mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-21 15:57:45 +00:00
77 lines
3.2 KiB
Python
77 lines
3.2 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import torch
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from scene import Scene
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import os
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from tqdm import tqdm
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from os import makedirs
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from gaussian_renderer import render
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import torchvision
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from utils.general_utils import safe_state
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from argparse import ArgumentParser
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from arguments import ModelParams, PipelineParams, get_combined_args
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from gaussian_renderer import GaussianModel
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try:
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from diff_gaussian_rasterization import SparseGaussianAdam
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SPARSE_ADAM_AVAILABLE = True
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except:
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SPARSE_ADAM_AVAILABLE = False
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def render_set(model_path, name, iteration, views, gaussians, pipeline, background, train_test_exp, separate_sh):
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render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
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gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
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makedirs(render_path, exist_ok=True)
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makedirs(gts_path, exist_ok=True)
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for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
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rendering = render(view, gaussians, pipeline, background, use_trained_exp=train_test_exp, separate_sh=separate_sh)["render"]
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gt = view.original_image[0:3, :, :]
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if args.train_test_exp:
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rendering = rendering[..., rendering.shape[-1] // 2:]
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gt = gt[..., gt.shape[-1] // 2:]
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torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
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torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
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def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, separate_sh: bool):
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with torch.no_grad():
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gaussians = GaussianModel(dataset.sh_degree)
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scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
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bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
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background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
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if not skip_train:
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render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background, dataset.train_test_exp, separate_sh)
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if not skip_test:
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render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background, dataset.train_test_exp, separate_sh)
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if __name__ == "__main__":
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# Set up command line argument parser
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parser = ArgumentParser(description="Testing script parameters")
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model = ModelParams(parser, sentinel=True)
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pipeline = PipelineParams(parser)
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parser.add_argument("--iteration", default=-1, type=int)
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parser.add_argument("--skip_train", action="store_true")
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parser.add_argument("--skip_test", action="store_true")
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parser.add_argument("--quiet", action="store_true")
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args = get_combined_args(parser)
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print("Rendering " + args.model_path)
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# Initialize system state (RNG)
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safe_state(args.quiet)
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render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, SPARSE_ADAM_AVAILABLE) |