-
Notifications
You must be signed in to change notification settings - Fork 350
/
feature_d2net.py
199 lines (164 loc) · 7.62 KB
/
feature_d2net.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
"""
* This file is part of PYSLAM
* Adapted from https://github.com/mihaidusmanu/d2-net/blob/master/extract_features.py, see the license therein.
*
* Copyright (C) 2016-present Luigi Freda <luigi dot freda at gmail dot com>
*
* PYSLAM 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.
*
* PYSLAM 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 PYSLAM. If not, see <http://www.gnu.org/licenses/>.
"""
# adapted from https://github.com/mihaidusmanu/d2-net/blob/master/extract_features.py
import config
config.cfg.set_lib('d2net')
import os
import argparse
import cv2
import numpy as np
import imageio
from threading import RLock
import torch
from tqdm import tqdm
import scipy
import scipy.io
import scipy.misc
from utils_sys import Printer
from lib.model_test import D2Net
from lib.utils import preprocess_image
from lib.pyramid import process_multiscale
from utils_sys import Printer, is_opencv_version_greater_equal
kVerbose = True
# convert matrix of pts into list of keypoints
def convert_pts_to_keypoints(pts, scores, size=1):
assert(len(pts)==len(scores))
kps = []
if pts is not None:
# convert matrix [Nx2] of pts into list of keypoints
if is_opencv_version_greater_equal(4,5,3):
kps = [ cv2.KeyPoint(p[0], p[1], size=size, response=scores[i]) for i,p in enumerate(pts) ]
else:
kps = [ cv2.KeyPoint(p[0], p[1], _size=size, _response=scores[i]) for i,p in enumerate(pts) ]
return kps
# interface for pySLAM
# from https://github.com/mihaidusmanu/d2-net
# N.B.: The singlescale features require less than 6GB of VRAM for 1200x1600 images.
# The multiscale flag can be used to extract multiscale features - for this, we recommend at least 12GB of VRAM.
class D2NetFeature2D:
def __init__(self,
use_relu=True, # remove ReLU after the dense feature extraction module
multiscale=False, # extract multiscale features (read the note above)
max_edge=1600, # maximum image size at network input
max_sum_edges=2800, # maximum sum of image sizes at network input
preprocessing='torch', # image preprocessing (caffe or torch)
do_cuda=True):
print('Using D2NetFeature2D')
self.lock = RLock()
self.model_base_path = config.cfg.root_folder + '/thirdparty/d2net/'
self.models_path = self.model_base_path + 'models/d2_ots.pth' # best performances obtained with 'd2_ots.pth'
self.use_relu = use_relu
self.multiscale = multiscale
self.max_edge = max_edge
self.max_sum_edges = max_sum_edges
self.preprocessing = preprocessing
self.pts = []
self.kps = []
self.des = []
self.frame = None
self.keypoint_size = 20 # just a representative size for visualization and in order to convert extracted points to cv2.KeyPoint
self.do_cuda = do_cuda & torch.cuda.is_available()
print('cuda:',self.do_cuda)
self.device = torch.device("cuda" if self.do_cuda else "cpu")
torch.set_grad_enabled(False)
print('==> Loading pre-trained network.')
# Creating CNN model
self.model = D2Net(
model_file=self.models_path,
use_relu=use_relu,
use_cuda=do_cuda)
if self.do_cuda:
print('Extracting on GPU')
else:
print('Extracting on CPU')
print('==> Successfully loaded pre-trained network.')
def compute_kps_des(self, image):
with self.lock:
print('D2Net image shape:',image.shape)
if len(image.shape) == 2:
image = image[:, :, np.newaxis]
image = np.repeat(image, 3, -1)
# TODO: switch to PIL.Image due to deprecation of scipy.misc.imresize.
resized_image = image
if max(resized_image.shape) > self.max_edge:
resized_image = scipy.misc.imresize(
resized_image,
self.max_edge / max(resized_image.shape)
).astype('float')
if sum(resized_image.shape[: 2]) > self.max_sum_edges:
resized_image = scipy.misc.imresize(
resized_image,
self.max_sum_edges / sum(resized_image.shape[: 2])
).astype('float')
fact_i = image.shape[0] / resized_image.shape[0]
fact_j = image.shape[1] / resized_image.shape[1]
print('scale factors: {}, {}'.format(fact_i,fact_j))
input_image = preprocess_image(
resized_image,
preprocessing=self.preprocessing
)
with torch.no_grad():
if self.multiscale:
self.pts, scores, descriptors = process_multiscale(
torch.tensor(
input_image[np.newaxis, :, :, :].astype(np.float32),
device=self.device
),
self.model
)
else:
self.pts, scores, descriptors = process_multiscale(
torch.tensor(
input_image[np.newaxis, :, :, :].astype(np.float32),
device=self.device
),
self.model,
scales=[1]
)
# Input image coordinates
self.pts[:, 0] *= fact_i
self.pts[:, 1] *= fact_j
# i, j -> u, v
self.pts = self.pts[:, [1, 0, 2]]
#print('pts.shape: ', self.pts.shape)
#print('pts:', self.pts)
self.kps = convert_pts_to_keypoints(self.pts, scores, self.keypoint_size)
self.des = descriptors
return self.kps, self.des
def detectAndCompute(self, frame, mask=None): #mask is a fake input
with self.lock:
self.frame = frame
self.kps, self.des = self.compute_kps_des(frame)
if kVerbose:
print('detector: D2NET, descriptor: D2NET, #features: ', len(self.kps), ', frame res: ', frame.shape[0:2])
return self.kps, self.des
# return keypoints if available otherwise call detectAndCompute()
def detect(self, frame, mask=None): # mask is a fake input
with self.lock:
if self.frame is not frame:
self.detectAndCompute(frame)
return self.kps
# return descriptors if available otherwise call detectAndCompute()
def compute(self, frame, kps=None, mask=None): # kps is a fake input, mask is a fake input
with self.lock:
if self.frame is not frame:
Printer.orange('WARNING: D2NET is recomputing both kps and des on last input frame', frame.shape)
self.detectAndCompute(frame)
return self.kps, self.des