render_by_interp.py

#
# 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 [email protected]
#

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
from utils.pose_utils import get_tensor_from_camera
from utils.camera_utils import generate_interpolated_path
from utils.camera_utils import visualizer
import cv2
import numpy as np
import imageio


def save_interpolate_pose(model_path, iter, n_views):

 org_pose = np.load(model_path + f"pose/pose_{iter}.npy")
 # visualizer(org_pose, ["green" for _ in org_pose], model_path + "pose/poses_optimized.png")
 # n_interp = int(10 * 30 / n_views) # 10second, fps=30
 n_interp = int(5 * 30 / n_views) # 5second, fps=30
 all_inter_pose = []
 for i in range(n_views-1):
 tmp_inter_pose = generate_interpolated_path(poses=org_pose[i:i+2], n_interp=n_interp)
 all_inter_pose.append(tmp_inter_pose)
 all_inter_pose = np.array(all_inter_pose).reshape(-1, 3, 4)

 inter_pose_list = []
 for p in all_inter_pose:
 tmp_view = np.eye(4)
 tmp_view[:3, :3] = p[:3, :3]
 tmp_view[:3, 3] = p[:3, 3]
 inter_pose_list.append(tmp_view)
 inter_pose = np.stack(inter_pose_list, 0)
 # visualizer(inter_pose, ["blue" for _ in inter_pose], model_path + "pose/poses_interpolated.png")
 np.save(model_path + "pose/pose_interpolated.npy", inter_pose)


def images_to_video(image_folder, output_video_path, fps=30):
 """
 Convert images in a folder to a video.

 Args:
 - image_folder (str): The path to the folder containing the images.
 - output_video_path (str): The path where the output video will be saved.
 - fps (int): Frames per second for the output video.
 """
 images = []

 for filename in sorted(os.listdir(image_folder)):
 if filename.endswith(('.png', '.jpg', '.jpeg', '.JPG', '.PNG')):
 image_path = os.path.join(image_folder, filename)
 image = imageio.imread(image_path)
 images.append(image)

 imageio.mimwrite(output_video_path, images, fps=fps)


def render_set(model_path, name, iteration, views, gaussians, pipeline, background):
 render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
 makedirs(render_path, exist_ok=True)

 # for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
 for idx, view in enumerate(views):
 camera_pose = get_tensor_from_camera(view.world_view_transform.transpose(0, 1))
 rendering = render(
 view, gaussians, pipeline, background, camera_pose=camera_pose
 )["render"]
 gt = view.original_image[0:3, :, :]
 torchvision.utils.save_image(
 rendering, os.path.join(render_path, "{0:05d}".format(idx) + ".png")
 )


def render_sets(
 dataset: ModelParams,
 iteration: int,
 pipeline: PipelineParams,
 skip_train: bool,
 skip_test: bool,
 args,
):

 # Applying interpolation
 save_interpolate_pose(dataset.model_path, iteration, args.n_views)

 with torch.no_grad():
 gaussians = GaussianModel(dataset.sh_degree)
 scene = Scene(dataset, gaussians, load_iteration=iteration, opt=args, 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")

 # render interpolated views
 render_set(
 dataset.model_path,
 "interp",
 scene.loaded_iter,
 scene.getTrainCameras(),
 gaussians,
 pipeline,
 background,
 )

 if args.get_video:
 image_folder = os.path.join(dataset.model_path, f'interp/ours_{args.iteration}/renders')
 output_video_file = os.path.join(dataset.model_path, f'{args.scene}_{args.n_views}_view.mp4')
 images_to_video(image_folder, output_video_file, fps=30)


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")

 parser.add_argument("--get_video", action="store_true")
 parser.add_argument("--n_views", default=None, type=int)
 parser.add_argument("--scene", default=None, type=str)
 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,
 args,
 )