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| import json | |
| import torch | |
| from scene import Scene | |
| from pathlib import Path | |
| from PIL import Image | |
| import numpy as np | |
| import sys | |
| 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, OptimizationParams | |
| from gaussian_renderer import GaussianModel | |
| from mediapy import write_video | |
| from tqdm import tqdm | |
| from einops import rearrange | |
| from utils.camera_utils import get_uniform_poses | |
| from mediapy import write_image | |
| def render_spiral(dataset, opt, pipe, model_path): | |
| gaussians = GaussianModel(dataset.sh_degree) | |
| scene = Scene(dataset, gaussians, load_iteration=-1, 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") | |
| viewpoint_stack = scene.getTrainCameras().copy() | |
| views = [] | |
| alphas = [] | |
| for view_cam in tqdm(viewpoint_stack): | |
| bg = torch.rand((3), device="cuda") if opt.random_background else background | |
| render_pkg = render(view_cam, gaussians, pipe, bg) | |
| image, viewspace_point_tensor, visibility_filter, radii = ( | |
| render_pkg["render"], | |
| render_pkg["viewspace_points"], | |
| render_pkg["visibility_filter"], | |
| render_pkg["radii"], | |
| ) | |
| views.append(image) | |
| alphas.append(render_pkg["alpha"]) | |
| views = torch.stack(views) | |
| alphas = torch.stack(alphas) | |
| png_images = ( | |
| (torch.cat([views, alphas], dim=1).clamp(0.0, 1.0) * 255) | |
| .cpu() | |
| .numpy() | |
| .astype(np.uint8) | |
| ) | |
| png_images = rearrange(png_images, "t c h w -> t h w c") | |
| poses = get_uniform_poses( | |
| dataset.num_frames, dataset.radius, dataset.elevation, opengl=True | |
| ) | |
| camera_angle_x = np.deg2rad(dataset.fov) | |
| name = Path(dataset.model_path).stem | |
| meta_dir = Path(f"blenders/{name}") | |
| meta_dir.mkdir(exist_ok=True, parents=True) | |
| meta = {} | |
| meta["camera_angle_x"] = camera_angle_x | |
| meta["frames"] = [] | |
| for idx, (pose, image) in enumerate(zip(poses, png_images)): | |
| this_frames = {} | |
| this_frames["file_path"] = f"{idx:06d}" | |
| this_frames["transform_matrix"] = pose.tolist() | |
| meta["frames"].append(this_frames) | |
| write_image(meta_dir / f"{idx:06d}.png", image) | |
| with open(meta_dir / "transforms_train.json", "w") as f: | |
| json.dump(meta, f, indent=4) | |
| with open(meta_dir / "transforms_val.json", "w") as f: | |
| json.dump(meta, f, indent=4) | |
| with open(meta_dir / "transforms_test.json", "w") as f: | |
| json.dump(meta, f, indent=4) | |
| if __name__ == "__main__": | |
| # Set up command line argument parser | |
| parser = ArgumentParser(description="Training script parameters") | |
| lp = ModelParams(parser) | |
| op = OptimizationParams(parser) | |
| pp = 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 = parser.parse_args(sys.argv[1:]) | |
| print("Rendering " + args.model_path) | |
| lp = lp.extract(args) | |
| fake_image = Image.fromarray(np.zeros([512, 512, 3], dtype=np.uint8)) | |
| lp.images = [fake_image] * args.num_frames | |
| # Initialize system state (RNG) | |
| render_spiral( | |
| lp, | |
| op.extract(args), | |
| pp.extract(args), | |
| model_path=args.model_path, | |
| ) | |