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render_mesh.py
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#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use
# under the terms of the LICENSE.md file.
#
# For inquiries contact george.drettakis@inria.fr
#
import torch
from scene import Scene, DeformModel
import os
from tqdm import tqdm
from os import makedirs
from gaussian_renderer import render
import torchvision
from utils.general_utils import safe_state
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, get_combined_args
from gaussian_renderer import GaussianModel
from utils.mesh_utils import GaussianExtractor, to_cam_open3d, post_process_mesh
from utils.render_utils import generate_path, create_videos
from utils.system_utils import load_config_from_file, merge_config
#import sys
import open3d as o3d
import numpy as np
from mesh_renderer import render_mesh, mesh_shape_renderer
import cv2
import json
import os
from PIL import Image
from read_gt_mesh import load_obj
def clean_mesh(mesh, edge_threshold: float = 0.1, min_triangles_connected: int = -1, fill_holes: bool = True) -> (torch.Tensor, torch.Tensor, torch.Tensor):
"""
Performs the following steps to clean the mesh:
1. edge_threshold_filter
2. remove_duplicated_vertices, remove_duplicated_triangles, remove_degenerate_triangles
3. remove small connected components
4. remove_unreferenced_vertices
5. fill_holes
:param vertices: (3, N) torch.Tensor of type torch.float32
:param faces: (3, M) torch.Tensor of type torch.long
:param colors: (3, N) torch.Tensor of type torch.float32 in range (0...1) giving RGB colors per vertex
:param edge_threshold: maximum length per edge (otherwise removes that face). If <=0, will not do this filtering
:param min_triangles_connected: minimum number of triangles in a connected component (otherwise removes those faces). If <=0, will not do this filtering
:param fill_holes: If true, will perform trimesh fill_holes step, otherwise not.
:return: (vertices, faces, colors) tuple as torch.Tensors of similar shape and type
"""
'''
if edge_threshold > 0:
# remove long edges
faces = edge_threshold_filter(vertices, faces, edge_threshold)
'''
# cleanup via open3d
#mesh = torch_to_o3d_mesh(vertices, faces) #, colors)
mesh.remove_duplicated_vertices()
mesh.remove_duplicated_triangles()
mesh.remove_degenerate_triangles()
if min_triangles_connected > 0:
# remove small components via open3d
triangle_clusters, cluster_n_triangles, cluster_area = mesh.cluster_connected_triangles()
triangle_clusters = np.asarray(triangle_clusters)
cluster_n_triangles = np.asarray(cluster_n_triangles)
triangles_to_remove = cluster_n_triangles[triangle_clusters] < min_triangles_connected
mesh.remove_triangles_by_mask(triangles_to_remove)
# cleanup via open3d
mesh.remove_unreferenced_vertices()
if fill_holes:
# misc cleanups via trimesh
mesh = o3d_to_trimesh(mesh)
mesh.process()
mesh.fill_holes()
return mesh
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
pipeline = PipelineParams(parser)
#hp = ModelHiddenParams(parser)
parser.add_argument("--iteration", default=-1, type=int)
parser.add_argument("--skip_train", action="store_true")
parser.add_argument("--skip_test", action="store_true")
parser.add_argument("--skip_mesh", action="store_true")
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--render_path", action="store_true")
parser.add_argument("--voxel_size", default=0.004, type=float, help='Mesh: voxel size for TSDF')
parser.add_argument("--depth_trunc", default=3.0, type=float, help='Mesh: Max depth range for TSDF')
parser.add_argument("--num_cluster", default=1000, type=int, help='Mesh: number of connected clusters to export')
parser.add_argument("--unbounded", action="store_true", help='Mesh: using unbounded mode for meshing')
parser.add_argument("--mesh_res", default=1024, type=int, help='Mesh: resolution for unbounded mesh extraction')
parser.add_argument("--white_background2", default=False, type=bool, help='Mesh: resolution for unbounded mesh extraction')
parser.add_argument("--config", type=str, default=None)
args = get_combined_args(parser)
#args = parser.parse_args(sys.argv[1:])
args.depth_trunc = 6
depth_filtering = True
#model._white_background = args.depth_trunc
print(model._white_background)
# Load config file
'''
if args.config:
config_data = load_config_from_file(args.config)
combined_args = merge_config(config_data, args)
args = Namespace(**combined_args)
'''
print("Rendering " + args.model_path)
dataset, iteration, pipe = model.extract(args), args.iteration, pipeline.extract(args)
#gaussians = GaussianModel(dataset.sh_degree,hp.extract(args))
deform = DeformModel(K=dataset.K, deform_type=dataset.deform_type, is_blender=dataset.is_blender, skinning=dataset.skinning, hyper_dim=dataset.hyper_dim, node_num=dataset.node_num, pred_opacity=dataset.pred_opacity, pred_color=dataset.pred_color, use_hash=dataset.use_hash, hash_time=dataset.hash_time, d_rot_as_res=dataset.d_rot_as_res, local_frame=dataset.local_frame, progressive_brand_time=dataset.progressive_brand_time, max_d_scale=dataset.max_d_scale)
deform.load_weights(dataset.model_path, iteration=iteration)
gs_fea_dim = deform.deform.node_num if dataset.skinning and deform.name == 'node' else dataset.hyper_dim
gaussians = GaussianModel(dataset.sh_degree, fea_dim=gs_fea_dim, with_motion_mask=dataset.gs_with_motion_mask)
#gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
train_dir = os.path.join(args.model_path, 'train', "ours_{}".format(scene.loaded_iter))
test_dir = os.path.join(args.model_path, 'test', "ours_{}".format(scene.loaded_iter))
gaussExtractor = GaussianExtractor(gaussians, render, pipe, bg_color=bg_color)
if not args.skip_train:
print("export training images ...")
