-
Notifications
You must be signed in to change notification settings - Fork 5
/
render.py
121 lines (108 loc) · 6.11 KB
/
render.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
#
# 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 torch
from scene import Scene
import os
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, get_combined_args
from gaussian_renderer import GaussianModel
###
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
###
def render_set(model_path, name, iteration, views, p_views_1, p_views_2, p_views_3, 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")
pr_path_1 = os.path.join(model_path, name, "ours_{}".format(iteration), "perturbation_render_1")
pr_path_2 = os.path.join(model_path, name, "ours_{}".format(iteration), "perturbation_render_2")
pr_path_3 = os.path.join(model_path, name, "ours_{}".format(iteration), "perturbation_render_3")
p_path_1 = os.path.join(model_path, name, "ours_{}".format(iteration), "perturbation_depth_1")
p_path_2 = os.path.join(model_path, name, "ours_{}".format(iteration), "perturbation_depth_2")
p_path_3 = os.path.join(model_path, name, "ours_{}".format(iteration), "perturbation_depth_3")
makedirs(render_path, exist_ok=True)
makedirs(gts_path, exist_ok=True)
makedirs(depth_path, exist_ok=True)
makedirs(pr_path_1, exist_ok=True)
makedirs(pr_path_2, exist_ok=True)
makedirs(pr_path_3, exist_ok=True)
makedirs(p_path_1, exist_ok=True)
makedirs(p_path_2, exist_ok=True)
makedirs(p_path_3, exist_ok=True)
for idx, view in enumerate(tqdm(views, desc="Rendering progress")):
render_pkg = render(view, gaussians, pipeline, background)
rendering = render_pkg["render"]
depth = render_pkg["depth"]
gt = view.original_image[0:3, :, :]
##########
scale_nor = depth.max().item()
depth_nor = depth / scale_nor
depth_tensor_squeezed = depth_nor.squeeze() # Remove the channel dimension
colormap = plt.get_cmap('jet')
depth_colored = colormap(depth_tensor_squeezed.cpu().numpy())
depth_colored_rgb = depth_colored[:, :, :3]
depth_image = Image.fromarray((depth_colored_rgb * 255).astype(np.uint8))
output_path = os.path.join(depth_path, '{0:05d}'.format(idx) + ".png")
depth_image.save(output_path)
##########
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"))
if name == 'train':
for idx, view in enumerate(tqdm(p_views_1, desc="Rendering progress")):
render_pkg = render(view, gaussians, pipeline, background)
p_render_1 = render_pkg["render"]
p_depth_1 = render_pkg["depth"]
torchvision.utils.save_image(p_depth_1, os.path.join(p_path_1, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(p_render_1, os.path.join(pr_path_1, '{0:05d}'.format(idx) + ".png"))
for idx, view in enumerate(tqdm(p_views_2, desc="Rendering progress")):
render_pkg = render(view, gaussians, pipeline, background)
p_render_2 = render_pkg["render"]
p_depth_2 = render_pkg["depth"]
torchvision.utils.save_image(p_depth_2, os.path.join(p_path_2, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(p_render_2, os.path.join(pr_path_2, '{0:05d}'.format(idx) + ".png"))
for idx, view in enumerate(tqdm(p_views_3, desc="Rendering progress")):
render_pkg = render(view, gaussians, pipeline, background)
p_render_3 = render_pkg["render"]
p_depth_3 = render_pkg["depth"]
torchvision.utils.save_image(p_depth_3, os.path.join(p_path_3, '{0:05d}'.format(idx) + ".png"))
torchvision.utils.save_image(p_render_3, os.path.join(pr_path_3, '{0:05d}'.format(idx) + ".png"))
def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False, api_key=None, self_refinement=None, num_prompt=None, max_rounds=None)
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(),
scene.getPerturbationCameras(stage=1), scene.getPerturbationCameras(stage=2), scene.getPerturbationCameras(stage=3), gaussians, pipeline, background)
if not skip_test:
render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(),
scene.getPerturbationCameras(stage=1), scene.getPerturbationCameras(stage=2), scene.getPerturbationCameras(stage=3), gaussians, pipeline, background)
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")
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)