forked from isl-org/DPT
-
Notifications
You must be signed in to change notification settings - Fork 1
/
eval_with_pngs.py
228 lines (168 loc) · 8.25 KB
/
eval_with_pngs.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
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
# Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>
from __future__ import absolute_import, division, print_function
import os
import argparse
import fnmatch
import cv2
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
def convert_arg_line_to_args(arg_line):
for arg in arg_line.split():
if not arg.strip():
continue
yield arg
parser = argparse.ArgumentParser(description='BTS TensorFlow implementation.', fromfile_prefix_chars='@')
parser.convert_arg_line_to_args = convert_arg_line_to_args
parser.add_argument('--pred_path', type=str, help='path to the prediction results in png', required=True)
parser.add_argument('--gt_path', type=str, help='root path to the groundtruth data', required=False)
parser.add_argument('--dataset', type=str, help='dataset to test on, nyu or kitti', default='nyu')
parser.add_argument('--eigen_crop', help='if set, crops according to Eigen NIPS14', action='store_true')
parser.add_argument('--garg_crop', help='if set, crops according to Garg ECCV16', action='store_true')
parser.add_argument('--min_depth_eval', type=float, help='minimum depth for evaluation', default=1e-3)
parser.add_argument('--max_depth_eval', type=float, help='maximum depth for evaluation', default=80)
parser.add_argument('--do_kb_crop', help='if set, crop input images as kitti benchmark images', action='store_true')
args = parser.parse_args()
def compute_errors(gt, pred):
thresh = np.maximum((gt / pred), (pred / gt))
d1 = (thresh < 1.25).mean()
d2 = (thresh < 1.25 ** 2).mean()
d3 = (thresh < 1.25 ** 3).mean()
rmse = (gt - pred) ** 2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log(gt) - np.log(pred)) ** 2
rmse_log = np.sqrt(rmse_log.mean())
abs_rel = np.mean(np.abs(gt - pred) / gt)
sq_rel = np.mean(((gt - pred)**2) / gt)
err = np.log(pred) - np.log(gt)
silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100
err = np.abs(np.log10(pred) - np.log10(gt))
log10 = np.mean(err)
return silog, log10, abs_rel, sq_rel, rmse, rmse_log, d1, d2, d3
def test():
global gt_depths, missing_ids, pred_filenames
gt_depths = []
missing_ids = set()
pred_filenames = []
for root, dirnames, filenames in os.walk(args.pred_path):
for pred_filename in fnmatch.filter(filenames, '*.png'):
if 'cmap' in pred_filename or 'gt' in pred_filename:
continue
dirname = root.replace(args.pred_path, '')
pred_filenames.append(os.path.join(dirname, pred_filename))
num_test_samples = len(pred_filenames)
pred_depths = []
for i in range(num_test_samples):
pred_depth_path = os.path.join(args.pred_path, pred_filenames[i])
pred_depth = cv2.imread(pred_depth_path, -1)
if pred_depth is None:
print('Missing: %s ' % pred_depth_path)
missing_ids.add(i)
continue
if args.dataset == 'nyu':
pred_depth = pred_depth.astype(np.float32) / 1000.0
else:
pred_depth = pred_depth.astype(np.float32) / 256.0
pred_depths.append(pred_depth)
print('Raw png files reading done')
print('Evaluating {} files'.format(len(pred_depths)))
if args.dataset == 'kitti':
for t_id in range(num_test_samples):
file_dir = pred_filenames[t_id].split('.')[0]
filename = file_dir.split('_')[-1]
directory = file_dir.replace('_' + filename, '')
gt_depth_path = os.path.join(args.gt_path, directory, 'proj_depth/groundtruth/image_02', filename + '.png')
depth = cv2.imread(gt_depth_path, -1)
if depth is None:
print('Missing: %s ' % gt_depth_path)
missing_ids.add(t_id)
continue
depth = depth.astype(np.float32) / 256.0
gt_depths.append(depth)
elif args.dataset == 'nyu':
for t_id in range(num_test_samples):
file_dir = pred_filenames[t_id].split('.')[0]
filename = file_dir.split('_')[-1]
directory = file_dir.replace('_rgb_'+file_dir.split('_')[-1], '')
gt_depth_path = os.path.join(args.gt_path, directory, 'sync_depth_' + filename + '.png')
depth = cv2.imread(gt_depth_path, -1)
if depth is None:
print('Missing: %s ' % gt_depth_path)
missing_ids.add(t_id)
continue
depth = depth.astype(np.float32) / 1000.0
gt_depths.append(depth)
print('GT files reading done')
print('{} GT files missing'.format(len(missing_ids)))
print('Computing errors')
eval(pred_depths)
print('Done.')
def eval(pred_depths):
num_samples = len(pred_depths)
pred_depths_valid = []
i = 0
for t_id in range(num_samples):
if t_id in missing_ids:
continue
pred_depths_valid.append(pred_depths[t_id])
num_samples = num_samples - len(missing_ids)
silog = np.zeros(num_samples, np.float32)
log10 = np.zeros(num_samples, np.float32)
rms = np.zeros(num_samples, np.float32)
log_rms = np.zeros(num_samples, np.float32)
abs_rel = np.zeros(num_samples, np.float32)
sq_rel = np.zeros(num_samples, np.float32)
d1 = np.zeros(num_samples, np.float32)
d2 = np.zeros(num_samples, np.float32)
d3 = np.zeros(num_samples, np.float32)
for i in range(num_samples):
gt_depth = gt_depths[i]
pred_depth = pred_depths_valid[i]
pred_depth[pred_depth < args.min_depth_eval] = args.min_depth_eval
pred_depth[pred_depth > args.max_depth_eval] = args.max_depth_eval
pred_depth[np.isinf(pred_depth)] = args.max_depth_eval
gt_depth[np.isinf(gt_depth)] = 0
gt_depth[np.isnan(gt_depth)] = 0
valid_mask = np.logical_and(gt_depth > args.min_depth_eval, gt_depth < args.max_depth_eval)
if args.do_kb_crop:
height, width = gt_depth.shape
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
pred_depth_uncropped = np.zeros((height, width), dtype=np.float32)
pred_depth_uncropped[top_margin:top_margin + 352, left_margin:left_margin + 1216] = pred_depth
pred_depth = pred_depth_uncropped
if args.garg_crop or args.eigen_crop:
gt_height, gt_width = gt_depth.shape
eval_mask = np.zeros(valid_mask.shape)
if args.garg_crop:
eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height), int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1
elif args.eigen_crop:
if args.dataset == 'kitti':
eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height), int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1
else:
eval_mask[45:471, 41:601] = 1
valid_mask = np.logical_and(valid_mask, eval_mask)
silog[i], log10[i], abs_rel[i], sq_rel[i], rms[i], log_rms[i], d1[i], d2[i], d3[i] = compute_errors(gt_depth[valid_mask], pred_depth[valid_mask])
print("{:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}".format(
'd1', 'd2', 'd3', 'AbsRel', 'SqRel', 'RMSE', 'RMSElog', 'SILog', 'log10'))
print("{:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}".format(
d1.mean(), d2.mean(), d3.mean(),
abs_rel.mean(), sq_rel.mean(), rms.mean(), log_rms.mean(), silog.mean(), log10.mean()))
return silog, log10, abs_rel, sq_rel, rms, log_rms, d1, d2, d3
def main():
test()
if __name__ == '__main__':
main()