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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 [email protected]
#
import torch
from scene import Scene, SpecularModel
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 utils.pose_utils import pose_spherical, render_wander_path
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args
from gaussian_renderer import GaussianModel
import imageio
import numpy as np
import cv2
def render_set(model_path, load2gpt_on_the_fly, name, iteration, views, gaussians, pipeline, background, specular):
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), "depth")
# acc_path = os.path.join(model_path, name, "ours_{}".format(iteration), "acc")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
makedirs(depth_path, exist_ok=True)
# makedirs(acc_path, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
dir_pp = (gaussians.get_xyz - view.camera_center.repeat(gaussians.get_features.shape[0], 1))
dir_pp_normalized = dir_pp / dir_pp.norm(dim=1, keepdim=True)
mlp_color = specular.step(gaussians.get_xyz.detach(), dir_pp_normalized)
results = render(view, gaussians, pipeline, background, mlp_color)
rendering = results["render"]
depth = results["depth"]
depth = depth / (depth.max() + 1e-5)
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"))
torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(idx) + ".png"))
def render_sets(dataset: ModelParams, iteration: int, pipeline: PipelineParams, skip_train: bool, skip_test: bool,
mode: str):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
specular = SpecularModel()
specular.load_weights(dataset.model_path)
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
render_func = render_set
if not skip_train:
render_func(dataset.model_path, dataset.load2gpu_on_the_fly, "train", scene.loaded_iter,
scene.getTrainCameras(), gaussians, pipeline,
background, specular)
if not skip_test:
render_func(dataset.model_path, dataset.load2gpu_on_the_fly, "test", scene.loaded_iter,
scene.getTestCameras(), gaussians, pipeline,
background, specular)
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")
parser.add_argument("--mode", default='render', choices=['render', 'view', 'all', 'pose', 'original'])
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, args.mode)