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render.py
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render.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 [email protected]
#
import imageio
import numpy as np
import torch
from scene import Scene
import os
import cv2
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,OptimizationParams, get_combined_args, ModelHiddenParams
from gaussian_renderer import GaussianModel
from time import time
to8b = lambda x : (255*np.clip(x.cpu().numpy(),0,1)).astype(np.uint8)
def render_set(model_path, name, iteration, views, gaussians, pipeline, background,multiview_video, fname='video_rgb.mp4'):
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)
render_images = []
gt_list = []
render_list = []
print(len(views))
# for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
# for idx in tqdm(range (100)):
fnum = 100
# fnum = 12
for idx in tqdm(range (fnum)):
view = views[idx]
if idx == 0:time1 = time()
#ww = torch.tensor([idx / 12]).unsqueeze(0)
ww = torch.tensor([idx / fnum]).unsqueeze(0)
# ww = torch.tensor([idx / 100]).unsqueeze(0)
if multiview_video:
rendering = render(view['cur_cam'], gaussians, pipeline, background, time=ww, stage='fine')["render"]
else:
rendering = render(view['pose0_cam'], gaussians, pipeline, background, time=ww, stage='fine')["render"]
render_images.append(to8b(rendering).transpose(1,2,0))
render_list.append(rendering)
time2=time()
print("FPS:",(len(views)-1)/(time2-time1))
print('Len', len(render_images))
imageio.mimwrite(os.path.join(model_path, name, "ours_{}".format(iteration), fname), render_images, fps=8, quality=8)
def render_set_timefix(model_path, name, iteration, views, gaussians, pipeline, background,multiview_video, fname='video_rgb.mp4',time_fix=-1):
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)
render_images = []
gt_list = []
render_list = []
print(len(views))
# for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
for idx in tqdm(range (12)):
#for idx in tqdm(range (100)):
view = views[idx]
if idx == 0:time1 = time()
# ww = torch.tensor([idx / 16]).unsqueeze(0)
ww = torch.tensor([idx / 100]).unsqueeze(0)
if time_fix!=-1:
ww=torch.tensor([time_fix/16]).unsqueeze(0)
if multiview_video:
rendering = render(view['cur_cam'], gaussians, pipeline, background, time=ww, stage='fine')["render"]
render_images.append(to8b(rendering).transpose(1,2,0))
render_list.append(rendering)
time2=time()
print("FPS:",(len(views)-1)/(time2-time1))
print('Len', len(render_images))
imageio.mimwrite(os.path.join(model_path, name, "ours_{}".format(iteration), fname), render_images, fps=7, quality=8)
def render_sets(dataset : ModelParams, hyperparam, opt,iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, skip_video: bool,multiview_video: bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree, hyperparam)
scene = Scene(dataset, gaussians, load_iteration=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 not skip_train:
render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background,multiview_video)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background,multiview_video)
if not skip_video:
#origin
render_set(dataset.model_path,"video",scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,multiview_video=True, fname='multiview.mp4')
render_set(dataset.model_path,"video",scene.loaded_iter,scene.getVideoCameras(),gaussians,pipeline,background,multiview_video=False, fname='pose0.mp4')
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser)
op = OptimizationParams(parser)
pipeline = PipelineParams(parser)
hyperparam = ModelHiddenParams(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("--skip_video", action="store_true")
parser.add_argument('--multiview_video',default=False,action="store_true")
parser.add_argument("--configs", type=str)
args = get_combined_args(parser)
print("Rendering " , args.model_path)
if args.configs:
import mmcv
from utils.params_utils import merge_hparams
config = mmcv.Config.fromfile(args.configs)
args = merge_hparams(args, config)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), hyperparam.extract(args), op.extract(args),args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, args.skip_video,args.multiview_video)