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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 plyfile import PlyData, PlyElement
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, hyperparam=None):
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 = []
deform_vertices = []
num_down_emb_c = hyperparam.min_embeddings
num_down_emb_f = hyperparam.min_embeddings
count = 0
total_time = 0
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
if type(view.original_image) == type(None):
if name == 'video':
view.set_image()
else:
view.load_image()
time1 = time()
rendering = render(view, gaussians, pipeline, background, iter=iteration, num_down_emb_c=num_down_emb_c, num_down_emb_f=num_down_emb_f)["render"]
time2 = time()
total_time += (time2 - time1)
render_images.append(to8b(rendering).transpose(1,2,0))
torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(count) + ".png"))
# render_list.append(rendering)
if name in ["train", "test"]:
gt = view.original_image[0:3, :, :]
torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(count) + ".png"))
# gt_list.append(gt)
count +=1
print("FPS:",(len(views)-1)/total_time)
# count = 0
# print("writing training images.")
# if len(gt_list) != 0:
# for image in tqdm(gt_list):
# torchvision.utils.save_image(image, os.path.join(gts_path, '{0:05d}'.format(count) + ".png"))
# count+=1
# count = 0
# print("writing rendering images.")
# if len(render_list) != 0:
# for image in tqdm(render_list):
# torchvision.utils.save_image(image, os.path.join(render_path, '{0:05d}'.format(count) + ".png"))
# count +=1
imageio.mimwrite(os.path.join(model_path, name, "ours_{}".format(iteration), 'video_rgb.mp4'), render_images, fps=30, quality=8)
def render_sets(dataset : ModelParams, hyperparam, opt, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, skip_video: bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree, hyperparam)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False, duration=hyperparam.total_num_frames, loader=dataset.loader, opt=opt)
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, hyperparam=hyperparam)
if not skip_test:
render_set(dataset.model_path, "test", scene.loaded_iter, scene.getTestCameras(), gaussians, pipeline, background, hyperparam=hyperparam)
if not skip_video:
render_set(dataset.model_path, "video", scene.loaded_iter, scene.getVideoCameras(), gaussians, pipeline, background, hyperparam=hyperparam)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
opt = 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("--configs", type=str)
# import sys
# args = parser.parse_args(sys.argv[1:])
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), opt.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, args.skip_video)
# CUDA_VISIBLE_DEVICES=2 python render.py --model_path output/dynerf/coffee_martini_wo_cam13 --skip_train --configs arguments/dynerf/coffee_martini_wo_cam13.py