-
Notifications
You must be signed in to change notification settings - Fork 12
/
Copy pathcalculate_statistics.py
138 lines (126 loc) · 5.01 KB
/
calculate_statistics.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
import argparse
import sys
import os
import argparse
import csv
import itertools
import cv2
import numpy
import torch
def parse_arguments(args):
desc = (
"3D60 dataset statistics calculation."
)
parser = argparse.ArgumentParser(description=desc)
# paths
parser.add_argument("--suncg_path", type=str,\
default=argparse.SUPPRESS,\
help="Path to the rendered data of SunCG")
parser.add_argument("--s2d3d_path", type=str,\
default=argparse.SUPPRESS,\
help="Path to the rendered data of Stanford2D3D")
parser.add_argument("--m3d_path", type=str,\
default=argparse.SUPPRESS,\
help="Path to the rendered data of Matterport3D")
parser.add_argument("--stats_path", type=str,\
default=".\\splits\\", \
help="Output path where the calculate dataset statistics files will be saved at.")
parser.add_argument("--max_depth", type=float,\
default=10, help="Max valid depth value for the statistics calculations")
return parser.parse_known_args(args)
def load_depth(filename, data_type=torch.float32):
dtmp = numpy.array(cv2.imread(filename, cv2.IMREAD_ANYDEPTH))
depth = torch.from_numpy(dtmp).type(data_type)
return depth.reshape(1, 1, depth.shape[0], depth.shape[1])
def calc_stats(name, folder, max_depth_meters=10.0):
depth_files = [f for f in os.listdir(folder) if ".exr" in f and "_depth_" in f]
total = torch.zeros(int(max_depth_meters * 2))
perc = torch.zeros(int(max_depth_meters * 2))
less_than_half_meter = 0.0
over_five_meters = 0.0
count = 0
for depth_file in depth_files:
filename = os.path.join(folder, depth_file)
depth = load_depth(filename)
b, c, h, w = depth.size()
depth = depth.reshape(h * w)
hist = torch.histc(depth, bins=int(2 * max_depth_meters), \
min=0, max=max_depth_meters)
total += hist
invalid = torch.sum(torch.isnan(depth)) + torch.sum(torch.isinf(depth)) \
+ torch.sum(depth > max_depth_meters)
valid = depth.size()[0] - invalid
if valid > 0:
perc += hist / valid
less_than_half_meter += torch.sum(depth < 0.5).float() / float(valid)
over_five_meters += torch.sum(depth > 5.0).float() / float(valid)
count += 1
return {
"name": name,
"total": total,
"perc": perc / count * 100,
'less0.5': less_than_half_meter / count * 100,
'over5': over_five_meters / count * 100
}
def calc_m3d_stats(m3d_path, max_depth_meters=10.0):
print("Calculating M3D stats...")
return calc_stats("M3D", m3d_path)
def calc_s2d3d_stats(s2d3d_path, max_depth_meters=10.0):
print("Calculating S2D3D stats...")
stats = []
count = 0
total = torch.zeros(int(2 * max_depth_meters))
perc = torch.zeros(int(2 * max_depth_meters))
less_than_half_meter = 0
over_five_meters = 0
for area in os.listdir(s2d3d_path):
stats.append(calc_stats("S2D3D", os.path.join(s2d3d_path, area)))
for area_stats in stats:
total += area_stats['total']
perc += area_stats['perc']
less_than_half_meter += area_stats['less0.5']
over_five_meters += area_stats['over5']
count = len(stats)
return {
"name" : "S2D3D",
"total": total,
"perc": perc / count,
'less0.5': less_than_half_meter / count,
'over5': over_five_meters / count
}
def calc_suncg_stats(suncg_path, max_depth_meters=10.0):
print("Calculating SunCG stats...")
return calc_stats("SCG", suncg_path)
def pairwise(iterable):
"s -> (s0,s1), (s1,s2), (s2, s3), ..."
a, b = itertools.tee(iterable)
next(b, None)
return zip(a, b)
def dump_stats(stats, args):
print("Dumping stats...")
for stat in stats:
with open(os.path.join(args.stats_path, '{}_stats.csv'.format(stat['name'])), mode='w') as csv_file:
stats_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
headers = ["{}-{}".format(p[0] / 2.0, p[1] / 2.0) for p in pairwise(range(0, 1 + 2 * int(args.max_depth)))]
headers += ["<0.5", ">5"]
stats_writer.writerow(headers)
stats_writer.writerow([str(float(v)) for v in stat['total']])
stats_writer.writerow([str(float(v) / 100.0) for v in stat['perc']] \
+ [float(stat['less0.5']) / 100.0, float(stat['over5']) / 100.0])
if __name__ == "__main__":
args, unknown = parse_arguments(sys.argv)
stats = []
''' Matterport3D '''
if 'm3d_path' in args:
m3d_stats = calc_m3d_stats(args.m3d_path, args.max_depth)
stats.append(m3d_stats)
''' Stanford2D3D '''
if 's2d3d_path' in args:
s2d3d_stats = calc_s2d3d_stats(args.s2d3d_path, args.max_depth)
stats.append(s2d3d_stats)
''' SunCG '''
if 'suncg_path' in args:
suncg_stats = calc_suncg_stats(args.suncg_path, args.max_depth)
stats.append(suncg_stats)
dump_stats(stats, args)
print("Done.")