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render_anchor.py
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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 george.drettakis@inria.fr
#
import os
import torch
from os import makedirs
import numpy as np
from scene import AnchorScene
import time
from gaussian_renderer import anchor_render, anchor_prefilter_voxel
import torchvision
from tqdm import tqdm
from utils.general_utils import safe_state
from utils.pose_utils import generate_ellipse_path, pose_spherical
from utils.graphics_utils import getWorld2View2
from argparse import ArgumentParser
from arguments import AnchorModelParams, PipelineParams, get_combined_args
from gaussian_renderer import AnchorGaussianModel
import imageio
def render_set(model_path, name, iteration, views, gaussians, pipeline, background):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
depth_path = os.path.join(model_path, name, "ours_{}".format(iteration), "depth")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
makedirs(depth_path, exist_ok=True)
t_list = []
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
voxel_visible_mask = anchor_prefilter_voxel(view, gaussians, pipeline, background)
render_pkg = anchor_render(view, gaussians, pipeline, background, visible_mask=voxel_visible_mask)
rendering = render_pkg["render"]
gt = view.original_image[0:3, :, :]
depth = render_pkg["depth"]
depth = depth / (depth.max() + 1e-5)
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(idx) + ".png"))
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
torch.cuda.synchronize();
t0 = time.time()
voxel_visible_mask = anchor_prefilter_voxel(view, gaussians, pipeline, background)
render_pkg = anchor_render(view, gaussians, pipeline, background, visible_mask=voxel_visible_mask)
torch.cuda.synchronize();
t1 = time.time()
t_list.append(t1 - t0)
t = np.array(t_list[5:])
fps = 1.0 / t.mean()
print(f'Test FPS: \033[1;35m{fps:.5f}\033[0m')
def render_video(model_path, iteration, views, gaussians, pipeline, background):
render_path = os.path.join(model_path, 'video', "ours_{}".format(iteration))
makedirs(render_path, exist_ok=True)
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
view = views[0]
renderings = []
for idx, pose in enumerate(tqdm(generate_ellipse_path(views, n_frames=600), desc="Rendering progress")):
view.world_view_transform = torch.tensor(
getWorld2View2(pose[:3, :3].T, pose[:3, 3], view.trans, view.scale)).transpose(0, 1).cuda()
view.full_proj_transform = (
view.world_view_transform.unsqueeze(0).bmm(view.projection_matrix.unsqueeze(0))).squeeze(0)
view.camera_center = view.world_view_transform.inverse()[3, :3]
voxel_visible_mask = anchor_prefilter_voxel(view, gaussians, pipeline, background)
rendering = anchor_render(view, gaussians, pipeline, background, visible_mask=voxel_visible_mask)["render"]
renderings.append(to8b(rendering.cpu().numpy()))
# torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
renderings = np.stack(renderings, 0).transpose(0, 2, 3, 1)
imageio.mimwrite(os.path.join(render_path, 'video.mp4'), renderings, fps=60, quality=8)
def interpolate_all(model_path, iteration, views, gaussians, pipeline, background):
render_path = os.path.join(model_path, "interpolate_all_{}".format(iteration), "renders")
depth_path = os.path.join(model_path, "interpolate_all_{}".format(iteration), "depth")
os.makedirs(render_path, exist_ok=True)
os.makedirs(depth_path, exist_ok=True)
frame = 520
render_poses = torch.stack([pose_spherical(angle, -30.0, 4) for angle in np.linspace(-180, 180, frame + 1)[:-1]], 0)
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
idx = torch.randint(0, len(views), (1,)).item()
view = views[idx] # Choose a specific time for rendering
renderings = []
for i, pose in enumerate(tqdm(render_poses, desc="Rendering progress")):
matrix = np.linalg.inv(np.array(pose))
R = -np.transpose(matrix[:3, :3])
R[:, 0] = -R[:, 0]
T = -matrix[:3, 3]
view.reset_extrinsic(R, T)
voxel_visible_mask = anchor_prefilter_voxel(view, gaussians, pipeline, background)
rendering = anchor_render(view, gaussians, pipeline, background, visible_mask=voxel_visible_mask)["render"]
renderings.append(to8b(rendering.cpu().numpy()))
# depth = results["depth"]
# depth = depth / (depth.max() + 1e-5)
# torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(i) + ".png"))
# torchvision.utils.save_image(depth, os.path.join(depth_path, '{0:05d}'.format(i) + ".png"))
renderings = np.stack(renderings, 0).transpose(0, 2, 3, 1)
imageio.mimwrite(os.path.join(render_path, 'video.mp4'), renderings, fps=60, quality=8)
def render_sets(dataset: AnchorModelParams, iteration: int, pipeline: PipelineParams, skip_train: bool,
skip_test: bool, mode: str):
with torch.no_grad():
gaussians = AnchorGaussianModel(dataset.feat_dim, dataset.n_offsets, dataset.voxel_size, dataset.update_depth,
dataset.update_init_factor, dataset.update_hierachy_factor)
scene = AnchorScene(dataset, gaussians, load_iteration=iteration, shuffle=False)
gaussians.eval()
bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
if mode == "real-360":
render_video(dataset.model_path, scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline,
background)
elif mode == "syn-360":
interpolate_all(dataset.model_path, scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline,
background)
else:
if not skip_train:
render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline,
background)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline,
background)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = AnchorModelParams(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("--mode", default='render', choices=['render', 'syn-360', 'real-360'])
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.mode)