62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
import imageio
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import numpy as np
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import torch
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from scene import Scene
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import os
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import cv2
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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, ModelHiddenParams
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from gaussian_renderer import GaussianModel
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from time import time
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import open3d as o3d
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# import torch.multiprocessing as mp
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import threading
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from utils.render_utils import get_state_at_time
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import concurrent.futures
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def render_sets(dataset : ModelParams, hyperparam, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, skip_video: bool):
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with torch.no_grad():
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gaussians = GaussianModel(dataset.sh_degree, hyperparam)
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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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return gaussians, scene
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def save_point_cloud(points, model_path, timestamp):
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output_path = os.path.join(model_path,"point_pertimestamp")
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if not os.path.exists(output_path):
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os.makedirs(output_path,exist_ok=True)
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points = points.detach().cpu().numpy()
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pcd = o3d.geometry.PointCloud()
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pcd.points = o3d.utility.Vector3dVector(points)
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ply_path = os.path.join(output_path,f"points_{timestamp}.ply")
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o3d.io.write_point_cloud(ply_path, pcd)
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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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hyperparam = ModelHiddenParams(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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parser.add_argument("--skip_video", action="store_true")
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parser.add_argument("--configs", type=str)
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args = get_combined_args(parser)
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print("Rendering " , args.model_path)
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if args.configs:
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import mmcv
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from utils.params_utils import merge_hparams
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config = mmcv.Config.fromfile(args.configs)
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args = merge_hparams(args, config)
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# Initialize system state (RNG)
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safe_state(args.quiet)
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gaussians, scene = render_sets(model.extract(args), hyperparam.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, args.skip_video)
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for index, viewpoint in enumerate(scene.getVideoCameras()):
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points, scales_final, rotations_final, opacity_final, shs_final = get_state_at_time(gaussians, viewpoint)
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save_point_cloud(points, args.model_path, index) |