-
Notifications
You must be signed in to change notification settings - Fork 39
/
detect_sift_keypoints_and_extract_patches.py
250 lines (214 loc) · 8.96 KB
/
detect_sift_keypoints_and_extract_patches.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
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
import numpy as np
import os
import cv2
from tqdm import tqdm
import argparse
import json
from extract_patches.core import extract_patches
from utils import save_h5
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
def l_clahe(img):
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
lab = cv2.cvtColor(img, cv2.COLOR_RGB2Lab)
lab[:, :, 0] = clahe.apply(lab[:, :, 0])
return cv2.cvtColor(lab, cv2.COLOR_Lab2RGB)
def get_SIFT_keypoints(sift, img, lower_detection_th=False):
# convert to gray-scale and compute SIFT keypoints
keypoints = sift.detect(img, None)
response = np.array([kp.response for kp in keypoints])
respSort = np.argsort(response)[::-1]
pt = np.array([kp.pt for kp in keypoints])[respSort]
size = np.array([kp.size for kp in keypoints])[respSort]
angle = np.array([kp.angle for kp in keypoints])[respSort]
response = np.array([kp.response for kp in keypoints])[respSort]
return pt, size, angle, response
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
"--scenes_folder",
default=os.path.join('..', 'imw-2020'),
help="path to config file",
type=str)
parser.add_argument(
"--folder_outp",
default=os.path.join('..', 'benchmark-patches-8k'),
type=str)
parser.add_argument(
"--mrSize",
default=12.0,
type=float,
help=' patch size in image is mrSize * pt.size. Default mrSize is 12')
parser.add_argument(
"--patchSize",
default=32,
type=int,
help=' patch size in pixels. Default 32')
parser.add_argument(
"--lower_sift_threshold",
default='True',
type=str2bool,
help='Lower detection threshold (useful to extract 8k features)')
parser.add_argument(
"--clahe-mode",
default='None',
type=str,
help='can be None, detector, descriptor, both')
parser.add_argument(
"--subset",
default='both',
type=str,
help='Options: "val", "test", "both", "spc-fix"')
parser.add_argument(
"--force_upright",
default='off',
type=str,
help='Options: "off", "no-dups", "no-dups-more-points"')
parser.add_argument("--n_keypoints", default=8000, type=int)
args = parser.parse_args()
if args.subset not in ['val', 'test', 'both', 'spc-fix']:
raise ValueError('Unknown value for --subset')
if args.lower_sift_threshold:
print('Instantiating SIFT detector with a lower detection threshold')
sift = cv2.xfeatures2d.SIFT_create(
contrastThreshold=-10000, edgeThreshold=-10000)
else:
print('Instantiating SIFT detector with default values')
sift = cv2.xfeatures2d.SIFT_create()
if not os.path.isdir(args.folder_outp):
os.makedirs(args.folder_outp)
scenes = []
if args.subset == 'spc-fix':
scenes += ['st_pauls_cathedral']
else:
if args.subset in ['val', 'both']:
with open(os.path.join('data', 'val.json')) as f:
scenes += json.load(f)
if args.subset in ['test', 'both']:
with open(os.path.join('data', 'test.json')) as f:
scenes += json.load(f)
print('Processing the following scenes: {}'.format(scenes))
suffix = ""
if args.clahe_mode.lower() == 'detector':
suffix = "_clahe_det"
elif args.clahe_mode.lower() == 'descriptor':
suffix = "_clahe_desc"
elif args.clahe_mode.lower() == 'both':
suffix = "_clahe_det_desc"
elif args.clahe_mode.lower() == 'none':
pass
else:
raise ValueError(
"unknown CLAHE mode. Try detector, descriptor or both")
assert(args.mrSize > 0)
if abs(args.mrSize - 12.) > 0.1:
suffix += '_mrSize{:.1f}'.format(args.mrSize)