os.makedirs(train_dir, exist_ok=True)
gaussExtractor.reconstruction(scene.getTrainCameras(),pipeline,background,deform,state="train",depth_filtering=depth_filtering)
gaussExtractor.export_image(train_dir)
if (not args.skip_test) and (len(scene.getTestCameras()) > 0):
print("export rendered testing images ...")
os.makedirs(test_dir, exist_ok=True)
gaussExtractor.reconstruction(scene.getTestCameras(),pipeline,background,deform,state="test",depth_filtering=depth_filtering)
gaussExtractor.export_image(test_dir)
if args.render_path:
print("render videos ...")
traj_dir = os.path.join(args.model_path, 'traj', "ours_{}".format(scene.loaded_iter))
os.makedirs(traj_dir, exist_ok=True)
n_fames = 240
cam_traj = generate_path(scene.getTrainCameras(), n_frames=n_fames)
gaussExtractor.reconstruction(cam_traj,pipeline,background,deform,state="video",depth_filtering=depth_filtering)
gaussExtractor.export_image(traj_dir)
create_videos(base_dir=traj_dir,
input_dir=traj_dir,
out_name='render_traj',
num_frames=n_fames)
file_path = os.path.join(args.source_path,"transforms_test.json")
# 假设 JSON 文件名为 'data.json'
with open(file_path, 'r') as file:
data = json.load(file)
# 遍历 frames 列表,并打印每个 frame 的 time 属性
timelist = []
for frame in data['frames']:
#print(f"time: {frame['time']}")
timelist.append(frame['time'])
print(timelist)
images_save_path = os.path.join(args.model_path, 'mesh_image')
meshshape_save_path = os.path.join(args.model_path, 'mesh_shape')
meshshape_gt_save_path = os.path.join(args.model_path, 'mesh_shape_gt')
if not os.path.exists(images_save_path):
os.mkdir(images_save_path)
if not os.path.exists(meshshape_save_path):
os.mkdir(meshshape_save_path)
if not os.path.exists(meshshape_gt_save_path):
os.mkdir(meshshape_gt_save_path)
if not args.skip_mesh:
for i in range(len(timelist)):
mesh_time = timelist[i]
print("export mesh ...")
os.makedirs(train_dir, exist_ok=True)
# set the active_sh to 0 to export only diffuse texture
gaussExtractor.gaussians.active_sh_degree = 0
gaussExtractor.reconstruction(scene.getTrainCameras_mesh(mesh_time =mesh_time),pipeline,background,deform,state="mesh",depth_filtering=depth_filtering)
# extract the mesh and save
if args.unbounded:
#name = f'fuse_unbounded_{mesh_time}.ply'
name = f'frame_{i}.ply'
mesh = gaussExtractor.extract_mesh_unbounded(resolution=args.mesh_res)
else:
name = f'frame_{i}.ply'
mesh = gaussExtractor.extract_mesh_bounded(voxel_size=args.voxel_size, sdf_trunc=5*args.voxel_size, depth_trunc=args.depth_trunc)
#o3d.io.write_triangle_mesh(os.path.join(train_dir, name), mesh)
#print("mesh saved at {}".format(os.path.join(train_dir, name)))
# post-process the mesh and save, saving the largest N clusters
mesh_post = post_process_mesh(mesh, cluster_to_keep=args.num_cluster)
#mesh_post = mesh_post.fill_holes()
o3d.io.write_triangle_mesh(os.path.join(train_dir, name), mesh_post)
print("mesh post processed saved at {}".format(os.path.join(train_dir, name)))
verts = torch.from_numpy(np.asarray(mesh_post.vertices)).unsqueeze(0).to(torch.float32).cuda()
faces = torch.from_numpy(np.asarray(mesh_post.triangles)).to(torch.int32).cuda()
vertex_colors = torch.from_numpy(np.asarray(mesh_post.vertex_colors)).unsqueeze(0).to(torch.float32).cuda()
cam = scene.getTestCameras()
viewpoint_cam = cam[i]
rets = render_mesh(viewpoint_cam, verts, faces, vertex_colors,whitebackground=True)
mesh_image = rets["render"]
mesh_img = mesh_image.cpu().detach().numpy()
mesh_img = mesh_img*255
imagename = str(i).zfill(5)
print('save images')
cv2.imwrite(images_save_path+f"/{imagename}.png", mesh_img)
mesh_image_shape = mesh_shape_renderer(verts, faces, viewpoint_cam)
mesh_image_shape_np = mesh_image_shape.detach().cpu().numpy() * 255
imagename = str(i).zfill(5)
print('save images')
cv2.imwrite(meshshape_save_path+f"/{imagename}.png", mesh_image_shape_np)