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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 [email protected]
#
import torch
from scene import Scene
import os
from tqdm import tqdm
from os import makedirs
from gaussian_renderer import render
import torchvision
import json
from utils.general_utils import safe_state
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args
from arguments import ModelParams, PipelineParams, SplattingSettings
from diff_gaussian_rasterization import ExtendedSettings
from gaussian_renderer import GaussianModel
def render_set(model_path, name, iteration, views, gaussians, pipeline, background, splat_args: ExtendedSettings, render_depth: bool):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders" if not render_depth else "depth")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
rendering = render(view, gaussians, pipeline, background, splat_args=splat_args, render_depth=render_depth)["render"]
gt = view.original_image[0:3, :, :]
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, splat_args: ExtendedSettings, render_depth: bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False, skip_test=args.skip_test, skip_train=args.skip_train)
bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
if not skip_train:
render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background, splat_args, render_depth)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background, splat_args, render_depth)
# write number of gaussians too
num_gaussians = scene.gaussians.get_xyz.shape[0]
with open(os.path.join(dataset.model_path, "point_cloud", f'iteration_{scene.loaded_iter}', 'num_gaussians.json'), 'w') as fp:
json.dump(obj={
"num_gaussians": num_gaussians,
}, fp=fp, indent=2)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
pipeline = PipelineParams(parser)
ss = SplattingSettings(parser, render=True)
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("--quiet", action="store_true")
parser.add_argument("--render_depth", action="store_true")
args = get_combined_args(parser)
print("Rendering " + args.model_path)
splat_args = ss.get_settings(args)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, splat_args, args.render_depth)