assert(args.patchSize > 0)
if args.patchSize != 32:
suffix += '_patchSize{}'.format(args.patchSize)
for scene in scenes:
print('Processing "{}"'.format(scene))
scene_patches, scene_kp, scene_loc, scene_scale, \
sec_ori, sec_resp = {}, {}, {}, {}, {}, {}
scene_path = os.path.join(args.scenes_folder,
scene, 'set_100/images/')
num_patches = []
img_list = [x for x in os.listdir(scene_path) if x.endswith('.jpg')]
for im_path in tqdm(img_list):
img_name = im_path.replace('.jpg', '')
im = cv2.cvtColor(
cv2.imread(os.path.join(scene_path, im_path)),
cv2.COLOR_BGR2RGB)
if args.clahe_mode.lower() in ['detector', 'both']:
img_gray = cv2.cvtColor(l_clahe(im), cv2.COLOR_RGB2GRAY)
else:
img_gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)
keypoints, scales, angles, responses = get_SIFT_keypoints(sift,
img_gray)
if args.force_upright == 'off':
# Nothing to do
kpts = [
cv2.KeyPoint(
x=point[0],
y=point[1],
_size=scales[i],
_angle=angles[i]) for i, point in enumerate(keypoints)
]
elif args.force_upright == 'no-dups':
# Set orientation to zero, remove duplicates later
# This is a subset of the previous set
kpts = [
cv2.KeyPoint(
x=keypoints[i][0],
y=keypoints[i][1],
_size=scales[i],
_angle=0) for i, point in enumerate(keypoints)
]
elif args.force_upright == 'no-dups-more-points':
# Copy without duplicates, set orientation to zero
# The cropped list may contain new points
kpts = [
cv2.KeyPoint(
x=keypoints[i][0],
y=keypoints[i][1],
_size=scales[i],
_angle=0) for i, point in enumerate(keypoints)
if point not in keypoints[:i]
]
else:
raise ValueError('Unknown --force_upright setting')
# apply CLAHE
im = cv2.cvtColor(
cv2.imread(os.path.join(scene_path, im_path)),
cv2.COLOR_BGR2RGB)
if args.clahe_mode.lower() in ['descriptor', 'both']:
im = l_clahe(im)
# Extract patches
patches = extract_patches(
kpts, im, args.patchSize, args.mrSize)
keypoints = np.array([(x.pt[0], x.pt[1]) for x in kpts ]).reshape(-1, 2)
scales = np.array([args.mrSize * x.size for x in kpts ]).reshape(-1, 1)
angles = np.array([x.angle for x in kpts ]).reshape(-1, 1)
responses = np.array([x.response for x in kpts ]).reshape(-1, 1)
# Crop
patches = np.array(patches)[:args.n_keypoints].astype(np.uint8)
keypoints = keypoints[:args.n_keypoints]
scales = scales[:args.n_keypoints]
angles = angles[:args.n_keypoints]
responses = responses[:args.n_keypoints]
# Remove duplicates after cropping
if args.force_upright == 'no-dups':
_, unique = np.unique(keypoints, axis=0, return_index=True)
patches = patches[unique]
keypoints = keypoints[unique]
scales = scales[unique]
angles = angles[unique]
responses = responses[unique]
# Patches are already uint8
num_patches.append(patches.shape[0])
scene_patches[img_name] = patches
scene_kp[img_name] = keypoints
scene_scale[img_name] = scales
sec_ori[img_name] = angles
sec_resp[img_name] = responses
print('Processed {} images: {} patches/image'.format(
len(num_patches), np.array(num_patches).mean()))
cur_path = os.path.join(args.folder_outp, scene)
# if args.force_upright == 'no-dups':
# cur_path += '_upright_v1'
# elif args.force_upright == 'no-dups-more-points':
# cur_path += '_upright_v2'
if not os.path.isdir(cur_path):
os.makedirs(cur_path)
save_h5(scene_patches,
os.path.join(cur_path, 'patches{}.h5'.format(suffix)))
save_h5(scene_kp, os.path.join(cur_path,
'keypoints{}.h5'.format(suffix)))
save_h5(scene_scale, os.path.join(cur_path,
'scales{}.h5'.format(suffix)))
save_h5(sec_ori, os.path.join(cur_path, 'angles{}.h5'.format(suffix)))
save_h5(sec_resp, os.path.join(cur_path, 'scores{}.h5'.format(suffix)))
print('Done!')