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render.py
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render.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
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
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
import open3d as o3d
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
pipeline = PipelineParams(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=-1.0, type=float, help='Mesh: voxel size for TSDF')
parser.add_argument("--depth_trunc", default=-1.0, type=float, help='Mesh: Max depth range for TSDF')
parser.add_argument("--sdf_trunc", default=-1.0, type=float, help='Mesh: truncation value for TSDF')
parser.add_argument("--num_cluster", default=50, 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')
args = get_combined_args(parser)
print("Rendering " + args.model_path)
dataset, iteration, pipe = model.extract(args), args.iteration, pipeline.extract(args)
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())
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())
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)
gaussExtractor.export_image(traj_dir)
create_videos(base_dir=traj_dir,
input_dir=traj_dir,
out_name='render_traj',
num_frames=n_fames)
if not args.skip_mesh:
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())
# extract the mesh and save
if args.unbounded:
name = 'fuse_unbounded.ply'
mesh = gaussExtractor.extract_mesh_unbounded(resolution=args.mesh_res)
else:
name = 'fuse.ply'
depth_trunc = (gaussExtractor.radius * 2.0) if args.depth_trunc < 0 else args.depth_trunc
voxel_size = (depth_trunc / args.mesh_res) if args.voxel_size < 0 else args.voxel_size
sdf_trunc = 5.0 * voxel_size if args.sdf_trunc < 0 else args.sdf_trunc
mesh = gaussExtractor.extract_mesh_bounded(voxel_size=voxel_size, sdf_trunc=sdf_trunc, depth_trunc=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)
o3d.io.write_triangle_mesh(os.path.join(train_dir, name.replace('.ply', '_post.ply')), mesh_post)
print("mesh post processed saved at {}".format(os.path.join(train_dir, name.replace('.ply', '_post.ply'))))