OpenGaussian/render.py at main · co-li/OpenGaussian · 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
import torch.nn.functional as F
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 numpy as np
from utils.opengs_utlis import get_SAM_mask_and_feat, load_code_book

# Randomly initialize 300 colors for visualizing the SAM mask. [OpenGaussian]
np.random.seed(42)
colors_defined = np.random.randint(100, 256, size=(300, 3))
colors_defined[0] = np.array([0, 0, 0]) # Ignore the mask ID of -1 and set it to black.
colors_defined = torch.from_numpy(colors_defined)

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")

    render_ins_feat_path1 = os.path.join(model_path, name, "ours_{}".format(iteration), "renders_ins_feat1")
    render_ins_feat_path2 = os.path.join(model_path, name, "ours_{}".format(iteration), "renders_ins_feat2")
    gt_sam_mask_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt_sam_mask")

    makedirs(render_path, exist_ok=True)
    makedirs(gts_path, exist_ok=True)
    makedirs(render_ins_feat_path1, exist_ok=True)
    makedirs(render_ins_feat_path2, exist_ok=True)
    makedirs(gt_sam_mask_path, exist_ok=True)

    # load codebook
    root_code_book_path = os.path.join(model_path, "point_cloud", f'iteration_{iteration}', "root_code_book")
    leaf_code_book_path = os.path.join(model_path, "point_cloud", f'iteration_{iteration}', "leaf_code_book")
    if os.path.exists(os.path.join(root_code_book_path, 'kmeans_inds.bin')):
        root_code_book, root_cluster_indices = load_code_book(root_code_book_path)
        root_cluster_indices = torch.from_numpy(root_cluster_indices).cuda()
    if os.path.exists(os.path.join(leaf_code_book_path, 'kmeans_inds.bin')):
        leaf_code_book, leaf_cluster_indices = load_code_book(leaf_code_book_path)
        leaf_cluster_indices = torch.from_numpy(leaf_cluster_indices).cuda()
    else:
        leaf_cluster_indices = None

    # render
    for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
        render_pkg = render(view, gaussians, pipeline, background, iteration, rescale=False)

        # RGB
        rendering = render_pkg["render"]
        gt = view.original_image[0:3, :, :]

        # ins_feat
        rendered_ins_feat = render_pkg["ins_feat"]
        gt_sam_mask = view.original_sam_mask.cuda()    # [4, H, W]

        # Rendered RGB
        torchvision.utils.save_image(rendering, os.path.join(render_path, view.image_name + ".png"))
        # GT RGB
        torchvision.utils.save_image(gt, os.path.join(gts_path, view.image_name + ".png"))

        # ins_feat
        torchvision.utils.save_image(rendered_ins_feat[:3,:,:], os.path.join(render_ins_feat_path1, view.image_name + "_1.png"))
        torchvision.utils.save_image(rendered_ins_feat[3:6,:,:], os.path.join(render_ins_feat_path2, view.image_name + "_2.png"))

        # NOTE get SAM id, mask bool, mask_feat, invalid pix
        mask_id, _, _, _ = \
            get_SAM_mask_and_feat(gt_sam_mask, level=0, original_mask_feat=view.original_mask_feat)
        # mask visualization
        mask_color_rand = colors_defined[mask_id.detach().cpu().type(torch.int64)].type(torch.float64)
        mask_color_rand = mask_color_rand.permute(2, 0, 1)
        torchvision.utils.save_image(mask_color_rand/255.0, os.path.join(gt_sam_mask_path, view.image_name + ".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)
        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.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)