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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 matplotlib.pyplot as plt
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, get_combined_args
from gaussian_renderer import GaussianModel
import cv2
import time
from tqdm import tqdm
from utils.graphics_utils import getWorld2View2
from utils.pose_utils import generate_ellipse_path, generate_spiral_path, generate_spiral_path_dtu
from utils.general_utils import vis_depth
import matplotlib.cm as cm
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")):
render_pkg = render(view, gaussians, pipeline, background)
gt = view.original_image[0:3, :, :]
torchvision.utils.save_image(render_pkg["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(render_pkg['depth'][0].detach().cpu().numpy())
cv2.imwrite(os.path.join(render_path, view.image_name + '_depth.png'), depth_map)
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)
print(f"point number is {gaussians.get_xyz.shape[0]}")
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 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__":
# 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("--skip_train", action="store_true")
parser.add_argument("--skip_test", action="store_true")
parser.add_argument("--quiet", action="store_true")
parser.add_argument("--video", action="store_true")
parser.add_argument("--fps", default=25, type=int)
parser.add_argument("--render_depth", action="store_true")
args = get_combined_args(parser)
print("Rendering " + args.model_path)
# Initialize system state (RNG)
safe_state(args.quiet)
render_sets(model.extract(args), pipeline.extract(args), args)