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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 matplotlib
matplotlib.use('Agg')
import math
import copy
import torch
from scene import Scene
import os
from tqdm import tqdm
from gaussian_renderer import render, render_gsplat
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
import numpy as np
import imageio.v3 as iio
from utils.graphics_utils import resize_render, make_video_divisble
from utils.trajectories import (
get_arc_w2cs,
get_avg_w2c,
get_lemniscate_w2cs,
get_spiral_w2cs,
get_wander_w2cs,
get_lookat,
)
from utils.camera_utils import generate_interpolated_path, generate_ellipse_path
from utils.camera_traj_config import trajectory_configs
def save_interpolated_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
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)
return inter_pose
def save_ellipse_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) * (n_views-1) # 10second, fps=30
all_inter_pose = generate_ellipse_path(org_pose, n_interp)
inter_pose_list = []
for p in all_inter_pose:
c2w = np.eye(4)
c2w[:3, :4] = p
inter_pose_list.append(np.linalg.inv(c2w))
inter_pose = np.stack(inter_pose_list, 0)
return inter_pose
def save_traj_pose(dataset, iter, args):
traj_up = trajectory_configs.get(args.dataset, {}).get(args.scene, {}).get('up', [-1, 1]) # Use -y axis in camera space as up vector
traj_params = trajectory_configs.get(args.dataset, {}).get(args.scene, {}).get(args.cam_traj, {})
# 1. Get training camera poses and calculate trajectory
org_pose = np.load(dataset.model_path + f"pose/pose_{iter}.npy")
train_w2cs = torch.from_numpy(org_pose).cuda()
# Calculate reference camera pose
avg_w2c = get_avg_w2c(train_w2cs)
train_c2ws = torch.linalg.inv(train_w2cs)
lookat = get_lookat(train_c2ws[:, :3, -1], train_c2ws[:, :3, 2])
# up = torch.tensor([0.0, 0.0, 1.0], device="cuda")
avg_c2w = torch.linalg.inv(avg_w2c)
up = traj_up[0] * (avg_c2w[:3, traj_up[1]])
# up = traj_up[0] * (avg_c2w[:3, 0]+avg_c2w[:3, 1])/2
# Temporarily load a camera to get intrinsic parameters
tmp_args = copy.deepcopy(args)
tmp_args.get_video = False
tmp_dataset = copy.deepcopy(dataset)
tmp_dataset.eval = False
with torch.no_grad():
temp_gaussians = GaussianModel(dataset.sh_degree)
temp_scene = Scene(tmp_dataset, temp_gaussians, load_iteration=iter, opt=tmp_args, shuffle=False)
view = temp_scene.getTrainCameras()[0]
tanfovx = math.tan(view.FoVx * 0.5)
tanfovy = math.tan(view.FoVy * 0.5)
focal_length_x = view.image_width / (2 * tanfovx)
focal_length_y = view.image_height / (2 * tanfovy)
K = torch.tensor([[focal_length_x, 0, view.image_width/2],
[0, focal_length_y, view.image_height/2],
[0, 0, 1]], device="cuda")
img_wh = (view.image_width, view.image_height)
del temp_scene # Release temporary scene
del temp_gaussians # Release temporary gaussians
# Calculate bounding sphere radius
rc_train_c2ws = torch.einsum("ij,njk->nik", torch.linalg.inv(avg_w2c), train_c2ws)
rc_pos = rc_train_c2ws[:, :3, -1]
rads = (rc_pos.amax(0) - rc_pos.amin(0)) * 1.25
num_frames = int(10 * 30 / args.n_views) * (args.n_views-1)
# Generate camera poses based on trajectory type
if args.cam_traj == 'arc':
w2cs = get_arc_w2cs(
ref_w2c=avg_w2c,
lookat=lookat,
up=up,
focal_length=K[0, 0].item(),
rads=rads,
num_frames=num_frames,
degree=traj_params.get('degree', 180.0)
)
elif args.cam_traj == 'spiral':
w2cs = get_spiral_w2cs(
ref_w2c=avg_w2c,
lookat=lookat,
up=up,
focal_length=K[0, 0].item(),
rads=rads,
num_frames=num_frames,
zrate=traj_params.get('zrate', 0.5),
rots=traj_params.get('rots', 1)
)
elif args.cam_traj == 'lemniscate':
w2cs = get_lemniscate_w2cs(
ref_w2c=avg_w2c,
lookat=lookat,
up=up,
focal_length=K[0, 0].item(),
rads=rads,
num_frames=num_frames,
