DC-Gaussian/render.py at master · linhanwang/DC-Gaussian · 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
import numpy as np
from tqdm import tqdm
from matplotlib import cm
from os import makedirs
from gaussian_renderer import render
import torchvision
from utils.visualization_tools import visualize_depth, visualize_cmap
from utils.general_utils import safe_state
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args
from gaussian_renderer import GaussianModel


def adapter(depth):
    p = 0.05
    distance_limits = np.percentile(depth.flatten(), [p, 100.0 - p])
    depth_curve_fn = lambda x: -np.log(x + np.finfo(np.float32).eps)
    lo, hi = distance_limits
    print(depth.shape)
    img = visualize_cmap(depth, np.ones_like(depth), cm.get_cmap('turbo'), lo, hi, curve_fn=depth_curve_fn)
    return torch.tensor(img).squeeze(0).permute(2, 0, 1)

def render_set(model: ModelParams, name, iteration, views, gaussians, pipeline, background):
    model_path = model.model_path
    render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
    gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")

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

    # torchvision.utils.save_image(gaussians._win, os.path.join(render_path, "reflection.png"))
    torchvision.utils.save_image(gaussians._alpha, os.path.join(render_path, "alpha.png"))
    for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
        output = render(view, gaussians, pipeline, background)
        gt = view.original_image[0:3, :, :]
        depth = output['depth'].cpu().squeeze(0).numpy()
        buffer_image = adapter(depth)
        torchvision.utils.save_image(buffer_image, os.path.join(render_path, f'{idx:05d}_depth.png'))
        # write_depth(os.path.join(render_path, f'{idx:05d}_depth2'), np.squeeze(depth), bits=2)
        tran, obs, image = gaussians.adjust_image(output['render'], view.T)
        torchvision.utils.save_image(image, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
        torchvision.utils.save_image(tran, os.path.join(render_path, '{0:05d}'.format(idx) + "_tran.png"))
        torchvision.utils.save_image(obs, os.path.join(render_path, '{0:05d}'.format(idx) + "_obs.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, dataset.win_type)
        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")

        if not skip_train:
            render_set(dataset, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background)

        if not skip_test:
            render_set(dataset, "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)