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render.py
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import torch
from scene import Scene
import os
from tqdm import tqdm
import numpy as np
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
from gaussian_renderer import GaussianModel
import cv2
from tqdm import tqdm
from utils.graphics_utils import getWorld2View2
from utils.pose_utils import generate_ellipse_path, generate_spiral_path
from utils.general_utils import vis_depth
def render_set(model_path, name, iteration, views, gaussians, pipeline, background, args):
render_path = os.path.join(model_path, name, "ours_{}".format(iteration), "renders")
gts_path = os.path.join(model_path, name, "ours_{}".format(iteration), "gt")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
rendering = render(view, gaussians, pipeline, background)
gt = view.original_image[0:3, :, :]
torchvision.utils.save_image(rendering["render"], os.path.join(render_path, view.image_name + '.png'))
torchvision.utils.save_image(gt, os.path.join(gts_path, view.image_name + ".png"))
if args.render_depth:
depth_map = vis_depth(rendering['depth'][0].detach().cpu().numpy())
np.save(os.path.join(render_path, view.image_name + '_depth.npy'), rendering['depth'][0].detach().cpu().numpy())
cv2.imwrite(os.path.join(render_path, view.image_name + '_depth.png'), depth_map)
def render_video(source_path, model_path, iteration, views, gaussians, pipeline, background, fps=30):
render_path = os.path.join(model_path, 'video', "ours_{}".format(iteration))
makedirs(render_path, exist_ok=True)
view = views[0]
if source_path.find('llff') != -1 or source_path.find('LLFF') != -1:
render_poses = generate_spiral_path(np.load(source_path + '/poses_bounds.npy'))
elif source_path.find('360') != -1:
render_poses = generate_ellipse_path(views)
size = (view.original_image.shape[2], view.original_image.shape[1])
fourcc = cv2.VideoWriter_fourcc(*'XVID')
final_video = cv2.VideoWriter(os.path.join(render_path, 'final_video.mp4'), fourcc, fps, size)
for idx, pose in enumerate(tqdm(render_poses, 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]
rendering = render(view, gaussians, pipeline, background)
img = torch.clamp(rendering["render"], min=0., max=1.)
torchvision.utils.save_image(img, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
video_img = (img.permute(1, 2, 0).detach().cpu().numpy() * 255.).astype(np.uint8)[..., ::-1]
final_video.write(video_img)
final_video.release()
def render_sets(dataset : ModelParams, pipeline : PipelineParams, args):
with torch.no_grad():
gaussians = GaussianModel(args)
scene = Scene(args, gaussians, load_iteration=args.iteration, shuffle=False)
if args.render_all_imgs:
scene.my_test(args, gaussians, load_iteration=args.iteration, 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")
if args.video:
render_video(dataset.source_path, dataset.model_path, scene.loaded_iter, scene.getTestCameras(),
gaussians, pipeline, background, args.fps)
if args.render_all_imgs:
render_set(dataset.model_path, "eval", scene.loaded_iter, scene.getEvalCameras(), gaussians, pipeline, background, args)
if not args.skip_train:
render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background, args)
if not args.skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background, args)
if __name__ == "__main__":
torch.manual_seed(0)
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser)
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("--render_all_imgs", action="store_true")
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--video", action="store_true")
parser.add_argument("--fps", default=30, type=int)
parser.add_argument("--render_depth", action="store_true", default=False)
parser.add_argument("--rand_ply", action="store_true")
args = parser.parse_args()
print("Rendering " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), pipeline.extract(args), args)