degree=traj_params.get('degree', 45.0)
)
elif args.cam_traj == 'wander':
w2cs = get_wander_w2cs(
ref_w2c=avg_w2c,
focal_length=K[0, 0].item(),
num_frames=num_frames,
max_disp=traj_params.get('max_disp', 48.0)
)
else:
raise ValueError(f"Unknown camera trajectory: {args.cam_traj}")
return w2cs.cpu().numpy()
def render_sets(dataset: ModelParams, iteration: int, pipeline: PipelineParams, args):
if args.cam_traj in ['interpolated', 'ellipse']:
w2cs = globals().get(f'save_{args.cam_traj}_pose')(dataset.model_path, iteration, args.n_views)
else:
w2cs = save_traj_pose(dataset, iteration, args)
# visualizer(org_pose, ["green" for _ in org_pose], dataset.model_path + f"pose/poses_optimized.png")
# visualizer(w2cs, ["blue" for _ in w2cs], dataset.model_path + f"pose/poses_{args.cam_traj}.png")
np.save(dataset.model_path + f"pose/pose_{args.cam_traj}.npy", w2cs)
# 2. Load model and scene
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]
bg_color = [1, 1, 1]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
# 3. Rendering
# render_path = os.path.join(dataset.model_path, args.cam_traj, f"ours_{iteration}", "renders")
# if os.path.exists(render_path):
# shutil.rmtree(render_path)
# makedirs(render_path, exist_ok=True)
video = []
for idx, w2c in enumerate(tqdm(w2cs, desc="Rendering progress")):
camera_pose = get_tensor_from_camera(w2c.transpose(0, 1))
view = scene.getTrainCameras()[0] # Use parameters from the first camera as template
if args.resize:
view = resize_render(view)
rendering = render(
view, gaussians, pipeline, background, camera_pose=camera_pose
)["render"]
# # Save single frame image
# torchvision.utils.save_image(
# rendering, os.path.join(render_path, "{0:05d}".format(idx) + ".png")
# )
# Add to video list
# img = (rendering.detach().cpu().numpy() * 255.0).astype(np.uint8)
img = (torch.clamp(rendering, 0, 1).detach().cpu().numpy() * 255.0).round().astype(np.uint8)
video.append(img)
video = np.stack(video, 0).transpose(0, 2, 3, 1)
# Save video
if args.get_video:
video_dir = os.path.join(dataset.model_path, 'videos')
os.makedirs(video_dir, exist_ok=True)
output_video_file = os.path.join(video_dir, f'{args.scene}_{args.n_views}_view_{args.cam_traj}.mp4')
# iio.imwrite(output_video_file, make_video_divisble(video), fps=30)
iio.imwrite(
output_video_file,
make_video_divisble(video),
fps=30,
codec='libx264',
quality=None,
output_params=[
'-crf', '28', # Good quality range between 18-28
'-preset', 'veryslow',
'-pix_fmt', 'yuv420p',
'-movflags', '+faststart'
]
)
# if args.get_video:
# image_folder = os.path.join(dataset.model_path, f'{args.cam_traj}/ours_{args.iteration}/renders')
# output_video_file = os.path.join(dataset.model_path, f'{args.scene}_{args.n_views}_view_{args.cam_traj}.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("--quiet", action="store_true")
parser.add_argument("--get_video", action="store_true")
parser.add_argument("--n_views", default=120, type=int)
parser.add_argument("--dataset", default=None, type=str)
parser.add_argument("--scene", default=None, type=str)
parser.add_argument("--cam_traj", default='arc', type=str,
choices=['arc', 'spiral', 'lemniscate', 'wander', 'interpolated', 'ellipse'],
help="Camera trajectory type")
parser.add_argument("--resize", action="store_true", default=True,
help="If True, resize rendering to square")
parser.add_argument("--feat_type", type=str, nargs='*', default=None,
help="Feature type(s). Multiple types can be specified for combination.")
parser.add_argument("--method", type=str, default='dust3r',
help="Method of Initialization, e.g., 'dust3r' or 'mast3r'")
args = get_combined_args(parser)
print("Rendering " + args.model_path)
render_sets(
model.extract(args),
args.iteration,
pipeline.extract(args),
args,
) |