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Zero
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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 [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,
)
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