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import torch
import boto3
import json
from os import path as osp
# from botocore.config import Config
# from botocore.exceptions import ClientError
import h5py
import io
import numpy as np
import skimage
import trimesh
import os
from scipy.spatial import KDTree
import gc
from plyfile import PlyData
## For remeshing
import mesh2sdf
import tetgen
import vtk
import math
import tempfile
### For mesh processing
import pymeshlab
from partfield.utils import *
#########################
## To handle quad inputs
#########################
def quad_to_triangle_mesh(F):
"""
Converts a quad-dominant mesh into a pure triangle mesh by splitting quads into two triangles.
Parameters:
quad_mesh (trimesh.Trimesh): Input mesh with quad faces.
Returns:
trimesh.Trimesh: A new mesh with only triangle faces.
"""
faces = F
### If already a triangle mesh -- skip
if len(faces[0]) == 3:
return F
new_faces = []
for face in faces:
if len(face) == 4: # Quad face
# Split into two triangles
new_faces.append([face[0], face[1], face[2]]) # Triangle 1
new_faces.append([face[0], face[2], face[3]]) # Triangle 2
else:
print(f"Warning: Skipping non-triangle/non-quad face {face}")
new_faces = np.array(new_faces)
return new_faces
#########################
class Demo_Dataset(torch.utils.data.Dataset):
def __init__(self, cfg):
super().__init__()
self.data_path = cfg.dataset.data_path
self.is_pc = cfg.is_pc
all_files = os.listdir(self.data_path)
selected = []
for f in all_files:
if ".ply" in f and self.is_pc:
selected.append(f)
elif (".obj" in f or ".glb" in f or ".off" in f) and not self.is_pc:
selected.append(f)
self.data_list = selected
self.pc_num_pts = 100000
self.preprocess_mesh = cfg.preprocess_mesh
self.result_name = cfg.result_name
print("val dataset len:", len(self.data_list))
def __len__(self):
return len(self.data_list)
def load_ply_to_numpy(self, filename):
"""
Load a PLY file and extract the point cloud as a (N, 3) NumPy array.
Parameters:
filename (str): Path to the PLY file.
Returns:
numpy.ndarray: Point cloud array of shape (N, 3).
"""
ply_data = PlyData.read(filename)
# Extract vertex data
vertex_data = ply_data["vertex"]
# Convert to NumPy array (x, y, z)
points = np.vstack([vertex_data["x"], vertex_data["y"], vertex_data["z"]]).T
return points
def get_model(self, ply_file):
uid = ply_file.split(".")[-2].replace("/", "_")
####
if self.is_pc:
ply_file_read = os.path.join(self.data_path, ply_file)
pc = self.load_ply_to_numpy(ply_file_read)
bbmin = pc.min(0)
bbmax = pc.max(0)
center = (bbmin + bbmax) * 0.5
scale = 2.0 * 0.9 / (bbmax - bbmin).max()
pc = (pc - center) * scale
else:
obj_path = os.path.join(self.data_path, ply_file)
mesh = load_mesh_util(obj_path)
vertices = mesh.vertices
faces = mesh.faces
bbmin = vertices.min(0)
bbmax = vertices.max(0)
center = (bbmin + bbmax) * 0.5
scale = 2.0 * 0.9 / (bbmax - bbmin).max()
vertices = (vertices - center) * scale
mesh.vertices = vertices
### Make sure it is a triangle mesh -- just convert the quad
mesh.faces = quad_to_triangle_mesh(faces)
print("before preprocessing...")
print(mesh.vertices.shape)
print(mesh.faces.shape)
print()
### Pre-process mesh
if self.preprocess_mesh:
# Create a PyMeshLab mesh directly from vertices and faces
ml_mesh = pymeshlab.Mesh(vertex_matrix=mesh.vertices, face_matrix=mesh.faces)
# Create a MeshSet and add your mesh
ms = pymeshlab.MeshSet()
ms.add_mesh(ml_mesh, "from_trimesh")
# Apply filters
ms.apply_filter('meshing_remove_duplicate_faces')
ms.apply_filter('meshing_remove_duplicate_vertices')
percentageMerge = pymeshlab.PercentageValue(0.5)
ms.apply_filter('meshing_merge_close_vertices', threshold=percentageMerge)
ms.apply_filter('meshing_remove_unreferenced_vertices')
# Save or extract mesh
processed = ms.current_mesh()
mesh.vertices = processed.vertex_matrix()
mesh.faces = processed.face_matrix()
print("after preprocessing...")
