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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, vis_depth
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args
from gaussian_renderer import GaussianModel
import imageio
import numpy as np
def render_set(model_path, name, iteration, views, gaussians, pipeline, background):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
depth_path = os.path.join(model_path, name, "ours_{}".format(iteration), "render_depth")
normal_path = os.path.join(model_path, name, "ours_{}".format(iteration), "render_normal")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
makedirs(depth_path , exist_ok=True)
makedirs(normal_path, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
renders = render(view, gaussians, pipeline, background, return_depth=True, return_normal=True)
rendering = renders["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"))
render_depth = renders["render_depth"]
if view.sky_mask is not None:
render_depth[~(view.sky_mask.to(render_depth.device).to(torch.bool))] = 300
render_depth = vis_depth(render_depth.detach().cpu().numpy())[0]
imageio.imwrite(os.path.join(depth_path , '{0:05d}'.format(idx) + ".png"), render_depth)
render_normal = (renders["render_normal"] + 1.0) / 2.0
if view.sky_mask is not None:
render_normal[~(view.sky_mask.to(rendering.device).to(torch.bool).unsqueeze(0).repeat(3, 1, 1))] = -10
# render_normal = renders["render_normal"]
np.save(os.path.join(normal_path, '{0:05d}'.format(idx) + ".png"), renders["render_normal"].detach().cpu().numpy())
torchvision.utils.save_image(render_normal, os.path.join(normal_path, '{0:05d}'.format(idx) + ".png"))
# normal_gt = torch.nn.functional.normalize(view.normal, p=2, dim=0)
# render_normal_gt = (normal_gt + 1.0) / 2.0
# torchvision.utils.save_image(render_normal_gt, os.path.join(normal_path, '{0:05d}'.format(idx) + "_normalgt.png"))
# exit()
def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
# gaussians._scaling[:, 0] = 0.001
# gaussians._scaling[:, 1] = 0.0005
# gaussians._scaling[:, 2] = -10000.0
# gaussians._rotation[:, 0] = 1
# gaussians._rotation[:, 1:] = 0
scales = gaussians.get_scaling
# min_scale, _ = torch.min(scales, dim=1)
# max_scale, _ = torch.max(scales, dim=1)
# median_scale, _ = torch.median(scales, dim=1)
# print(min_scale)
# print(max_scale)
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)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), 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)