/
render_blender.py
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/
render_blender.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
import time
def render_set(train_resolution,mode,save_name,model_path, name, iteration, views, gaussians, pipeline, background):
render_path = os.path.join(model_path, name, save_name, "renders")
gts_path = os.path.join(model_path, name, save_name, "gt")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
dict_width2resolution={
800:1,
400:2,
200:4,
100:8
}
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
gt = view.original_image[0:3, :, :]
kernel_ratio=train_resolution/dict_width2resolution[gt.shape[-1]]
rendering = render(view, gaussians, pipeline, background, kernel_ratio=kernel_ratio,mode=mode)["render"]
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):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
resolution=1
train_resolution=dataset.resolution_train
# if not dataset.load_allres:
# resolution = dataset.resolution
dict_res_str={
1:"d0.png",
2:"d1.png",
4:"d2.png",
8:"d3.png"
}
dataset.resolution_str = dict_res_str[dataset.resolution]
dataset.resolution=1
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False,resolution_scales=[resolution])
bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
mode = dataset.mode
assert mode in["only-filter" ,"source-GS","integration","super-sampling"]
if mode == "only-filter": mode=3
elif mode=="source-GS": mode=0
elif mode=="integration": mode=1
elif mode=="super-sampling": mode=2
else: raise Exception("Not allowed this mode")
render_set(train_resolution,mode,dataset.save_name,dataset.model_path, "val", scene.loaded_iter, scene.getTestCameras(scale=resolution), gaussians, pipeline, background)
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("--quiet", action="store_true")
args = get_combined_args(parser)
print("Rendering " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test)