-
Notifications
You must be signed in to change notification settings - Fork 18
/
Copy pathevaluate_kitti.py
executable file
·172 lines (130 loc) · 7.82 KB
/
evaluate_kitti.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
import numpy as np
import scipy.misc as sm
import argparse
import matplotlib.pyplot as plt
from evaluation_utils import *
def load_depths(pred_depths_org, split, gt_path, test_fn):
if split == 'kitti':
num_samples = 200
gt_disparities = load_gt_disp_kitti(gt_path)
gt_depths, pred_depths, pred_disparities_resized = convert_disps_to_depths_kitti(gt_disparities, pred_depths_org)
if split == 'eigen':
num_samples = 698
test_files = read_text_lines(gt_path + 'test_files_eigen.txt')
gt_files, gt_calib, im_sizes, im_files, cams = read_file_data(test_files, gt_path)
num_test = len(im_files)
gt_depths = []
pred_depths = []
gt_disparities = [[] for i in range(num_samples)]
for t_id in range(num_samples):
camera_id = cams[t_id] # 2 is left, 3 is right
depth = generate_depth_map(gt_calib[t_id], gt_files[t_id], im_sizes[t_id], camera_id, False, True)
gt_depths.append(depth.astype(np.float32))
disp_pred = sm.imresize(pred_depths_org[t_id], (im_sizes[t_id][0], im_sizes[t_id][1]), interp="bilinear", mode='F')
pred_depths.append(disp_pred)
if split == "nyuv2":
gt_depths = load_gt_disp_nyuv2(gt_path, test_fn)
num_samples = len(gt_depths)
pred_depths = []
gt_disparities = [[] for i in range(num_samples)]
for t_id in range(num_samples):
pred_depth = sm.imresize(pred_depths_org[t_id], gt_depths[0].shape, mode="F")
pred_depths.append(pred_depth)
if split == "cs":
num_samples = 500
gt_depths = [[] for i in range(num_samples)]
gt_disparities = [[] for i in range(num_samples)]
pred_depths = pred_depths_org
if split == "make3d":
gt_disparities = load_gt_disp_make3d(gt_path, test_fn)
gt_depths, pred_depths, pred_disparities_resized = convert_disps_to_depths_kitti(gt_disparities, pred_depths_org)
return gt_depths, pred_depths, gt_disparities
def process_depth(gt_depth, pred_depth, gt_disp, split, min_depth, max_depth, garg_crop=True, eigen_crop=False):
if split == 'eigen':
mask = np.logical_and(gt_depth > min_depth, gt_depth < max_depth)
if garg_crop or eigen_crop:
gt_height, gt_width = gt_depth.shape
# crop used by Garg ECCV16
# if used on gt_size 370x1224 produces a crop of [-218, -3, 44, 1180]
if garg_crop:
crop = np.array([0.40810811 * gt_height, 0.99189189 * gt_height,
0.03594771 * gt_width, 0.96405229 * gt_width]).astype(np.int32)
# crop we found by trial and error to reproduce Eigen NIPS14 results
elif eigen_crop:
crop = np.array([0.3324324 * gt_height, 0.91351351 * gt_height,
0.0359477 * gt_width, 0.96405229 * gt_width]).astype(np.int32)
crop_mask = np.zeros(mask.shape)
crop_mask[crop[0]:crop[1],crop[2]:crop[3]] = 1
mask = np.logical_and(mask, crop_mask)
# Scale matching
scalor = np.median(gt_depth[mask])/np.median(pred_depth[mask])
pred_depth[mask] *= scalor
pred_depth[pred_depth < min_depth] = min_depth
pred_depth[pred_depth > max_depth] = max_depth
gt_depth[gt_depth < min_depth] = min_depth
gt_depth[gt_depth > max_depth] = max_depth
if split in ['kitti']:
mask = gt_disp > 0
## median normalize the pred_depth to gt_depth scale
scalor = np.median(gt_depth[mask])/np.median(pred_depth[mask])
pred_depth[mask] *= scalor
pred_depth[pred_depth < min_depth] = min_depth
pred_depth[pred_depth > max_depth] = max_depth
gt_depth[gt_depth < min_depth] = min_depth
gt_depth[gt_depth > max_depth] = max_depth
if split == "make3d":
mask = gt_disp > 30
scalor = np.median(gt_depth)/np.median(pred_depth)
pred_depth *= scalor
min_depth, max_depth = np.percentile(gt_depth,1), np.percentile(gt_depth, 90)
pred_depth[pred_depth < min_depth] = min_depth
pred_depth[pred_depth > max_depth] = max_depth
gt_depth[gt_depth < min_depth] = min_depth
gt_depth[gt_depth > max_depth] = max_depth
if split == "nyuv2":
mask = gt_depth > -1
scalor = np.median(gt_depth[mask]) / np.median(pred_depth[mask])
pred_depth *= scalor
pred_depth[pred_depth < min_depth] = min_depth
pred_depth[pred_depth > max_depth] = max_depth
gt_depth[gt_depth < min_depth] = min_depth
gt_depth[gt_depth > max_depth] = max_depth
return gt_depth, pred_depth, mask
def eval_depth(gt_depths, pred_depths, gt_disparities, split, min_depth=1e-3, max_depth=80):
num_samples = len(pred_depths)
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_all = np.zeros(num_samples, np.float32)
a1 = np.zeros(num_samples, np.float32)
a2 = np.zeros(num_samples, np.float32)
a3 = np.zeros(num_samples, np.float32)
for i in range(num_samples):
gt_depth = gt_depths[i]
pred_depth = pred_depths[i]
if split == "cs": continue
gt_depth, pred_depth, mask = process_depth(gt_depth, pred_depth, gt_disparities[i], split, min_depth, max_depth)
abs_rel[i], sq_rel[i], rms[i], log_rms[i], a1[i], a2[i], a3[i] = compute_errors(gt_depth[mask], pred_depth[mask])
if abs_rel[i] == 0: continue
if split == "cs": return
print("{:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}, {:>10}".format('abs_rel', 'sq_rel', 'rms', 'log_rms', 'a1', 'a2', 'a3'))
print("{:10.4f}, {:10.4f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}, {:10.3f}".format(abs_rel.mean(), sq_rel.mean(), rms.mean(), log_rms.mean(), a1.mean(), a2.mean(), a3.mean()))
return abs_rel.mean(), sq_rel.mean(), rms.mean(), log_rms.mean(), a1.mean(), a2.mean(), a3.mean()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Evaluation on the KITTI dataset')
# parser.add_argument('--vis', type=bool, help='whether to visualize the depth', default=False)
# parser.add_argument('--vis_path', type=str, help='path to store depth visualization', default='./depth/')
parser.add_argument('--split', type=str, help='data split, kitti or eigen', required=True)
parser.add_argument('--predicted_disp_path', type=str, help='path to estimated disparities', required=True)
parser.add_argument('--gt_path', type=str, help='path to ground truth disparities', required=True)
parser.add_argument('--min_depth', type=float, help='minimum depth for evaluation', default=1e-3)
parser.add_argument('--max_depth', type=float, help='maximum depth for evaluation', default=80)
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')
args = parser.parse_args()
pred_disparities = np.load(args.predicted_disp_path)
split, gt_path = args.split, args.gt_path
min_depth, max_depth = args.min_depth, args.max_depth
gt_depths, pred_depths, gt_disparities = load_depths(pred_disparities, split, gt_path, test_fn = "")
eval_depth(gt_depths, pred_depths, gt_disparities, split, min_depth, max_depth)