-
Notifications
You must be signed in to change notification settings - Fork 18
/
demo_FFD.py
72 lines (57 loc) · 2.46 KB
/
demo_FFD.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
import os
import sys
import numpy as np
import argparse
import subprocess
import time
from util.util import readkp
if __name__ == '__main__':
curr_dir = os.getcwd()
parser = argparse.ArgumentParser(
description='Image pair matching and pose evaluation with SuperGlue',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
'--input_pairs', type=str, default='image',
help='Path to the images')
parser.add_argument(
'--max_keypoints', type=int, default=10000,
help='Maximum number of keypoints detected by FFD'
' (\'-1\' keeps all keypoints)')
parser.add_argument(
'--num_level', type=int, default=3,
help='Number of decomposition levels')
parser.add_argument(
'--contrast_threshold', type=float, default=0.05,
help='FFD\'s contrast threshold')
parser.add_argument(
'--curvature_ratio', type=float, default=10.,
help='FFD\'s curvature ratio')
parser.add_argument(
'--Time_cost', type=bool, default=False,
help='Report running time over 25 runs'
' (\'-1\' doesn\' report time')
opt = parser.parse_args()
print(opt)
image_formats = [".jpg", ".png", ".ppm", ".pgm"]
for image_name in os.listdir(opt.input_pairs):
ext = os.path.splitext(image_name)[1]
if ext.lower() in image_formats:
image_dir = os.path.join(curr_dir, opt.input_pairs, image_name)
store_dir = os.path.join(curr_dir, opt.input_pairs)
process = subprocess.Popen('./FFD '+ str(os.path.join(curr_dir, opt.input_pairs, image_name)) + ' ' \
+ str(store_dir) + ' ' \
+ str(opt.num_level) + ' ' \
+ str(opt.max_keypoints) + ' ' \
+ str(opt.contrast_threshold) + ' ' \
+ str(opt.curvature_ratio) + ' '\
+ str(1*opt.Time_cost),
shell=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
stdout, stderr = process.communicate()
if (opt.Time_cost):
print('Feature extraction details: ', stdout)
keypoints, num_kp = readkp(os.path.join(store_dir, 'FFD_'+image_name+'.txt'))
print("[+] \n %s: #detected keypoints->%d"%(image_name,num_kp) )
with np.printoptions(precision=3, suppress=True, threshold=5):
print("\tx,\ty,\tscale,\tresponse:\n", keypoints )