_video(save_mesh_prefix: str, meshes: Meshes, with_timestamp=True, **kwargs) -> Tuple[str, str]:
  import time
  if '.' in save_mesh_prefix:
  save_mesh_prefix = ".".join(save_mesh_prefix.split('.')[:-1])
  if with_timestamp:
  save_mesh_prefix = save_mesh_prefix + f"_{int(time.time())}"
  ret_mesh = save_mesh_prefix + ".glb"
- # optimizied version
  save_py3dmesh_with_trimesh_fast(meshes, ret_mesh)
  return ret_mesh, None
 
+# Utility Functions
+def srgb_to_linear(c_srgb):
+ return np.where(c_srgb <= 0.04045, c_srgb / 12.92, ((c_srgb + 0.055) / 1.055) ** 2.4).clip(0, 1.)
 
-def simple_clean_mesh(pyml_mesh: ml.Mesh, apply_smooth=True, stepsmoothnum=1, apply_sub_divide=False, sub_divide_threshold=0.25):
- ms = ml.MeshSet()
- ms.add_mesh(pyml_mesh, "cube_mesh")
- 
- if apply_smooth:
- ms.apply_filter("apply_coord_laplacian_smoothing", stepsmoothnum=stepsmoothnum, cotangentweight=False)
- if apply_sub_divide: # 5s, slow
- ms.apply_filter("meshing_repair_non_manifold_vertices")
- ms.apply_filter("meshing_repair_non_manifold_edges", method='Remove Faces')
- ms.apply_filter("meshing_surface_subdivision_loop", iterations=2, threshold=PercentageValue(sub_divide_threshold))
- return meshlab_mesh_to_py3dmesh(ms.current_mesh())
-
-
-def expand2square(pil_img, background_color):
- width, height = pil_img.size
- if width == height:
- return pil_img
- elif width > height:
- result = Image.new(pil_img.mode, (width, width), background_color)
- result.paste(pil_img, (0, (width - height) // 2))
- return result
- else:
- result = Image.new(pil_img.mode, (height, height), background_color)
- result.paste(pil_img, ((height - width) // 2, 0))
- return result
-
-
-def simple_preprocess(input_image, rembg_session=session, background_color=255):
- RES = 2048
- input_image.thumbnail([RES, RES], Image.Resampling.LANCZOS)
- if input_image.mode != 'RGBA':
- image_rem = input_image.convert('RGBA')
- input_image = remove(image_rem, alpha_matting=False, session=rembg_session)
-
- arr = np.asarray(input_image)
- alpha = np.asarray(input_image)[:, :, -1]
- x_nonzero = np.nonzero((alpha > 60).sum(axis=1))
- y_nonzero = np.nonzero((alpha > 60).sum(axis=0))
- x_min = int(x_nonzero[0].min())
- y_min = int(y_nonzero[0].min())
- x_max = int(x_nonzero[0].max())
- y_max = int(y_nonzero[0].max())
- arr = arr[x_min: x_max, y_min: y_max]
- input_image = Image.fromarray(arr)
- input_image = expand2square(input_image, (background_color, background_color, background_color, 0))
- return input_image
+def fix_vert_color_glb(mesh_path):
+ from pygltflib import GLTF2, Material, PbrMetallicRoughness
+ obj1 = GLTF2().load(mesh_path)
+ obj1.meshes[0].primitives[0].material = 0
+ obj1.materials.append(Material(
+ pbrMetallicRoughness = PbrMetallicRoughness(
+ baseColorFactor = [1.0, 1.0, 1.0, 1.0],
+ metallicFactor = 0.,
+ roughnessFactor = 1.0,
+ ),
+ emissiveFactor = [0.0, 0.0, 0.0],
+ doubleSided = True,
+ ))
+ obj1.save(mesh_path)
 
 def init_target(img_pils, new_bkgd=(0., 0., 0.), device="cuda"):
- # Convert the background color to a PyTorch tensor
  new_bkgd = torch.tensor(new_bkgd, dtype=torch.float32).view(1, 1, 3).to(device)
- 
- # Convert all images to PyTorch tensors and process them
  imgs = torch.stack([torch.from_numpy(np.array(img, dtype=np.float32)) for img in img_pils]).to(device) / 255
- img_nps = imgs[..., :3]
- alpha_nps = imgs[..., 3]
+ img_nps, alpha_nps = imgs[..., :3], imgs[..., 3]
  ori_bkgds = img_nps[:, :1, :1]
  
- # Avoid divide by zero and calculate the original image
  alpha_nps_clamp = torch.clamp(alpha_nps, 1e-6, 1)
  ori_img_nps = (img_nps - ori_bkgds * (1 - alpha_nps.unsqueeze(-1))) / alpha_nps_clamp.unsqueeze(-1)
  ori_img_nps = torch.clamp(ori_img_nps, 0, 1)
  img_nps = torch.where(alpha_nps.unsqueeze(-1) > 0.05, ori_img_nps * alpha_nps.unsqueeze(-1) + new_bkgd * (1 - alpha_nps.unsqueeze(-1)), new_bkgd)
 
- rgba_img_np = torch.cat([img_nps, alpha_nps.unsqueeze(-1)], dim=-1)
- return rgba_img_np
+ return torch.cat([img_nps, alpha_nps.unsqueeze(-1)], dim=-1)
+
+def save_obj_and_video(save_mesh_prefix: str, meshes: Meshes, with_timestamp=True, **kwargs) -> Tuple[str, str]:
+ if '.' in save_mesh_prefix:
+ save_mesh_prefix = ".".join(save_mesh_prefix.split('.')[:-1])
+ if with_timestamp:
+ save_mesh_prefix = save_mesh_prefix + f"_{int(time.time())}"
+ ret_mesh = save_mesh_prefix + ".obj"
+ 
+ vertices = meshes.verts_packed().cpu().float().numpy()
+ triangles = meshes.faces_packed().cpu().long().numpy()
+ np_color = meshes.textures.verts_features_packed().cpu().float().numpy()
+ 
+ # Apply sRGB to LinearRGB conversion
+ np_color = srgb_to_linear(np_color)
+ 
+ mesh = trimesh.Trimesh(vertices=vertices, faces=triangles, vertex_colors=np_color)
+ mesh.remove_unreferenced_vertices()
+ mesh.export(ret_mesh)
+ 
+ print(f"Saved to {ret_mesh}")
+ 
+ return ret_mesh, None