print(mesh.vertices.shape)
print(mesh.faces.shape)
### Save input
save_dir = f"exp_results/{self.result_name}"
os.makedirs(save_dir, exist_ok=True)
view_id = 0
mesh.export(f'{save_dir}/input_{uid}_{view_id}.ply')
pc, _ = trimesh.sample.sample_surface(mesh, self.pc_num_pts)
result = {
'uid': uid
}
result['pc'] = torch.tensor(pc, dtype=torch.float32)
if not self.is_pc:
result['vertices'] = mesh.vertices
result['faces'] = mesh.faces
return result
def __getitem__(self, index):
gc.collect()
return self.get_model(self.data_list[index])
##############
###############################
class Demo_Remesh_Dataset(torch.utils.data.Dataset):
def __init__(self, cfg):
super().__init__()
self.data_path = cfg.dataset.data_path
all_files = os.listdir(self.data_path)
selected = []
for f in all_files:
if (".obj" in f or ".glb" in f):
selected.append(f)
self.data_list = selected
self.pc_num_pts = 100000
self.preprocess_mesh = cfg.preprocess_mesh
self.result_name = cfg.result_name
print("val dataset len:", len(self.data_list))
def __len__(self):
return len(self.data_list)
def get_model(self, ply_file):
uid = ply_file.split(".")[-2]
####
obj_path = os.path.join(self.data_path, ply_file)
mesh = load_mesh_util(obj_path)
vertices = mesh.vertices
faces = mesh.faces
bbmin = vertices.min(0)
bbmax = vertices.max(0)
center = (bbmin + bbmax) * 0.5
scale = 2.0 * 0.9 / (bbmax - bbmin).max()
vertices = (vertices - center) * scale
mesh.vertices = vertices
### Pre-process mesh
if self.preprocess_mesh:
# Create a PyMeshLab mesh directly from vertices and faces
ml_mesh = pymeshlab.Mesh(vertex_matrix=mesh.vertices, face_matrix=mesh.faces)
# Create a MeshSet and add your mesh
ms = pymeshlab.MeshSet()
ms.add_mesh(ml_mesh, "from_trimesh")
# Apply filters
ms.apply_filter('meshing_remove_duplicate_faces')
ms.apply_filter('meshing_remove_duplicate_vertices')
percentageMerge = pymeshlab.PercentageValue(0.5)
ms.apply_filter('meshing_merge_close_vertices', threshold=percentageMerge)
ms.apply_filter('meshing_remove_unreferenced_vertices')
# Save or extract mesh
processed = ms.current_mesh()
mesh.vertices = processed.vertex_matrix()
mesh.faces = processed.face_matrix()
print("after preprocessing...")
print(mesh.vertices.shape)
print(mesh.faces.shape)
### Save input
save_dir = f"exp_results/{self.result_name}"
os.makedirs(save_dir, exist_ok=True)
view_id = 0
mesh.export(f'{save_dir}/input_{uid}_{view_id}.ply')
try:
###### Remesh ######
size= 256
level = 2 / size
sdf = mesh2sdf.core.compute(mesh.vertices, mesh.faces, size)
# NOTE: the negative value is not reliable if the mesh is not watertight
udf = np.abs(sdf)
vertices, faces, _, _ = skimage.measure.marching_cubes(udf, level)
#### Only use SDF mesh ###
# new_mesh = trimesh.Trimesh(vertices, faces)
##########################
#### Make tet #####
components = trimesh.Trimesh(vertices, faces).split(only_watertight=False)
new_mesh = [] #trimesh.Trimesh()
if len(components) > 100000:
raise NotImplementedError
for i, c in enumerate(components):
c.fix_normals()
new_mesh.append(c) #trimesh.util.concatenate(new_mesh, c)
new_mesh = trimesh.util.concatenate(new_mesh)
# generate tet mesh
tet = tetgen.TetGen(new_mesh.vertices, new_mesh.faces)
tet.tetrahedralize(plc=True, nobisect=1., quality=True, fixedvolume=True, maxvolume=math.sqrt(2) / 12 * (2 / size) ** 3)
tmp_vtk = tempfile.NamedTemporaryFile(suffix='.vtk', delete=True)
tet.grid.save(tmp_vtk.name)
# extract surface mesh from tet mesh
reader = vtk.vtkUnstructuredGridReader()
reader.SetFileName(tmp_vtk.name)
reader.Update()
surface_filter = vtk.vtkDataSetSurfaceFilter()
surface_filter.SetInputConnection(reader.GetOutputPort())
surface_filter.Update()
polydata = surface_filter.GetOutput()
writer = vtk.vtkOBJWriter()
tmp_obj = tempfile.NamedTemporaryFile(suffix='.obj', delete=True)
writer.SetFileName(tmp_obj.name)
writer.SetInputData(polydata)
writer.Update()
new_mesh = load_mesh_util(tmp_obj.name)
##########################
new_mesh.vertices = new_mesh.vertices * (2.0 / size) - 1.0 # normalize it to [-1, 1]
mesh = new_mesh
####################
except:
print("Error in tet.")
mesh = mesh
pc, _ = trimesh.sample.sample_surface(mesh, self.pc_num_pts)
result = {
'uid': uid
}
result['pc'] = torch.tensor(pc, dtype=torch.float32)
result['vertices'] = mesh.vertices
result['faces'] = mesh.faces
return result
def __getitem__(self, index):
gc.collect()
return self.get_model(self.data_list[index])
class Correspondence_Demo_Dataset(Demo_Dataset):
def __init__(self, cfg):
super().__init__(cfg)
self.data_path = cfg.dataset.data_path
self.is_pc = cfg.is_pc
self.data_list = cfg.dataset.all_files
self.pc_num_pts = 100000
self.preprocess_mesh = cfg.preprocess_mesh
self.result_name = cfg.result_name
print("val dataset len:", len(self.data_list))
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