-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathmetrics_dtu.py
126 lines (104 loc) · 5.41 KB
/
metrics_dtu.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
122
123
124
125
126
#
# 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
#
from pathlib import Path
import os
from PIL import Image
import torch
import torchvision
import torchvision.transforms.functional as tf
from utils.loss_utils import ssim
from skimage.metrics import structural_similarity
from lpipsPyTorch import lpips
import json
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser
def readImages(renders_dir, gt_dir, mask_dir):
renders = []
gts = []
masks = []
image_names = []
for fname in os.listdir(renders_dir):
render = Image.open(renders_dir / fname)
gt = Image.open(gt_dir / fname)
mask = Image.open(mask_dir / fname)
mask = mask.resize((mask.size[0] // 4, mask.size[1] // 4))
render = render.resize(mask.size)
gt = gt.resize(mask.size)
mask = tf.to_tensor(mask).unsqueeze(0)[:, :3, :, :].cuda()
mask_bin = (mask == 1.)
renders.append(tf.to_tensor(render).unsqueeze(0)[:, :3, :, :].cuda() * mask + (1-mask))
gts.append(tf.to_tensor(gt).unsqueeze(0)[:, :3, :, :].cuda() * mask + (1-mask))
masks.append(mask_bin)
image_names.append(fname)
return renders, gts, image_names, masks
def evaluate(model_paths, mask_paths):
full_dict = {}
per_view_dict = {}
full_dict_polytopeonly = {}
per_view_dict_polytopeonly = {}
print("")
for midx in range(len(model_paths)):
scene_dir = model_paths[midx]
mask_dir = Path(mask_paths[midx])
print("Scene:", scene_dir)
full_dict[scene_dir] = {}
per_view_dict[scene_dir] = {}
full_dict_polytopeonly[scene_dir] = {}
per_view_dict_polytopeonly[scene_dir] = {}
test_dir = Path(scene_dir) / "test"
for test_dir in [test_dir]:
dataset = test_dir.stem
for method in os.listdir(test_dir):
print("Method:", method, dataset)
full_dict[scene_dir][method] = {}
per_view_dict[scene_dir][method] = {}
full_dict_polytopeonly[scene_dir][method] = {}
per_view_dict_polytopeonly[scene_dir][method] = {}
method_dir = test_dir / method
gt_dir = method_dir / "gt"
renders_dir = method_dir / "renders"
renders, gts, image_names, masks = readImages(renders_dir, gt_dir, mask_dir)
ssims = []
ssims_sk = []
psnrs = []
lpipss = []
for idx in tqdm(range(len(renders)), desc="Metric evaluation progress", ascii=True, dynamic_ncols=True):
ssims.append(ssim(renders[idx], gts[idx]))
ssims_sk.append(structural_similarity(renders[idx][0].permute(1,2,0).cpu().numpy(), gts[idx][0].permute(1,2,0).cpu().numpy(), multichannel=True, data_range=1.0))
psnrs.append(psnr(renders[idx][masks[idx]][None, ...], gts[idx][masks[idx]][None, ...]))
# Following previous works to keep the range of RGB in [0, 1]. (however, may be a mistake : https://github.com/richzhang/PerceptualSimilarity)
lpipss.append(lpips(renders[idx], gts[idx], net_type='vgg'))
print(" SSIM : {:>12.7f}".format(torch.tensor(ssims).mean(), ".5"))
print(" SSIM_sk : {:>12.7f}".format(torch.tensor(ssims_sk).mean(), ".5"))
print(" PSNR : {:>12.7f}".format(torch.tensor(psnrs).mean(), ".5"))
print(" LPIPS : {:>12.7f}".format(torch.tensor(lpipss).mean(), ".5"))
print("")
full_dict[scene_dir][method].update({"SSIM": torch.tensor(ssims).mean().item(),
"SSIM_sk": torch.tensor(ssims_sk).mean().item(),
"PSNR": torch.tensor(psnrs).mean().item(),
"LPIPS": torch.tensor(lpipss).mean().item()})
per_view_dict[scene_dir][method].update({"SSIM": {name: ssim for ssim, name in zip(torch.tensor(ssims).tolist(), image_names)},
"PSNR": {name: psnr for psnr, name in zip(torch.tensor(psnrs).tolist(), image_names)},
"LPIPS": {name: lp for lp, name in zip(torch.tensor(lpipss).tolist(), image_names)}})
with open(scene_dir + "/results_{}_mask.json".format(dataset), 'w') as fp:
json.dump(full_dict[scene_dir], fp, indent=True)
with open(scene_dir + "/per_view_{}_mask.json".format(dataset), 'w') as fp:
json.dump(per_view_dict[scene_dir], fp, indent=True)
if __name__ == "__main__":
device = torch.device("cuda:0")
torch.cuda.set_device(device)
# Set up command line argument parser
parser = ArgumentParser(description="Training script parameters")
parser.add_argument('--model_paths', '-m', required=True, nargs="+", type=str, default=[])
parser.add_argument('--mask_paths', required=True, nargs="+", type=str, default=[])
args = parser.parse_args()
evaluate(args.model_paths, args.mask_paths)