gaussian-splatting/render.py at main · nicholasnge/gaussian-splatting · GitHub

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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 pandas as pd

try:
    from diff_gaussian_rasterization import SparseGaussianAdam
    SPARSE_ADAM_AVAILABLE = True
except:
    SPARSE_ADAM_AVAILABLE = False

models_configuration = {
    'baseline': {
        'quantised': False,
        'half_float': False,
        'name': 'point_cloud.ply'
        },
    'quantised': {
        'quantised': True,
        'half_float': False,
        'name': 'point_cloud_quantised.ply'
        },
    'quantised_half': {
        'quantised': True,
        'half_float': True,
        'name': 'point_cloud_quantised_half.ply'
        },
}

def render_set(model_path,
               name,
               iteration,
               views,
               gaussians,
               pipeline,
               background,
               pcd_name):
    render_path = os.path.join(model_path, name, f"{pcd_name}_{iteration}", "renders")
    gts_path = os.path.join(model_path, name, f"{pcd_name}_{iteration}", "gt")

    makedirs(render_path, exist_ok=True)
    makedirs(gts_path, exist_ok=True)

    for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
        view.load()
        rendering = render(view, gaussians, pipeline, background)["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, separate_sh: bool):
    with torch.no_grad():
        gaussians = GaussianModel(dataset.sh_degree)
        scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)

        bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
        background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")

        configurations = {}
        if not skip_train:
            configurations["train"] = scene.getTrainCameras()
        if not skip_test:
            configurations["test"] = scene.getTestCameras()

        for model in args.models:
            name = models_configuration[model]['name']
            quantised = models_configuration[model]['quantised']
            half_float = models_configuration[model]['half_float']
            try:
                scene.gaussians.load_ply(os.path.join(scene.model_path,
                                                            "point_cloud",
                                                            "iteration_" + str(scene.loaded_iter),
                                                            name), quantised=quantised, half_float=half_float)
            except:
                raise RuntimeError(f"Configuration {model} with name {name} not found!")

            for k,v in configurations.items():
                df = pd.DataFrame()
                render_set(dataset.model_path, k, scene.loaded_iter, v, gaussians, pipeline, background, name)

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("--models",
                    help="Types of models to test",
                    choices=models_configuration.keys(),
                    default=['baseline', 'quantised', 'quantised_half'],
                    nargs="+")
    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, SPARSE_ADAM_AVAILABLE)